Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
@BroadInstitute a shift from Permanently editing DNA to Temporarily revising RNA – An approach with promise for addressing the risk of developing Alzheimer’s by deactivating APOE4 – RESCUE: RNA Editing for Specific C to U Exchange, the platform builds on REPAIR: RNA Editing for Programmable A to I
CRISPR–Cas13 systems have been developed for precise RNA editing, and can potentially be used therapeutically when temporary changes are desirable or when DNA editing is challenging. We have identified and characterized an ultrasmall family of Cas13b proteins—Cas13bt—that can mediate mammalian transcript knockdown. We have engineered compact variants of REPAIR and RESCUE RNA editors by functionalizing Cas13bt with adenosine and cytosine deaminase domains, and demonstrated packaging of the editors within a single adeno-associated virus.
2.1.3.14 @BroadInstitute a shift from Permanently editing DNA to Temporarily revising RNA – An approach with promise for addressing the risk of developing Alzheimer’s by deactivating APOE4 – RESCUE: RNA Editing for Specific C to U Exchange, the platform builds on REPAIR: RNA Editing for Programmable A to I, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
The RNA editors converted “the nucleotide base adenine to inosine, or letters A to I. Zhang and colleagues took the REPAIR fusion and evolved it in the lab until it could change cytosine to uridine, or C to U.”
Using Cas13, Zhang’s team was able to take the APOE4 gene — believed to carry the added risk of spurring Alzheimer’s — and changed it to a benign APOE2.
RNA-guided DNA insertion with CRISPR-associated transposases
Other related articles on CRISPR derived Gene Editing for Gene Therapy published in this Open Access on Online Scientific Journal include the following:
Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
Subject: Regarding your article: Walter Isaacson is writing a book about CRISPR. He’s got company —>>>>>> e-mail from AVIVA LEV-ARI, PhD, RN, EDITOR-in-CHIEF PharmaceuticalIntelligence.com
It will be appreciated if you will write a follow up to your 7/2/2019 article to cover this volume (Eight Parts) and all our 16 volumes, BioMed e-Series, 96,000 Page Downloads !!
SOURCE for Damian Grade’s article in StatNews:
Walter Isaacson is writing a book about CRISPR. He’s got company
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
Reporter: Aviva Lev-Ari, PhD, RN
3.2.8 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, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
Best three machine-learning methods with the best predictive capacity had area under the ROC curve (AUC) scores of
0.7086 (quadratic discriminant analysis),
0.7084 (NaiveBayes) and
0.7042 (neural networks)
the conventional risk-scaling methods—which are widely used in clinical practice in Spain—fell in at 11th and 12th places, with AUCs below 0.64.
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:10.1111/ijcp.13389
To analyze the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales.
Methods
We calculated cardiovascular risk by means of 15 machine‐learning methods and using the SCORE and REGICOR scales and in 38,527 patients in the Spanish ESCARVAL RISK cohort, with five‐year follow‐up. We considered patients to be at high risk when the risk of a cardiovascular event was over 5% (according to SCORE and machine learning methods) or over 10% (using REGICOR). The area under the receiver operating curve (AUC) and the C‐index were calculated, as well as the diagnostic accuracy rate, error rate, sensitivity, specificity, positive and negative predictive values, positive likelihood ratio, and number of needed to treat to prevent a harmful outcome.
Results
The method with the greatest predictive capacity was quadratic discriminant analysis, with an AUC of 0.7086, followed by NaiveBayes and neural networks, with AUCs of 0.7084 and 0.7042, respectively. REGICOR and SCORE ranked 11th and 12th, respectively, in predictive capacity, with AUCs of 0.63. Seven machine learning methods showed a 7% higher predictive capacity (AUC) as well as higher sensitivity and specificity than the REGICOR and SCORE scales.
Conclusions
Ten of the 15 machine learning methods tested have a better predictive capacity for cardiovascular events and better classification indicators than the SCORE and REGICOR risk assessment scales commonly used in clinical practice in Spain. Machine learning methods should be considered in the development of future cardiovascular risk scales.
This article is protected by copyright. All rights reserved.
This article is excerpted from the Harvard Business Review, May 28, 2019
By Moni Miyashita, Michael Brady
3.4.13 The Health Care Benefits of Combining Wearables and AI, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 3: AI in Medicine
In southeast England, patients discharged from a group of hospitals serving 500,000 people are being fitted with a Wi-Fi-enabled armband that remotely monitors vital signs such as respiratory rate, oxygen levels, pulse, blood pressure, and body temperature.
