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Archive for the ‘Artificial Intelligence in Health Care – Tools & Innovations’ Category

Patients with type 2 diabetes may soon receive artificial pancreas and a smartphone app assistance

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

In a brief, randomized crossover investigation, adults with type 2 diabetes and end-stage renal disease who needed dialysis benefited from an artificial pancreas. Tests conducted by the University of Cambridge and Inselspital, University Hospital of Bern, Switzerland, reveal that now the device can help patients safely and effectively monitor their blood sugar levels and reduce the risk of low blood sugar levels.

Diabetes is the most prevalent cause of kidney failure, accounting for just under one-third (30%) of all cases. As the number of people living with type 2 diabetes rises, so does the number of people who require dialysis or a kidney transplant. Kidney failure raises the risk of hypoglycemia and hyperglycemia, or unusually low or high blood sugar levels, which can lead to problems ranging from dizziness to falls and even coma.

Diabetes management in adults with renal failure is difficult for both the patients and the healthcare practitioners. Many components of their therapy, including blood sugar level targets and medications, are poorly understood. Because most oral diabetes drugs are not indicated for these patients, insulin injections are the most often utilized diabetic therapy-yet establishing optimum insulin dose regimes is difficult.

A team from the University of Cambridge and Cambridge University Hospitals NHS Foundation Trust earlier developed an artificial pancreas with the goal of replacing insulin injections for type 1 diabetic patients. The team, collaborating with experts at Bern University Hospital and the University of Bern in Switzerland, demonstrated that the device may be used to help patients with type 2 diabetes and renal failure in a study published on 4 August 2021 in Nature Medicine.

The study’s lead author, Dr Charlotte Boughton of the Wellcome Trust-MRC Institute of Metabolic Science at the University of Cambridge, stated:

Patients living with type 2 diabetes and kidney failure are a particularly vulnerable group and managing their condition-trying to prevent potentially dangerous highs or lows of blood sugar levels – can be a challenge. There’s a real unmet need for new approaches to help them manage their condition safely and effectively.

The Device

The artificial pancreas is a compact, portable medical device that uses digital technology to automate insulin delivery to perform the role of a healthy pancreas in managing blood glucose levels. The system is worn on the outside of the body and consists of three functional components:

  • a glucose sensor
  • a computer algorithm for calculating the insulin dose
  • an insulin pump

The artificial pancreas directed insulin delivery on a Dana Diabecare RS pump using a Dexcom G6 transmitter linked to the Cambridge adaptive model predictive control algorithm, automatically administering faster-acting insulin aspart (Fiasp). The CamDiab CamAPS HX closed-loop app on an unlocked Android phone was used to manage the closed loop system, with a goal glucose of 126 mg/dL. The program calculated an insulin infusion rate based on the data from the G6 sensor every 8 to 12 minutes, which was then wirelessly routed to the insulin pump, with data automatically uploaded to the Diasend/Glooko data management platform.

The Case Study

Between October 2019 and November 2020, the team recruited 26 dialysis patients. Thirteen patients were randomly assigned to get the artificial pancreas first, followed by 13 patients who received normal insulin therapy initially. The researchers compared how long patients spent as outpatients in the target blood sugar range (5.6 to 10.0mmol/L) throughout a 20-day period.

Patients who used the artificial pancreas spent 53 % in the target range on average, compared to 38% who utilized the control treatment. When compared to the control therapy, this translated to approximately 3.5 more hours per day spent in the target range.

The artificial pancreas resulted in reduced mean blood sugar levels (10.1 vs. 11.6 mmol/L). The artificial pancreas cut the amount of time patients spent with potentially dangerously low blood sugar levels, known as ‘hypos.’

The artificial pancreas’ efficacy improved significantly over the research period as the algorithm evolved, and the time spent in the target blood sugar range climbed from 36% on day one to over 60% by the twentieth day. This conclusion emphasizes the need of employing an adaptive algorithm that can adapt to an individual’s fluctuating insulin requirements over time.

When asked if they would recommend the artificial pancreas to others, everyone who responded indicated they would. Nine out of ten (92%) said they spent less time controlling their diabetes with the artificial pancreas than they did during the control period, and a comparable amount (87%) said they were less concerned about their blood sugar levels when using it.

Other advantages of the artificial pancreas mentioned by study participants included fewer finger-prick blood sugar tests, less time spent managing their diabetes, resulting in more personal time and independence, and increased peace of mind and reassurance. One disadvantage was the pain of wearing the insulin pump and carrying the smartphone.

Professor Roman Hovorka, a senior author from the Wellcome Trust-MRC Institute of Metabolic Science, mentioned:

Not only did the artificial pancreas increase the amount of time patients spent within the target range for the blood sugar levels, but it also gave the users peace of mind. They were able to spend less time having to focus on managing their condition and worrying about the blood sugar levels, and more time getting on with their lives.

The team is currently testing the artificial pancreas in outpatient settings in persons with type 2 diabetes who do not require dialysis, as well as in difficult medical scenarios such as perioperative care.

The artificial pancreas has the potential to become a fundamental part of integrated personalized care for people with complicated medical needs,” said Dr Lia Bally, who co-led the study in Bern.

The authors stated that the study’s shortcomings included a small sample size due to “Brexit-related study funding concerns and the COVID-19 epidemic.”

Boughton concluded:

We would like other clinicians to be aware that automated insulin delivery systems may be a safe and effective treatment option for people with type 2 diabetes and kidney failure in the future.

Main Source:

Boughton, C. K., Tripyla, A., Hartnell, S., Daly, A., Herzig, D., Wilinska, M. E., & Hovorka, R. (2021). Fully automated closed-loop glucose control compared with standard insulin therapy in adults with type 2 diabetes requiring dialysis: an open-label, randomized crossover trial. Nature Medicine, 1-6.

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes

Reporter: Amandeep Kaur, B.Sc., M.Sc.

https://pharmaceuticalintelligence.com/2021/05/29/developing-machine-learning-models-for-prediction-of-onset-of-type-2-diabetes/

Artificial pancreas effectively controls type 1 diabetes in children age 6 and up

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2020/10/08/artificial-pancreas-effectively-controls-type-1-diabetes-in-children-age-6-and-up/

Google, Verily’s Uses AI to Screen for Diabetic Retinopathy

Reporter : Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/04/08/49900/

World’s first artificial pancreas

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/05/16/worlds-first-artificial-pancreas/

Artificial Pancreas – Medtronic Receives FDA Approval for World’s First Hybrid Closed Loop System for People with Type 1 Diabetes

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/09/30/artificial-pancreas-medtronic-receives-fda-approval-for-worlds-first-hybrid-closed-loop-system-for-people-with-type-1-diabetes/

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Science Policy Forum: Should we trust healthcare explanations from AI predictive systems?

