Advertisements
Feeds:
Posts
Comments

Archive for the ‘Artificial Intelligence – Breakthroughs in Theories and Technologies’ Category


Artificial Intelligence and Cardiovascular Disease

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

 

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

 

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

 

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

 

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

 

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

 

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

 

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

 

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

 

References:

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

 

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

 

https://www.nature.com/articles/s41569-019-0158-5

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

 

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

 

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

 

https://clinicaltrials.gov/ct2/show/NCT03877614

 

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

 

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

 

Advertisements

Read Full Post »


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

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.

 

Machine learning to predict cardiovascular risk

First published: 01 July 2019

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

Abstract

Aims

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.

SOURCE

https://onlinelibrary.wiley.com/doi/abs/10.1111/ijcp.13389

Read Full Post »


The Health Care Benefits of Combining Wearables and AI

Reporter: Gail S. Thornton, M.A.

 

 

This article is excerpted from the Harvard Business Review, May 28, 2019

By Moni Miyashita, Michael Brady

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.

Source:

https://hbr.org/2019/05/the-health-care-benefits-of-combining-wearables-and-ai

 

Read Full Post »


Sepsis Detection using an Algorithm More Efficient than Standard Methods

Reporter : Irina Robu, PhD

Sepsis is a complication of severe infection categorized by a systemic inflammatory response with mortality rates between 25% to 30% for severe sepsis and 40% to 70% for septic shock. The most common sites of infection are the respiratory, genitourinary, and gastrointestinal systems, as well as the skin and soft tissue. The first manifestation of sepsis is fever with pneumonia being the most common symptom of sepsis with initial treatment which contains respiratory stabilization shadowed by aggressive fluid resuscitation. When fluid resuscitation fails to restore mean arterial pressure and organ perfusion, vasopressor therapy is indicated.

However, a machine-learning algorithm tested by Christopher Barton, MD from UC-San Francisco has exceeded the four typical methods used for catching sepsis early in hospital patients, giving clinicians up to 48 hours to interfere before the condition has a chance to begin turning dangerous. The four standard methods were Systemic Inflammatory Response Syndrome (SIRS) criteria, Sequential (Sepsis-Related) Organ-Failure Assessment (SOFA) and Modified Early Warning System (MEWS). The purpose of dividing the data sets between two far-flung institutions was to train and test the algorithm on demographically miscellaneous patient populations.

The patients involved in the study were admitted to hospital without sepsis and all had at least one recording of each of six vital signs such as oxygen levels in the blood, heart rate, respiratory rate, temperature, systolic blood pressure and diastolic blood pressure. Even though they were admitted to the hospital without it, some have contracted sepsis during their stay while others did not. Researchers used their algorithm detection versus the standard methods applied at sepsis onset at 24 hours and 48 hours prior.
Even though sepsis affects at least 1.7 million adults mostly outside of the hospital settings, nearly 270,000 die. Researchers are hoping that the algorithm would allow clinicians to interfere prior to the condition becoming deadly.

SOURCE
https://www.aiin.healthcare/topics/diagnostics/sepsis-diagnosis-machine-learning

Read Full Post »


AI App for People with Digestive Disorders

Reporter: Irina Robu, PhD

Artificial intelligence (AI) constitutes machine learning and deep learning, which allows computers to learn without being clearly programmed every step of the way. The basic principle decrees that AI is machine intelligence leading to the best outcome when given a problem. This sets up AI well for life science applications, which states that AI can be taught to differentiate cells, be used for higher quality imaging techniques, and analysis of genomic data.

Obviously, this type of technology which serves a function and removes the need for explicit programming. It is clear that digital therapeutics will have an essential role in treatment of individuals with gastrointestinal disorders such as IBS. Deep learning is a favorite among the AI facets in biology. The structure of deep learning has its roots in the structure of the human brain which connect to one another through which the data is passed. At each layer, some data is extracted. For example, in cells, one layer may analyze cell membrane, the next some organelle, and so on until the cell can be identified.

A Berlin-based startup,Cara Care uses AI to help people manage their chronic digestive problems and intends to spend the funding raised getting the app in the hands of gastrointestinal patients in the U.S. The company declares its app has already helped up to 400,000 people in Germany and the U.S. manage widespread GI conditions such as reflux, irritable or inflammatory bowel, food intolerances, Crohn’s disease and ulcerative colitis “with a 78.8% treatment success rate.” Cara Care will also use the funding to conduct research and expand collaborations with companies in the pharmaceutical, diagnostics and food-production industries.

SOURCE
https://www.aiin.healthcare/topics/connected-care/ai-app-digestive-disorders-raises-7m?utm_source=newsletter

Read Full Post »


The launch of SCAI – Interview with Gérard Biau, director of the Sorbonne Center for Artificial Intelligence (SCAI).

 

Reporter: Aviva Lev-Ari, PhD, RN

Why create a center dedicated to artificial intelligence at Sorbonne University?

Today, artificial intelligence (AI) is everywhere. It is already transforming society and changing our lives. In the context of increasing international competition, the French government launched an ambitious strategy, AI for Humanity, in March 2018 with the goal of propelling France among the leaders of AI. SCAI is fully committed to this program.

Thanks to Sorbonne University’s faculties of Letters, Medicine and Sciences & Engineering, with its partners of the Alliance, has brought together considerable strength in the fundamental aspects of the AI (in mathematics, in computer science, in robotics), its applications (in health, environment or artistic creation) and in digital humanities. In total, more than one hundred experts from many laboratories are directly involved in AI research.