Under a National Health Service pilot program that now incorporates artificial intelligence to analyze all that patient data in real time, hospital readmission rates are down, and emergency room visits have been reduced. What’s more, the need for costly home visits has dropped by 22%. Longer term, adherence to treatment plans have increased to 96%, compared to the industry average of 50%.
The AI pilot is targeting what Harvard Business School Professor and Innosight co-founder Clay Christensen calls “non-consumption.” These are opportunity areas where consumers have a job to be done that isn’t currently addressed by an affordable or convenient solution.
Before the U.K. pilot at the Dartford and Gravesham hospitals, for instance, home monitoring had involved dispatching hospital staffers to drive up to 90 minutes round-trip to check in with patients in their homes about once per week. But with algorithms now constantly searching for warning signs in the data and alerting both patients and professionals instantly, a new capability is born: providing healthcare before you knew you even need it.
The biggest promise of artificial intelligence — accurate predictions at near-zero marginal cost — has rightly generated substantial interest in applying AI to nearly every area of healthcare. But not every application of AI in healthcare is equally well-suited to benefit. Moreover, very few applications serve as an appropriate strategic response to the largest problems facing nearly every health system: decentralization and margin pressure.
Take for example, medical imaging AI tools — an area in which hospitals are projected to spend $2 billion annually within four years. Accurately diagnosing diseases from cancers to cataracts is a complex task, with difficult-to-quantify but typically major consequences. However, the task is currently typically part of larger workflows performed by extensively trained, highly specialized physicians who are among some of the world’s best minds. These doctors might need help at the margins, but this is a job already being done. Such factors make disease diagnosis an extraordinarily difficult area for AI to create transformative change. And so the application of AI in such settings — even if beneficial to patient outcomes — is unlikely to fundamentally improve the way healthcare is delivered or to substantially lower costs in the near-term.
However, leading organizations seeking to decentralize care can deploy AI to do things that have never been done before. For example: There’s a wide array of non-acute health decisions that consumers make daily. These decisions do not warrant the attention of a skilled clinician but ultimately play a large role in determining patient’s health — and ultimately the cost of healthcare.
According to the World Health Organization, 60% of related factors to individual health and quality of life are correlated to lifestyle choices, including taking prescriptions such as blood-pressure medications correctly, getting exercise, and reducing stress. Aided by AI-driven models, it is now possible to provide patients with interventions and reminders throughout this day-to-day process based on changes to the patient’s vital signs.
Home health monitoring itself isn’t new. Active programs and pilot studies are underway through leading institutions ranging from Partners Healthcare, United Healthcare, and the Johns Hopkins School of Medicine, with positive results. But those efforts have yet to harness AI to make better judgements and recommendations in real time. Because of the massive volumes of data involved, machine learning algorithms are particularly well suited to scaling that task for large populations. After all, large sets of data are what power AI by making those algorithms smarter.
By deploying AI, for instance, the NHS program is not only able to scale up in the U.K. but also internationally. Current Health, the venture-capital backed maker of the patient monitoring devices used in the program, recently received FDA clearance to pilot the system in the U.S. and is now testing it with New York’s Mount Sinai Hospital. It’s part of an effort to reduce patient readmissions, which costs U.S. hospitals about $40 billion annually.
The early success of such efforts drives home three lessons in using AI to address non-consumption in the new world of patient-centric healthcare:
1) Focus on impacting critical metrics – for example, reducing costly hospital readmission rates.
Start small to home in on the goal of making an impact on a key metric tied to both patient outcomes and financial sustainability. As in the U.K. pilot, this can be done through a program with select hospitals or provider locations. In another case Grady Hospital, the largest public hospital in Atlanta, points to $4M in saving from reduced readmission rates by 31% over two years thanks to the adoption of an AI tool which identifies ‘at-risk’ patients. The system alerts clinical teams to initiate special patient touch points and interventions.
2) Reduce risk by relying on new kinds of partners.