Some in industry voice their concerns

Curator: Stephen J. Williams, PhD

Post on AI healthcare and explainable AI

   In a Policy Forum article in ScienceBeware explanations from AI in health care”, Boris Babic, Sara Gerke, Theodoros Evgeniou, and Glenn Cohen discuss the caveats on relying on explainable versus interpretable artificial intelligence (AI) and Machine Learning (ML) algorithms to make complex health decisions.  The FDA has already approved some AI/ML algorithms for analysis of medical images for diagnostic purposes.  These have been discussed in prior posts on this site, as well as issues arising from multi-center trials.  The authors of this perspective article argue that choice of type of algorithm (explainable versus interpretable) algorithms may have far reaching consequences in health care.

Summary

Artificial intelligence and machine learning (AI/ML) algorithms are increasingly developed in health care for diagnosis and treatment of a variety of medical conditions (1). However, despite the technical prowess of such systems, their adoption has been challenging, and whether and how much they will actually improve health care remains to be seen. A central reason for this is that the effectiveness of AI/ML-based medical devices depends largely on the behavioral characteristics of its users, who, for example, are often vulnerable to well-documented biases or algorithmic aversion (2). Many stakeholders increasingly identify the so-called black-box nature of predictive algorithms as the core source of users’ skepticism, lack of trust, and slow uptake (3, 4). As a result, lawmakers have been moving in the direction of requiring the availability of explanations for black-box algorithmic decisions (5). Indeed, a near-consensus is emerging in favor of explainable AI/ML among academics, governments, and civil society groups. Many are drawn to this approach to harness the accuracy benefits of noninterpretable AI/ML such as deep learning or neural nets while also supporting transparency, trust, and adoption. We argue that this consensus, at least as applied to health care, both overstates the benefits and undercounts the drawbacks of requiring black-box algorithms to be explainable.

Source: https://science.sciencemag.org/content/373/6552/284?_ga=2.166262518.995809660.1627762475-1953442883.1627762475

Types of AI/ML Algorithms: Explainable and Interpretable algorithms

  1.  Interpretable AI: A typical AI/ML task requires constructing algorithms from vector inputs and generating an output related to an outcome (like diagnosing a cardiac event from an image).  Generally the algorithm has to be trained on past data with known parameters.  When an algorithm is called interpretable, this means that the algorithm uses a transparent or “white box” function which is easily understandable. Such example might be a linear function to determine relationships where parameters are simple and not complex.  Although they may not be as accurate as the more complex explainable AI/ML algorithms, they are open, transparent, and easily understood by the operators.
  2. Explainable AI/ML:  This type of algorithm depends upon multiple complex parameters and takes a first round of predictions from a “black box” model then uses a second algorithm from an interpretable function to better approximate outputs of the first model.  The first algorithm is trained not with original data but based on predictions resembling multiple iterations of computing.  Therefore this method is more accurate or deemed more reliable in prediction however is very complex and is not easily understandable.  Many medical devices that use an AI/ML algorithm use this type.  An example is deep learning and neural networks.

The purpose of both these methodologies is to deal with problems of opacity, or that AI predictions based from a black box undermines trust in the AI.

For a deeper understanding of these two types of algorithms see here:

https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html

or https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability/

(a longer read but great explanation)

From the above blog post of Jonathan Johnson

  • How interpretability is different from explainability
  • Why a model might need to be interpretable and/or explainable
  • Who is working to solve the black box problem—and how

What is interpretability?

Does Chipotle make your stomach hurt? Does loud noise accelerate hearing loss? Are women less aggressive than men? If a machine learning model can create a definition around these relationships, it is interpretable.

All models must start with a hypothesis. Human curiosity propels a being to intuit that one thing relates to another. “Hmm…multiple black people shot by policemen…seemingly out of proportion to other races…something might be systemic?” Explore.

People create internal models to interpret their surroundings. In the field of machine learning, these models can be tested and verified as either accurate or inaccurate representations of the world.

Interpretability means that the cause and effect can be determined.

What is explainability?

ML models are often called black-box models because they allow a pre-set number of empty parameters, or nodes, to be assigned values by the machine learning algorithm. Specifically, the back-propagation step is responsible for updating the weights based on its error function.

To predict when a person might die—the fun gamble one might play when calculating a life insurance premium, and the strange bet a person makes against their own life when purchasing a life insurance package—a model will take in its inputs, and output a percent chance the given person has at living to age 80.

Below is an image of a neural network. The inputs are the yellow; the outputs are the orange. Like a rubric to an overall grade, explainability shows how significant each of the parameters, all the blue nodes, contribute to the final decision.

In this neural network, the hidden layers (the two columns of blue dots) would be the black box.

For example, we have these data inputs:

  • Age
  • BMI score
  • Number of years spent smoking
  • Career category

If this model had high explainability, we’d be able to say, for instance:

  • The career category is about 40% important
  • The number of years spent smoking weighs in at 35% important
  • The age is 15% important
  • The BMI score is 10% important

Explainability: important, not always necessary

Explainability becomes significant in the field of machine learning because, often, it is not apparent. Explainability is often unnecessary. A machine learning engineer can build a model without ever having considered the model’s explainability. It is an extra step in the building process—like wearing a seat belt while driving a car. It is unnecessary for the car to perform, but offers insurance when things crash.

The benefit a deep neural net offers to engineers is it creates a black box of parameters, like fake additional data points, that allow a model to base its decisions against. These fake data points go unknown to the engineer. The black box, or hidden layers, allow a model to make associations among the given data points to predict better results. For example, if we are deciding how long someone might have to live, and we use career data as an input, it is possible the model sorts the careers into high- and low-risk career options all on its own.

Perhaps we inspect a node and see it relates oil rig workers, underwater welders, and boat cooks to each other. It is possible the neural net makes connections between the lifespan of these individuals and puts a placeholder in the deep net to associate these. If we were to examine the individual nodes in the black box, we could note this clustering interprets water careers to be a high-risk job.

In the previous chart, each one of the lines connecting from the yellow dot to the blue dot can represent a signal, weighing the importance of that node in determining the overall score of the output.

  • If that signal is high, that node is significant to the model’s overall performance.
  • If that signal is low, the node is insignificant.

With this understanding, we can define explainability as:

Knowledge of what one node represents and how important it is to the model’s performance.

So how does choice of these two different algorithms make a difference with respect to health care and medical decision making?

The authors argue: 

“Regulators like the FDA should focus on those aspects of the AI/ML system that directly bear on its safety and effectiveness – in particular, how does it perform in the hands of its intended users?”

A suggestion for

  • Enhanced more involved clinical trials
  • Provide individuals added flexibility when interacting with a model, for example inputting their own test data
  • More interaction between user and model generators
  • Determining in which situations call for interpretable AI versus explainable (for instance predicting which patients will require dialysis after kidney damage)

Other articles on AI/ML in medicine and healthcare on this Open Access Journal include

Applying AI to Improve Interpretation of Medical Imaging

Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence #AI: Realizing Precision Medicine One Patient at a Time

LIVE Day Three – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 10, 2019

Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package

 

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Al is on the way to lead critical ED decisions on CT

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Artificial intelligence (AI) has infiltrated many organizational processes, raising concerns that robotic systems will eventually replace many humans in decision-making. The advent of AI as a tool for improving health care provides new prospects to improve patient and clinical team’s performance, reduce costs, and impact public health. Examples include, but are not limited to, automation; information synthesis for patients, “fRamily” (friends and family unpaid caregivers), and health care professionals; and suggestions and visualization of information for collaborative decision making.

In the emergency department (ED), patients with Crohn’s disease (CD) are routinely subjected to Abdomino-Pelvic Computed Tomography (APCT). It is necessary to diagnose clinically actionable findings (CAF) since they may require immediate intervention, which is typically surgical. Repeated APCTs, on the other hand, results in higher ionizing radiation exposure. The majority of APCT performance guidance is clinical and empiric. Emergency surgeons struggle to identify Crohn’s disease patients who actually require a CT scan to determine the source of acute abdominal distress.

Image Courtesy: Jim Coote via Pixabay https://www.aiin.healthcare/media/49446

Aid seems to be on the way. Researchers employed machine learning to accurately distinguish these sufferers from Crohn’s patients who appear with the same complaint but may safely avoid the recurrent exposure to contrast materials and ionizing radiation that CT would otherwise wreak on them.

The study entitled “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department” was published on July 9 in Digestive and Liver Disease by gastroenterologists and radiologists at Tel Aviv University in Israel.

Retrospectively, Jacob Ollech and his fellow researcher have analyzed 101 emergency treatments of patients with Crohn’s who underwent abdominopelvic CT.

They were looking for examples where a scan revealed clinically actionable results. These were classified as intestinal blockage, perforation, intra-abdominal abscess, or complex fistula by the researchers.

On CT, 44 (43.5 %) of the 101 cases reviewed had such findings.

Ollech and colleagues utilized a machine-learning technique to design a decision-support tool that required only four basic clinical factors to test an AI approach for making the call.

The approach was successful in categorizing patients into low- and high-risk groupings. The researchers were able to risk-stratify patients based on the likelihood of clinically actionable findings on abdominopelvic CT as a result of their success.

Ollech and co-authors admit that their limited sample size, retrospective strategy, and lack of external validation are shortcomings.

Moreover, several patients fell into an intermediate risk category, implying that a standard workup would have been required to guide CT decision-making in a real-world situation anyhow.

Consequently, they generate the following conclusion:

We believe this study shows that a machine learning-based tool is a sound approach for better-selecting patients with Crohn’s disease admitted to the ED with acute gastrointestinal complaints about abdominopelvic CT: reducing the number of CTs performed while ensuring that patients with high risk for clinically actionable findings undergo abdominopelvic CT appropriately.

Main Source:

Konikoff, Tom, Idan Goren, Marianna Yalon, Shlomit Tamir, Irit Avni-Biron, Henit Yanai, Iris Dotan, and Jacob E. Ollech. “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department.” Digestive and Liver Disease (2021). https://www.sciencedirect.com/science/article/abs/pii/S1590865821003340

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Al App for People with Digestive Disorders

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/24/ai-app-for-people-with-digestive-disorders/

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

Al System Used to Detect Lung Cancer

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/07/06/yet-another-success-story-machine-learning-to-predict-immunotherapy-response/

Systemic Inflammatory Diseases as Crohn’s disease, Rheumatoid Arthritis and Longer Psoriasis Duration May Mean Higher CVD Risk

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/10/09/systemic-inflammatory-diseases-as-crohns-disease-rheumatoid-arthritis-and-longer-psoriasis-duration-may-mean-higher-cvd-risk/

Autoimmune Inflammatory Bowel Diseases: Crohn’s Disease & Ulcerative Colitis: Potential Roles for Modulation of Interleukins 17 and 23 Signaling for Therapeutics

Curators: Larry H Bernstein, MD FCAP and Aviva Lev-Ari, PhD, RN https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/

Inflammatory Disorders: Inflammatory Bowel Diseases (IBD) – Crohn’s and Ulcerative Colitis (UC) and Others

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/gama-delta-epsilon-gde-is-a-global-holding-company-absorbing-lpbi/subsidiary-5-joint-ventures-for-ip-development-jvip/drug-discovery-with-3d-bioprinting/ibd-inflammatory-bowl-diseases-crohns-and-ulcerative-colitis/

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This AI Just Evolved From Companion Robot To Home-Based Physician Helper

Reporter: Ethan Coomber, Research Assistant III, Data Science and Podcast Library Development 

Article Author: Gil Press Senior Contributor Enterprise & Cloud @Forbes 

Twitter: @GilPress I write about technology, entrepreneurs and innovation.

Intuition Robotics announced today that it is expanding its mission of improving the lives of older adults to include enhancing their interactions with their physicians. The Israeli startup has developed the AI-based, award-winning proactive social robot ElliQ which has spent over 30,000 days in older adults’ homes over the past two years. Now ElliQ will help increase patient engagement while offering primary care providers continuous actionable data and insights for early detection and intervention.

The very big challenge Intuition Robotics set up to solve was to “understand how to create a relationship between a human and a machine,” says co-founder and CEO Dor Skuler. Unlike a number of unsuccessful high-profile social robots (e.g., Pepper) that tried to perform multiple functions in multiple settings, ElliQ has focused exclusively on older adults living alone. Understanding empathy and how to grow a trusting relationship were the key objectives of Intuition Robotics’ research project, as well as how to continuously learn the specific (and changing) behavioral characteristics, habits, and preferences of the older adults participating in the experiment.