Don’t try to do everything alone. Instead, form alliances with partners that are aiming to tackle similar problems. Consider the Synaptic Healthcare Alliance, a collaborative pilot program between Aetna, Ascension, Humana, Optum, and others. The alliance is using Blockchain to create a giant dataset across various health care providers, with AI trials on the data getting underway. The aim is to streamline health care provider data management with the goal of reducing the cost of processing claims while also improving access to care. Going it alone can be risky due to data incompatibility issues alone. For instance, the M.D. Anderson Cancer Center had to write off millions in costs for a failed AI project due in part to incompatibility with its electronic health records system. By joining forces, Synaptic’s dataset will be in a standard format that makes records and results transportable.
3) Use AI to collaborate, not compete, with highly-trained professionals.
Clinicians are often looking to augment their knowledge and reasoning, and AI can help. Many medical AI applications do actually compete with doctors. In radiology, for instance, some algorithms have performed image-bases diagnosis as well as or better than human experts. Yet it’s unclear if patients and medical institutions will trust AI to automate that job entirely. A University of California at San Diego pilot in which AI successfully diagnosed childhood diseases more accurately than junior-level pediatricians still required senior doctors to personally review and sign off on the diagnosis. The real aim is always going to be to use AI to collaborate with clinicians seeking higher precision — not try to replace them.
MIT and MGH have developed a deep learning model which identifies patients likely to develop breast cancer in the future. Learning from data on 60,000 prior patients, the AI system allows physicians to personalize their approach to breast cancer screening, essentially creating a detailed risk profile for each patient.
Taken together, these three lessons paired with solutions targeted at non-consumption have the potential to provide a clear path to effectively harnessing a technology that has been subject to rampant over-promising. Longer term, we believe the one of the transformative benefits of AI will be deepening relationships between health providers and patients. The U.K. pilot, for instance, is resulting in more frequent proactive check-ins that never would have happened before. That’s good for both improving health as well as customer loyalty in the emerging consumer-centric healthcare marketplace.
3.4.14 These twelve artificial intelligence innovations are expected to start impacting clinical care by the end of the decade, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 3: AI in Medicine
April 11, 2019 – There’s no question that artificial intelligence is moving quickly in the healthcare industry. Even just a few months ago, AI was still a dream for the next generation: something that would start to enter regular care delivery in a couple of decades – maybe ten or fifteen years for the most advanced health systems.
Even Partners HealthCare, the Boston-based giant on the very cutting edge of research and reform, set a ten-year timeframe for artificial intelligence during its 2018 World Medical Innovation Forum, identifying a dozen AI technologies that had the potential to revolutionize patient care within the decade.
But over the past twelve months, research has progressed so rapidly that Partners has blown up that timeline.
Instead of viewing AI as something still lingering on the distant horizon, this year’s Disruptive Dozen panel was tasked with assessing which AI innovations will be ready to fundamentally alter the delivery of care by 2020 – now less than a year away.
Sixty members of the Partners faculty participated in nominating and narrowing down the tools they think will have an almost immediate benefit for patients and providers, explained Erica Shenoy, MD, PhD, an infectious disease specialist at Massachusetts General Hospital (MGH).
“These are innovations that have a strong potential to make significant advancement in the field, and they are also technologies that are pretty close to making it to market,” she said.
The results include everything from mental healthcare and clinical decision support to coding and communication, offering patients and their providers a more efficient, effective, and cost-conscious ecosystem for improving long-term outcomes.
In order from least to greatest potential impact, here are the twelve artificial intelligence innovations poised to become integral components of the next decade’s data-driven care delivery system.
NARROWING THE GAPS IN MENTAL HEALTHCARE
Nearly twenty percent of US patients struggle with a mental health disorder, yet treatment is often difficult to access and expensive to use regularly. Reducing barriers to access for mental and behavioral healthcare, especially during the opioid abuse crisis, requires a new approach to connecting patients with services.
AI-driven applications and therapy programs will be a significant part of the answer.
“The promise and potential for digital behavioral solutions and apps is enormous to address the gaps in mental healthcare in the US and across the world,” said David Ahern, PhD, a clinical psychologist at Brigham & Women’s Hospital (BWH).
Smartphone-based cognitive behavioral therapy and integrated group therapy are showing promise for treating conditions such as depression, eating disorders, and substance abuse.
While patients and providers need to be wary of commercially available applications that have not been rigorously validated and tested, more and more researchers are developing AI-based tools that have the backing of randomized clinical trials and are showing good results.
A panel of experts from Partners HealthCare presents the Disruptive Dozen at WMIF19.