The results are impressive: 90% of users engage with ElliQ every day, without deterioration in engagement over time. When ElliQ proactively initiates deep conversational interactions with its users, there’s 70% response rate. Most important, the participants share something personal with ElliQ almost every day. “She has picked up my attitude… she’s figured me out,” says Deanna Dezern, an ElliQ user who describes her robot companion as “my sister from another mother.”

The very big challenge Intuition Robotics set up to solve was to “understand how to create a relationship between a human and a machine,” says co-founder and CEO Dor Skuler. Unlike a number of unsuccessful high-profile social robots (e.g., Pepper) that tried to perform multiple functions in multiple settings, ElliQ has focused exclusively on older adults living alone. Understanding empathy and how to grow a trusting relationship were the key objectives of Intuition Robotics’ research project, as well as how to continuously learn the specific (and changing) behavioral characteristics, habits, and preferences of the older adults participating in the experiment.

The results are impressive: 90% of users engage with ElliQ every day, without deterioration in engagement over time. When ElliQ proactively initiates deep conversational interactions with its users, there’s 70% response rate. Most important, the participants share something personal with ElliQ almost every day. “She has picked up my attitude… she’s figured me out,” says Deanna Dezern, an ElliQ user who describes her robot companion as “my sister from another mother.”

Higher patient engagement leads to lower costs of delivering care and the quality of the physician-patient relationship is positively associated with improved functional health, studies have found. Typically, however, primary care physicians see their patients anywhere from once a month to once a year, even though about 85% of seniors in the U.S. have at least one chronic health condition. ElliQ, with the consent of its users, can provide data on the status of patients in between office visits and facilitate timely and consistent communications between physicians and their patients.

Supporting the notion of a home-based physician assistant robot is the transformation of healthcare delivery in the U.S. More and more primary care physicians are moving from a fee-for-service business model, where doctors are paid according to the procedures used to treat a patient, to “capitation,” where doctors are paid a set amount for each patient they see. This shift in how doctors are compensated is gaining momentum as a key solution for reducing the skyrocketing costs of healthcare: “…inadequate, unnecessary, uncoordinated, and inefficient care and suboptimal business processes eat up at least 35%—and maybe over 50%—of the more than $3 trillion that the country spends annually on health care. That suggests more than $1 trillion is being squandered,” states “The Case for Capitation,” a Harvard Business Review article.

Under this new business model, physicians have a strong incentive to reduce or eliminate visits to the ER and hospitalization, so ElliQ’s assistance in early intervention and support of proactive and preventative healthcare is highly valuable. ElliQ’s “new capabilities provide physicians with visibility into the patient’s condition at home while allowing seamless communication… can assist me and my team in early detection and mitigation of health issues, and it increases patients’ involvement in their care through more frequent engagement and communication,” says in a statement Dr. Peter Barker of Family Doctors, a Mass General Brigham-affiliated practice in Swampscott, MA, that is working with Intuition Robotics.

With the new stage in its evolution, ElliQ becomes “a conversational agent for self-reported data on how people are doing based on what the doctor is telling us to look for and, at the same time, a super-simple communication channel between the physician and the patient,” says Skuler. As only 20% of the individual’s health has to do with the administration of healthcare, Skuler says the balance is already taken care of by ElliQ—encouraging exercise, watching nutrition, keeping mentally active, connecting to the outside world, and promoting a sense of purpose.

A recent article in The Communication of the ACM pointed out that “usability concerns have for too long overshadowed questions about the usefulness and acceptability of digital technologies for older adults.” Specifically, the authors challenge the long-held assumption that accessibility and aging research “fall under the same umbrella despite the fact that aging is neither an illness nor a disability.”

For Skuler, a “pyramid of value” is represented in Intuition Robotics offering. At the foundation is the physical product, easy to use and operate and doing what it is expected to do. Then there is the layer of “building relationships based on trust and empathy,” with a lot of humor and social interaction and activities for the users. On top are specific areas of value to older adults, and the first one is healthcare. There will be more in the future, anything that could help older adults live better lives, such as direct connections to the local community. ”Healthcare is an interesting experiment and I’m very much looking forward to see what else the future holds for ElliQ,” says Skuler.

Original. Reposted with permission, 7/7/2021.

Other related articles published in this Open Access Online Scientific Journal include the Following:

The Future of Speech-Based Human-Computer Interaction
Reporter: Ethan Coomber
https://pharmaceuticalintelligence.com/2021/06/23/the-future-of-speech-based-human-computer-interaction/

Deep Medicine: How Artificial Intelligence Can Make Health Care Human Again
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2020/11/11/deep-medicine-how-artificial-intelligence-can-make-health-care-human-again/

Supporting the elderly: A caring robot with ‘emotions’ and memory
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2015/02/10/supporting-the-elderly-a-caring-robot-with-emotions-and-memory/

Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves
Reporter: Abhisar Anand, Research Assistant I
https://pharmaceuticalintelligence.com/2021/06/22/developing-deep-learning-models-dl-for-classifying-emotions-through-brainwaves/

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The Future of Speech-Based Human-Computer Interaction

Reporter: Ethan Coomber, Research Assistant III

2021 LPBI Summer Internship in Data Science and Podcast Library Development
This article reports on a research conducted by the Tokyo Institute of Technology, published on 9 June 2021.

As technology continues to advance, the human-computer relationship develops alongside with it. As researchers and developers find new ways to improve a computer’s ability to recognize the distinct pitches that compose a human’s voice, the potential of technology begins to push back what people previously thought was possible. This constant improvement in technology has allowed us to identify new potential challenges in voice-based technological interaction.

When humans interact with one another, we do not convey our message with only our voices. There are a multitude of complexities to our emotional states and personality that cannot be obtained simply through the sound coming out of our mouths. Aspects of our communication such as rhythm, tone, and pitch are essential in our understanding of one another. This presents a challenge to artificial intelligence as technology is not able to pick up on these cues.

https://www.eurekalert.org/pub_releases/2021-06/tiot-tro060121.php

In the modern day, our interactions with voice-based devices and services continue to increase. In this light, researchers at Tokyo Institute of Technology and RIKEN, Japan, have performed a meta-synthesis to understand how we perceive and interact with the voice (and the body) of various machines. Their findings have generated insights into human preferences, and can be used by engineers and designers to develop future vocal technologies.

– Kate Seaborn

While it will always be difficult for technology to perfectly replicate a human interaction, the inclusion of filler terms such as “I mean…”, “um” and “like…” have been shown to improve human’s interaction and comfort when communicating with technology. Humans prefer communicating with agents that match their personality and overall communication style. The illusion of making the artificial intelligence appear human has a dramatic affect on the overall comfort of the person interacting with the technology. Several factors that have been proven to improve communication are when the artificial intelligence comes across as happy or empathetic with a higher pitched voice.

Using machine learning, computers are able to recognize patterns within human speech rather than requiring programming for specific patterns. This allows for the technology to adapt to human tendencies as they continue to see them. Over time, humans develop nuances in the way they speak and communicate which frequently results in a tendency to shorten certain words. One of the more common examples is the expression “I don’t know”. This expression is frequently reduced to the phrase “dunno”. Using machine learning, computers would be able to recognize this pattern and realize what the human’s intention is.

With advances in technology and the development of voice assistance in our lives, we are expanding our interactions to include computer interfaces and environments. While there are still many advances that need to be made in order to achieve the desirable level of communication, developers have identified the necessary steps to achieve the desirable human-computer interaction.

Sources:

Tokyo Institute of Technology. “The role of computer voice in the future of speech-based human-computer interaction.” ScienceDaily. ScienceDaily, 9 June 2021.

Rev. “Speech Recognition Trends to Watch in 2021 and Beyond: Responsible AI.” Rev, 2 June 2021, http://www.rev.com/blog/artificial-intelligence-machine-learning-speech-recognition.

“The Role of Computer Voice in the Future of Speech-Based Human-Computer Interaction.” EurekAlert!, 1 June 2021, http://www.eurekalert.org/pub_releases/2021-06/tiot-tro060121.php.

Other related articles published in this Open Access Online Scientific Journal include the Following:

Deep Medicine: How Artificial Intelligence Can Make Health Care Human Again
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2020/11/11/deep-medicine-how-artificial-intelligence-can-make-health-care-human-again/

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Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves
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Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis
Reporter: Aviva Lev-Ari, PhD, RN
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The Human Genome Project
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Developing Deep Learning Models (DL) for the Instant Prediction of Patients with Epilepsy

Reporter: Srinivas Sriram, Research Assistant I
Research Team: Srinivas Sriram, Abhisar Anand

2021 LPBI Summer Intern in Data Science and Website Construction
This article reports on a research study conducted from January 2021 to May 2021.
This Research was completed before the 2021 LPBI Summer Internship that began on 6/15/2021.

The main criterion of this study was to utilize the dataset (shown above) to develop a DL network that could accurately predict new seizures based on incoming data. To begin the study, our research group did some exploratory data analysis on the dataset and we recognized the key defining pattern of the data that allowed for the development of the DL model. This pattern of the data can be represented in the graph above, where the lines representing seizure data had major spikes in extreme hertz values, while the lines representing normal patient data remained stable without any spikes. We utilized this pattern as a baseline for our model. 

Conclusions and Future Improvements:

Through our system, we were able to create a prototype solution that would predict when seizures happened in a potential patient using an accurate LSTM network and a reliable hardware system. This research can be implemented in hospitals with patients suffering from epilepsy in order to help them as soon as they experience a seizure to prevent damage. However, future improvements need to be made to this solution to allow it to be even more viable in the Healthcare Industry, which is listed below.

  • Needs to be implemented on a more reliable EEG headset (covers all neurons of the brain, less prone to electric disruptions shown in the prototype). 
  • Needs to be tested on live patients to deem whether the solution is viable and provides a potential solution to the problem. 
  • The network can always be fine-tuned to maximize performance. 
  • A better alert system can be implemented to provide as much help as possible. 

These improvements, when implemented, can help provide a real solution to one of the most common diseases faced in the world. 

Background Information:

Epilepsy is described as a brain disorder diagnostic category for multiple occurrences of seizures that happen within recurrent and/or a brief timespan. According to the World Health Organization, seizure disorders, including epilepsy, are among the most common neurological diseases. Those who suffer seizures have a 3 times higher risk of premature death. Epilepsy is often treatable, especially when physicians can provide necessary treatment quickly. When untreated, however, seizures can cause physical, psychological, and emotional, including isolation from others. Quick diagnosis and treatment prevent suffering and save lives. The importance of a quick diagnosis of epilepsy has led to our research team developing Deep Learning (DL) algorithms for the sole purpose of detecting epileptic seizures as soon as they occur. 

Throughout the years, one common means of detecting Epilepsy has emerged in the form of an electroencephalogram (EEG). EEGs can detect and compile “normal” and “abnormal “brain wave activity” and “indicate brain activity or inactivity that correlates with physical, emotional, and intellectual activities”. EEG waves are classified mainly by brain wave frequencies (EEG, 2020). The most commonly studied are delta, theta, alpha, sigma, and beta waves. Alpha waves, 8 to 12 hertz, are the key wave that occurs in normal awake people. They are the defining factor for the everyday function of the adult brain. Beta waves, 13 to 30 hertz, are the most common type of wave in both children and adults. They are found in the frontal and central areas of the brain and occur at a certain frequency which, if slow, is likely to cause dysfunction. Theta waves, 4 to 7 hertz, are also found in the front of the brain, but they slowly move backward as drowsiness increases and the brain enters the early stages of sleep. Theta waves are known as active during focal seizures. Delta waves, 0.5 to 4 hertz, are found in the frontal areas of the brain during deep sleep. Sigma waves, 12-16 hertz, are very slow frequency waves that occur during sleep. EEG detection of electrical brain wave frequencies can be used to detect and diagnose seizures based on their deviation from usual brain wave patterns.

In this particular research project, our research group hoped to develop a DL algorithm that when implemented on a live, portable EEG brain wave capturing device, could accurately predict when a particular patient was suffering from Epilepsy as soon as it occurred. This would be accomplished by creating a network that could detect when the brain frequencies deviated from the normal frequency ranges. 

The Study:

Line Graph representing EEG Brain Waves from a Seizure versus EEG Brain Waves from a normal individual. 