Source: Partners HealthCare
STREAMLINING WORKFLOWS WITH VOICE-FIRST TECHNOLOGY
Natural language processing is already a routine part of many behind-the-scenes clinical workflows, but voice-first tools are expected to make their way into the patient-provider encounter in a new way.
Smart speakers in the clinic are prepping to relieve clinicians of their EHR burdens, capturing free-form conversations and translating the content into structured documentation. Physicians and nurses will be able to collect and retrieve information more quickly while spending more time looking patients in the eye.
Patients may benefit from similar technologies at home as the consumer market for virtual assistants continues to grow. With companies like Amazon achieving HIPAA compliance for their consumer-facing products, individuals may soon have more robust options for voice-first chronic disease management and patient engagement.
IDENTIFYING INDIVIDUALS AT HIGH RISK OF DOMESTIC VIOLENCE
Underreporting makes it difficult to know just how many people suffer from intimate partner violence (IPV), says Bharti Khurana, MD, an emergency radiologist at BWH. But the symptoms are often hiding in plain sight for radiologists.
Using artificial intelligence to flag worrisome injury patterns or mismatches between patient-reported histories and the types of fractures present on x-rays can alert providers to when an exploratory conversation is called for.
“As a radiologist, I’m very excited because this will enable me to provide even more value to the patient instead of simply evaluating their injuries. It’s a powerful tool for clinicians and social workers that will allow them to approach patients with confidence and with less worry about offending the patient or the spouse,” said Khurana.
REVOLUTIONIZING ACUTE STROKE CARE
Every second counts when a patient experiences a stroke. In far-flung regions of the United States and in the developing world, access to skilled stroke care can take hours, drastically increasing the likelihood of significant long-term disability or death.
Artificial intelligence has the potential to close the gaps in access to high-quality imaging studies that can identify the type of stroke and the location of the clot or bleed. Research teams are currently working on AI-driven tools that can automate the detection of stroke and support decision-making around the appropriate treatment for the individual’s needs.
In rural or low-resource care settings, these algorithms can compensate for the lack of a specialist on-site and ensure that every stroke patient has the best possible chance of treatment and recovery.
Source: Getty Images
REDUCING ADMINISTRATIVE BURDENS FOR PROVIDERS
The costs of healthcare administration are off the charts. Recent data from the Center for American progress states that providers spend about $282 billion per year on insurance and medical billing, and the burdens are only going to keep getting bigger.
Medical coding and billing is a perfect use case for natural language processing and machine learning. NLP is well-suited to translating free-text notes into standardized codes, which can move the task off the plates of physicians and reduce the time and effort spent on complying with convoluted regulations.
“The ultimate goal is to help reduce the complexity of the coding and billing process through automation, thereby reducing the number of mistakes – and, in turn, minimizing the need for such intense regulatory oversight,” Partners says.
NLP is already in relatively wide use for this task, and healthcare organizations are expected to continue adopting this strategy as a way to control costs and speed up their billing cycles.
UNLEASHING HEALTH DATA THROUGH INFORMATION EXCHANGE
AI will combine with another game-changing technology, known as FHIR, to unlock siloes of health data and support broader access to health information.
Patients, providers, and researchers will all benefit from a more fluid health information exchange environment, especially since artificial intelligence models are extremely data-hungry.
Stakeholders will need to pay close attention to maintaining the privacy and security of data as it moves across disparate systems, but the benefits have the potential to outweigh the risks.
“It completely depends on how everyone in the medical community advocates for, builds, and demands open interfaces and open business models,” said Samuel Aronson, Executive Director of IT at Partners Personalized Medicine.
“If we all row in the same direction, there’s a real possibility that we will see fundamental improvements to the healthcare system in 3 to 5 years.”
OFFERING NEW APPROACHES FOR EYE HEALTH AND DISEASE
Image-heavy disciplines have started to see early benefits from artificial intelligence since computers are particularly adept at analyzing patterns in pixels. Ophthalmology is one area that could see major changes as AI algorithms become more accurate and more robust.
From glaucoma to diabetic retinopathy, millions of patients experience diseases that can lead to irreversible vision loss every year. Employing AI for clinical decision support can extend access to eye health services in low-resource areas while giving human providers more accurate tools for catching diseases sooner.
REAL-TIME MONITORING OF BRAIN HEALTH
The brain is still the body’s most mysterious organ, but scientists and clinicians are making swift progress unlocking the secrets of cognitive function and neurological disease. Artificial intelligence is accelerating discovery by helping providers interpret the incredibly complex data that the brain produces.