Source Dataset: https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition

To expand more on the dataset, it is an EEG data set compiled by Qiuyi Wu and Ernest Fokoue (2021) from the work of medical researchers R.Andrzejak, M.D. et al. (2001) which had been made public domain through the UCI Machine Learning Repository We also confirmed fair use permission with UCI. The dataset had been gathered by Andrzejak during examinations of 500 patients with a chronic seizure disorder. R.G.Andrzejak, et al. (2001) recorded each entry in the EEG dataset used for this project within 23.6 seconds in a time-series data structure. Each row in the dataset represented a patient recorded. The continuous variables in the dataset were single EEG data points at that specific point in time during the measuring period. At the end of the dataset, was a y-variable that indicated whether or not the patient had a seizure during the period the data was recorded. The continuous variables, or the EEG data, for each patient, varied widely based on whether the patient was experiencing a seizure at that time. The Wu & Fokoue Dataset (2021) consists of one file of 11,500 rows, each with 178 sequential data points concatenated from the original dataset of 5 data folders, each including 100 files of EEG recordings of 23.6 seconds and containing 4097 data points. Each folder contained a single, original subset. Subset A contained EEG data gathering during epileptic seizure…. Subset B contained EEG data from brain tumor sites. Subset 3, from a healthy site where tumors had been located. Subsets 4 and 5 from non-seizure patients at rest with eyes open and closed, respectively. 

Based on the described data, our team recognized that a Recurrent Neural Network (RNN) was needed to input the sequential data and return an output of whether the sequential data was a seizure or not. However, we realized that RNN models are known to get substantially large over time, reducing computation speeds. To help provide a solution to this issue, our group decided to implement a long-short-term memory (LSTM) model. After deciding our model’s architecture, we proceeded to train our model in two different DL frameworks inside Python, TensorFlow, and PyTorch. Through various rounds of retesting and redesigning, we were able to train and develop two accurate models in each of the models that not only performed well while learning the data while training, but also could accurately predict new data in the testing set (98 percent accuracy on the unseen data). These LSTM networks could classify normal EEG data when the brain waves are normal, and then immediately predict the seizure data based on if a dramatic spike occurred in the data. 

After training our model, we had to implement our model in a real-life prototype scenario in which we utilized a Single Board Computer (SBC) in the Raspberry Pi 4 and a live capturing EEG headset in the Muse 2 Headband. The two hardware components would sync up through Bluetooth and the headband would return EEG data to the Raspberry Pi, which would process the data. Through the Muselsl API in Python, we were able to retrieve this EEG data in a format similar to the manner implemented during training. This new input data would be fed into our LSTM network (TensorFlow was chosen for the prototype due to its better performance than the PyTorch network), which would then output the result of the live captured EEG data in small intervals. This constant cycle would be able to accurately predict a seizure as soon as it occurs through batches of EEG data being fed into the LSTM network. Part of the reason why our research group chose the Muse Headband, in particular, was not only due to its compatibility with Python but also due to the fact that it was able to represent seizure data. Because none of our members had epilepsy, we had to find a reliable way of testing our model to make sure it worked on the new data. Through electrical disruptions in the wearable Muse Headband, we were able to simulate these seizures that worked with our network’s predictions. In our program, we implemented an alert system that would email the patient’s doctor as soon as a seizure was detected.

Individual wearing the Muse 2 Headband

Image Source: https://www.techguide.com.au/reviews/gadgets-reviews/muse-2-review-device-help-achieve-calm-meditation/

Sources Cited:

Wu, Q. & Fokoue, E. (2021).  Epileptic seizure recognition data set: Data folder & Data set description. UCI Machine Learning Repository: Epileptic Seizure Recognition. Jan. 30. Center for Machine Learning and Intelligent Systems, University of California Irvine.

Nayak, C. S. (2020). EEG normal waveforms.” StatPearls [Internet]. U.S. National Library of Medicine, 31 Jul. 2020, www.ncbi.nlm.nih.gov/books/NBK539805/#.

Epilepsy. (2019). World Health Organization Fact Sheet. Jun. https://www.who.int/ news-room/fact-sheet s/detail/epilepsy

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Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves

Reporter: Abhisar Anand, Research Assistant I
Research Team: Abhisar Anand, Srinivas Sriram

2021 LPBI Summer Internship in Data Science and Website construction.
This article reports on a research study conducted till December 2020.
Research completed before the 2021 LPBI Summer Internship began in 6/15/2021.

As the field of Artificial Intelligence progresses, various algorithms have been implemented by researchers to classify emotions from EEG signals. Few researchers from China and Singapore released a paper (“An Investigation of Deep Learning Models from EEG-Based Emotion Recognition”) analyzing different types of DL model architectures such as deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid of CNN and LSTM (CNN-LSTM). The dataset used in this investigation was the DEAP Dataset which consisted of EEG signals of patients that watched 40 one-minute long music videos and then rated them in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. The result of the investigation presented that CNN (90.12%) and CNN-LSTM (94.7%) models had the highest performance out of the batch of DL models. On the other hand, the DNN model had a very fast training speed but was not able to perform as accurately as other other models. The LSTM model was also not able to perform accurately and the training speed was much slower as it was difficult to achieve convergence.

This research in the various model architectures provides a sense of what the future of Emotion Classification with AI holds. These Deep Learning models can be implemented in a variety of different scenarios across the world, all to help with detecting emotions in scenarios where it may be difficult to do so. However, there needs to be more research implemented in the model training aspect to ensure the accuracy of the classification is top-notch. Along with that, newer and more reliable hardware can be implemented in society to provide an easy-to-access and portable EEG collection device that can be used in any different scenario across the world. Overall, although future improvements need to be implemented, the future of making sure that emotions are accurately detected in all people is starting to look a lot brighter thanks to the innovation of AI in the neuroscience field.

Emotions are a key factor in any person’s day to day life. Most of the time, we as humans can detect these emotions through physical cues such as movements, facial expressions, and tone of voice. However, in certain individuals, it can be hard to identify their emotions through their visible physical cues. Recent studies in the Machine Learning and AI field provide a particular development in the ability to detect emotions through brainwaves, more specifically EEG brainwaves. These researchers from across the world utilize the same concept of EEG implemented in AI to help predict the state an individual is in at any given moment.

Emotion classification based on brain wave: a survey (Figure 4)

Image Source: https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0201-x

EEGs can detect and compile normal and abnormal brain wave activity and indicate brain activity or inactivity that correlates with physical, emotional, and intellectual activities. EEG signals are classified mainly by brain wave frequencies. The most commonly studied are delta, theta, alpha, sigma, and beta waves. Alpha waves, 8 to 12 hertz, are the key wave that occurs in normal awake people. They are the defining factor for the everyday function of the adult brain. Beta waves, 13 to 30 hertz, are the most common type of wave in both children and adults. They are found in the frontal and central areas of the brain and occur at a certain frequency which, if slowed, is likely to cause dysfunction. Theta waves, 4 to 7 hertz, are also found in the front of the brain, but they slowly move backward as drowsiness increases and the brain enters the early stages of sleep. Theta waves are known as active during focal seizures. Delta waves, 0.5 to 4 hertz, are found in the frontal areas of the brain during deep sleep. Sigma waves, 12-16 hertz, are very slow frequency waves that occur during sleep. These EEG signals can help for the detection of emotions based on the frequencies that the signals happen in and the activity of the signals (whether they are active or relatively calm). 

Sources:

Zhang, Yaqing, et al. “An Investigation of Deep Learning Models for EEG-Based Emotion Recognition.” Frontiers in Neuroscience, vol. 14, 2020. Crossref, doi:10.3389/fnins.2020.622759.

Nayak, Anilkumar, Chetan, Arayamparambil. “EEG Normal Waveforms.” National Center for Biotechnology Information, StatPearls Publishing LLC., 4 May 2021, http://www.ncbi.nlm.nih.gov/books/NBK539805.

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Prediction of Cardiovascular Risk by Machine Learning (ML) Algorithm: Best performing algorithm by predictive capacity had area under the ROC curve (AUC) scores: 1st, quadratic discriminant analysis; 2nd, NaiveBayes and 3rd, neural networks, far exceeding the conventional risk-scaling methods in Clinical Use
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Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes
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Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes

Reporter: Amandeep Kaur, B.Sc., M.Sc.

A recent study reports the development of an advanced AI algorithm which predicts up to five years in advance the starting of type 2 diabetes by utilizing regularly collected medical data. Researchers described their AI model as notable and distinctive based on the specific design which perform assessments at the population level.

The first author Mathieu Ravaut, M.Sc. of the University of Toronto and other team members stated that “The main purpose of our model was to inform population health planning and management for the prevention of diabetes that incorporates health equity. It was not our goal for this model to be applied in the context of individual patient care.”

Research group collected data from 2006 to 2016 of approximately 2.1 million patients treated at the same healthcare system in Ontario, Canada. Even though the patients were belonged to the same area, the authors highlighted that Ontario encompasses a diverse and large population.

The newly developed algorithm was instructed with data of approximately 1.6 million patients, validated with data of about 243,000 patients and evaluated with more than 236,000 patient’s data. The data used to improve the algorithm included the medical history of each patient from previous two years- prescriptions, medications, lab tests and demographic information.

When predicting the onset of type 2 diabetes within five years, the algorithm model reached a test area under the ROC curve of 80.26.

The authors reported that “Our model showed consistent calibration across sex, immigration status, racial/ethnic and material deprivation, and a low to moderate number of events in the health care history of the patient. The cohort was representative of the whole population of Ontario, which is itself among the most diverse in the world. The model was well calibrated, and its discrimination, although with a slightly different end goal, was competitive with results reported in the literature for other machine learning–based studies that used more granular clinical data from electronic medical records without any modifications to the original test set distribution.”

This model could potentially improve the healthcare system of countries equipped with thorough administrative databases and aim towards specific cohorts that may encounter the faulty outcomes.

Research group stated that “Because our machine learning model included social determinants of health that are known to contribute to diabetes risk, our population-wide approach to risk assessment may represent a tool for addressing health disparities.”

Sources:

https://www.cardiovascularbusiness.com/topics/prevention-risk-reduction/new-ai-model-healthcare-data-predict-type-2-diabetes?utm_source=newsletter

Reference:

Ravaut M, Harish V, Sadeghi H, et al. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA Netw Open. 2021;4(5):e2111315. doi:10.1001/jamanetworkopen.2021.11315 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2780137

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Fighting Chaos with care, community trust, engagement must be cornerstones of pandemic response

Reporter: Amandeep Kaur, BSc, MSc (Exp. 6/2021)

According to the Global Health Security Index released by Johns Hopkins University in October 2019 in collaboration with Nuclear Threat Initiative (NTI) and The Economist Intelligence Unit (EIU), the United States was announced to be the best developed country in the world to tackle any pandemic or health emergency in future.

The table turned within in one year of outbreak of the novel coronavirus COVID-19. By the end of March 2021, the country with highest COVID-19 cases and deaths in the world was United States. According to the latest numbers provided by World Health Organization (WHO), there were more than 540,000 deaths and more than 30 million confirmed cases in the United States.

Joia Mukherjee, associate professor of global health and social medicine in the Blavatnik Institute at Harvard Medical School said,

“When we think about how to balance control of an epidemic over chaos, we have to double down on care and concern for the people and communities who are hardest hit”.

She also added that U.S. possess all the necessary building blocks required for a health system to work, but it lacks trust, leadership, engagement and care to assemble it into a working system.

Mukherjee mentioned about the issues with the Index that it undervalued the organized and integrated system which is necessary to help public meet their needs for clinical care. Another necessary element for real health safety which was underestimated was conveying clear message and social support to make effective and sustainable efforts for preventive public health measures.

Mukherjee is a chief medical officer at Partners In Health, an organization focused on strengthening community-based health care delivery. She is also a core member of HMS community members who play important role in constructing a more comprehensive response to the pandemic in all over the U.S. With years of experience, they are training global health care workers, analyzing the results and constructing an integrated health system to fight against the widespread health emergency caused by coronavirus all around the world.

Mukherjee encouraged to strengthen the consensus among the community to constrain this infectious disease epidemic. She suggested that validation of the following steps are crucial such as testing of the people with symptoms of infection with coronavirus, isolation of infected individuals by providing them with necessary resources and providing clinical treatment and care to those people who are in need. Mukherjee said, that community engagement and material support are not just idealistic goal rather these are essential components for functioning of health care system during an outburst of coronavirus.

Continued alertness such as social distancing and personal contact with infected individual is important because it is not possible to rapidly replace the old-school public health approaches with new advanced technologies like smart phone applications or biomedical improvements.

Public health specialists emphasized that the infection limitation is the only and most vital strategy for controlling the outbreak in near future, even if the population is getting vaccinated. It is crucial to slowdown the spread of disease for restricting the natural modification of more dangerous variants as that could potentially escape the immune protection mechanism developed by recently generated vaccines as well as natural immune defense systems.

Making Crucial connections

The treatment is more expensive and complicated in areas with less health facilities, said Paul Farmer, the Kolokotrones University Professor at Harvard and chair of the HMS Department of Global Health and Social Medicine. He called this situation as treatment nihilism. Due to shortage of resources, the maximum energy is focused in public health care and prevention efforts. U.S. has resources to cope up with the increasing demand of hospital space and is developing vaccines, but there is a form of containment nihilism- which means prevention and infection containment are unattainable- said by many experts.