From predicting seizures by reading EEG tests to identifying the beginnings of dementia earlier than any human, artificial intelligence is allowing providers to access more detailed, continuous measurements – and helping patients improve their quality of life.
Seizures can happen in patients with other serious illnesses, such as kidney or liver failure, explained, Bandon Westover, MD, PhD, executive director of the Clinical Data Animation Center at MGH, but many providers simply don’t know about it.
“Right now, we mostly ignore the brain unless there’s a special need for suspicion,” he said. “In a year’s time, we’ll be catching a lot more seizures and we’ll be doing it with algorithms that can monitor patients continuously and identify more ambiguous patterns of dysfunction that can damage the brain in a similar manner to seizures.”
AUTOMATING MALARIA DETECTION IN DEVELOPING REGIONS
Malaria is a daily threat for approximately half the world’s population. Nearly half a million people died from the mosquito-borne disease in 2017, according to the World Health Organization, and the majority of the victims are children under the age of five.
Deep learning tools can automate the process of quantifying malaria parasites in blood samples, a challenging task for providers working without pathologist partners. One such tool achieved 90 percent accuracy and specificity, putting it on par with pathology experts.
This type of software can be run on a smartphone hooked up to a camera on a microscope, dramatically expanding access to expert-level diagnosis and monitoring.
Source: Getty Images
AUGMENTING DIAGNOSTICS AND DECISION-MAKING
Artificial intelligence has made especially swift progress in diagnostic specialties, including pathology. AI will continue to speed down the road to maturity in this area, predicts Annette Kim, MD, PhD, associate professor of pathology at BWH and Harvard Medical School.
“Pathology is at the center of diagnosis, and diagnosis underpins a huge percentage of all patient care. We’re integrating a huge amount of data that funnels through us to come to a diagnosis. As the number of data points increases, it negatively impacts the time we have to synthesize the information,” she said.
AI can help automate routine, high-volume tasks, prioritize and triage cases to ensure patients are getting speedy access to the right care, and make sure that pathologists don’t miss key information hidden in the enormous volumes of clinical and test data they must comb through every day.
“This is where AI can have a huge impact on practice by allowing us to use our limited time in the most meaningful manner,” Kim stressed.
PREDICTING THE RISK OF SUICIDE AND SELF-HARM
Suicide is the tenth leading cause of death in the United States, claiming 45,000 lives in 2016. Suicide rates are on the rise due to a number of complex socioeconomic and mental health factors, and identifying patients at the highest risk of self-harm is a difficult and imprecise science.
Natural language processing and other AI methodologies may help providers identify high-risk patients earlier and more reliably. AI can comb through social media posts, electronic health record notes, and other free-text documents to flag words or concepts associated with the risk of harm.
Researchers also hope to develop AI-driven apps to provide support and therapy to individuals likely to harm themselves, especially teenagers who commit suicide at higher rates than other age groups.
Connecting patients with mental health resources before they reach a time of crisis could save thousands of lives every year.
REIMAGINING THE WORLD OF MEDICAL IMAGING
Radiology is already one of AI’s early beneficiaries, but providers are just at the beginning of what they will be able to accomplish in the next few years as machine learning explodes into the imaging realm.
AI is predicted to bring earlier detection, more accurate assessment of complex images, and less expensive testing for patients across a huge number of clinical areas.
But as leaders in the AI revolution, radiologists also have a significant responsibility to develop and deploy best practices in terms of trustworthiness, workflow, and data protection.
“We certainly feel the onus on the radiology community to make sure we do deliver and translate this into improved care,” said Alexandra Golby, MD, a neurosurgeon and radiologist at BWH and Harvard Medical School.
“Can radiology live up to the expectations? There are certainly some challenges, including trust and understanding of what the algorithms are delivering. But we desperately need it, and we want to equalize care across the world.”
Radiologists have been among the first to overcome their trepidation about the role of AI in a changing clinical world, and are eagerly embracing the possibilities of this transformative approach to augmenting human skills.”
“All of the imaging societies have opened their doors to the AI adventure,” Golby said. “The community very anxious to learn, codevelop, and work with all of the industry partners to turn this technology into truly valuable tools. We’re very optimistic and very excited, and we look forward to learning more about how AI can improve care.”
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