Farmer said, integration of necessary elements such as clinical care, therapies, vaccines, preventive measures and social support into a single comprehensive plan is the best approach for a better response to COVID-19 disease. He understands the importance of community trust and integrated health care system for fighting against this pandemic, as being one of the founders of Partners In Health and have years of experience along with his colleagues from HMS and PIH in fighting epidemics of HIV, Ebola, cholera, tuberculosis, other infectious and non-infectious diseases.

PIH launched the Massachusetts Community Tracing Collaborative (CTC), which is an initiative of contact tracing statewide in partnership with several other state bodies, local boards of Health system and PIH. The CTC was setup in April 2020 in U.S. by Governor Charlie Baker, with leadership from HMS faculty, to build a unified response to COVID-19 and create a foundation for a long-term movement towards a more integrated community-based health care system.

The contact tracing involves reaching out to individuals who are COVID-19 positive, then further detect people who came in close contact with infected individuals and screen out people with coronavirus symptoms and encourage them to seek testing and take necessary precautions to break the chain of infection into the community.

In the initial phase of outbreak, the CTC group comprises of contact tracers and health care coordinators who spoke 23 different languages, including social workers, public health practitioners, nurses and staff members from local board health agencies with deep links to the communities they are helping. The CTC worked with 339 out of 351 state municipalities with local public health agencies relied completely on CTC whereas some cities and towns depend occasionally on CTC backup. According to a report, CTC members reached up to 80 percent of contact tracking in hard-hit and resource deprived communities such as New Bedford.

Putting COVID-19 in context

Based on generations of experience helping people surviving some of the deadliest epidemic and endemic outbreaks in places like Haiti, Mexico, Rwanda and Peru, the staff was alert that people with bad social and economic condition have less space to get quarantined and follow other public health safety measures and are most vulnerable people at high risk in the pandemic situation.

Infected individuals or individuals at risk of getting infected by SARS-CoV-2 had many questions regarding when to seek doctor’s help and where to get tested, reported by contact tracers. People were worried about being evicted from work for two weeks and some immigrants worried about basic supplies as they were away from their family and friends.

The CTC team received more than 7,000 requests for social support assistance in the initial three months. The staff members and contact tracers were actively connecting the resourceful individuals with the needy people and filling up the gap when there was shortage in their own resources.

Farmer said, “COVID is a misery-seeking missile that has targeted the most vulnerable.”

The reality that infected individuals concerned about lacking primary household items, food items and access to childcare, emphasizes the urgency of rudimentary social care and community support in fighting against the pandemic. Farmer said, to break the chain of infection and resume society it is mandatory to meet all the elementary needs of people.

“What kinds of help are people asking for?” Farmer said and added “it’s important to listen to what your patients are telling you.”

An outbreak of care

The launch of Massachusetts CTC with the support from PIH, started receiving requests from all around the country to assist initiating contact tracing procedures. In May, 2020 the organization announced the launch of a U.S. public health accompaniment to cope up with the asked need.

The unit has included team members in nearly 24 states and municipal health departments in the country and work in collaboration with local organizations. The technical support on things like choosing and implementing the tools and software for contact tracing was provided by PIH. To create awareness and provide new understanding more rapidly, a learning collaboration was established with more than 200 team members from more than 100 different organizations. The team worked to meet the needs of population at higher risk of infection by advocating them for a stronger and more reliable public health response.

The PIH public health team helped to train contact trackers in the Navajo nation and operate to strengthen the coordination between SARS-CoV-2 testing, efforts for precaution, clinical health care delivery and social support in vulnerable communities around the U.S.

“For us to reopen our schools, our churches, our workplaces,” Mukherjee said, “we have to know where the virus is spreading so that we don’t just continue on this path.”

SOURCE:

https://hms.harvard.edu/news/fighting-chaos-care?utm_source=Silverpop&utm_medium=email&utm_term=field_news_item_1&utm_content=HMNews04052021

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The WHO team is expected to soon publish a 300-page final report on its investigation, after scrapping plans for an interim report on the origins of SARS-CoV-2 — the new coronavirus responsible for killing 2.7 million people globally

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/03/27/the-who-team-is-expected-to-soon-publish-a-300-page-final-report-on-its-investigation-after-scrapping-plans-for-an-interim-report-on-the-origins-of-sars-cov-2-the-new-coronavirus-responsibl/

Need for Global Response to SARS-CoV-2 Viral Variants

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/02/12/need-for-global-response-to-sars-cov-2-viral-variants/

Mechanistic link between SARS-CoV-2 infection and increased risk of stroke using 3D printed models and human endothelial cells

Reporter: Adina Hazan, PhD

https://pharmaceuticalintelligence.com/2020/12/28/mechanistic-link-between-sars-cov-2-infection-and-increased-risk-of-stroke-using-3d-printed-models-and-human-endothelial-cells/

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2021/02/04/artificial-intelligence-predicts-the-immunogenic-landscape-of-sars-cov-2/

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Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2

Reporter: Irina Robu, PhD

Artificial intelligence makes it imaginable for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using the technologies, computer can be trained to achieve specific tasks by processing large amount of data and recognizing patterns. Scientists from NEC OncoImmunity use artificial intelligence to forecast designs for designing universal vaccines for COVID 19, that contain a broad spectrum of T-cell epitopes capable of providing coverage and protection across the global population. To help test their hypothesis, they profiled the entire SARS COV2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population using host infected cell surface antigen and immunogenicity predictors from NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. They use the epitope maps as a starting point for Monte Carlo simulation intended to identify the most significant epitope hotspot in the virus. Then they analyzed the antigen arrangement and immunogenic landscape to recognize a trend where SARS-COV-2 mutations are expected to have minimized potential to be accessible by host-infected cells, and subsequently noticed by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome.

By merging the antigen arrangement to the infected-host cell surface and immunogenicity estimates of the NEC Immune Profiler with a Monte Carlo and digital twin simulation, the researchers have outlined the entire SARS-CoV-2 proteome and recognized a subset of epitope hotspots that could be used  in a vaccine formulation to provide a wide-ranging coverage across the global population.

By using the database of HLA haplotypes of approximately 22,000 individuals to design  a “digital twin” type simulation to model how efficient various  combinations of hotspots would work in a varied human population. 

SOURCE

https://www.nature.com/articles/s41598-020-78758-5?utm_content=buffer4ebb7

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