The aim of SCAI is to unite these capabilities in a place where the disciplines can enrich each other, share their experiences and identify common issues to advance innovative projects.

Designed as an “AI house” in the heart of Paris, the center aims to motivate, organize and make visible multidisciplinary research in AI through the establishment of chairs of excellence, support and hospitality, interdisciplinary projects, concerted responses to calls for tenders, the creation of task forces, the setting up of doctoral programs and more.

Internationally recognized experts, such as Jim Kurose, professor emeritus at the University of Massachusetts and advisor to the US government on AI, will bring their expertise to define the strategic directions of the center.

What relationships does the research and training center intend to maintain with the industrial world?

The development of AI, which involves the deployment of technological objects such as the autonomous car, will not happen without the expertise and know-how from companies. It is therefore essential to involve our industrial partners today to focus on an application transformation of AI.

To achieve this goal, we wanted to establish Chairs of Excellence that will allow us to collaborate on innovative themes with the industrial world. A memorandum of understanding has been signed with Thales and Total and a partnership with Atos is being developed on the subject of precision medicine.

In addition, we have signed an agreement with the AP-HP which should enable the exchange of expertise, skills, the setting up of joint projects, the implementation of simplified access to the Health Data Warehouse, the mobility of researchers or students, and communication and awareness actions.

SCAI is also working with international transfer and industrial recovery centers and will be an integral part of Paris Parc, the future innovation park of Sorbonne University.

SCAI:

  • 4 research areas: mathematics-informatics-robotics, health-medicine, climate-environment-Universes and digital humanities
  • More than 100 scientists, 150 doctoral and post-doctoral researchers, 300 students
  • More than 20 industrial partners (from start-ups to large international groups)
  • 700 m² in the heart of Paris
  • 1 satellite center on the Abu Dhabi campus of Sorbonne University

SOURCE

http://www.sorbonne-universite.fr/en/newsroom/actualites/launch-scai

Read Full Post »


Tweets, Pictures and Retweets at 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, MIT by @pharma_BI and @AVIVA1950 for #KIsymposium PharmaceuticalIntelligence.com and Social Media

 

Pictures taken in Real Time

 

Notification from Twitter.com on June 14, 2019 and in the 24 hours following the symposium

 

     liked your Tweet

    3 hours ago

  1.  Retweeted your Tweet

    5 hours ago

    1 other Retweet

  2.  liked your Tweets

    11 hours ago

    2 other likes

     and  liked a Tweet you were mentioned in

    11 hours ago

     liked your reply

    12 hours ago

  3. Replying to 

    It was an incredibly touching and “metzamrer” surprise to meet you at MIT

  4.  liked your Tweets

    13 hours ago

    3 other likes

     liked your reply

    15 hours ago

    Amazing event @avivregev @reginabarzilay 2pharma_BI Breakthrough in

     and  liked a Tweet you were mentioned in

    17 hours ago

  5. ‘s machine learning tool characterizes proteins, which are biomarkers of disease development and progression. Scientists can know more about their relationship to specific diseases and can interview earlier and precisely. ,

  6. learning and are undergoing dramatic changes and hold great promise for cancer research, diagnostics, and therapeutics. @KIinstitute by

     liked your Tweet

    Jun 16

     Retweeted your Retweet

    Jun 16

     liked your Retweet

    Jun 15

     Retweeted your Tweet

    Jun 15

     Retweeted your Tweet

    Jun 15

     Retweeted your Retweet

    Jun 15

     and 3 others liked your reply

    Jun 15

     and  Retweeted your Tweet

    Jun 14

     and  liked your Tweet

    Jun 14

     and  Retweeted your Tweet

    Jun 14

     liked your Tweet

    Jun 14

  7.  liked your Tweets

    Jun 14

    2 other likes

     liked your Tweet

    Jun 14

     Retweeted your Retweet

    Jun 14

     liked your Tweet

    Jun 14

     and  Retweeted your Tweet

    Jun 14

     liked your Tweet

    Jun 14

     liked your Tweet

    Jun 14

  8. identification in the will depend on highly

  9.  liked your Tweets

    Jun 14

    2 other likes

     Retweeted your Tweet

    Jun 14

     liked your Tweet

    Jun 14

     and 3 others liked your reply

    Jun 14

     liked your Retweet

    Jun 14

  10. this needed to be done a long time ago

     Retweeted your Tweet

    Jun 14

     and  Retweeted your reply

    Jun 14

     liked your Tweet

    Jun 14

     liked your reply

    Jun 14

     Retweeted your reply

    Jun 14

 

Tweets by @pharma_BI and by @AVIVA1950

&

Retweets and replies by @pharma_BI and @AVIVA1950

eProceedings 18th Symposium 2019 covered in Amazing event, Keynote best talks @avivregev ’er @reginabarzelay

  1. Top lectures by @reginabarzilay @avivaregev

  2. eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA via

  1.   Retweeted

    eProceedings 18th Symposium 2019 covered in Amazing event, Keynote best talks @avivregev ’er @reginabarzelay

  2. Top lectures by @reginabarzilay @avivaregev

  3. eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA via

  4. eProceedings & eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA via

  5.   Retweeted
  6.   Retweeted

    Einstein, Curie, Bohr, Planck, Heisenberg, Schrödinger… was this the greatest meeting of minds, ever? Some of the world’s most notable physicists participated in the 1927 Solvay Conference. In fact, 17 of the 29 scientists attending were or became Laureates.

  7.   Retweeted

    identification in the will depend on highly

  8. eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, Cambridge, MA via

 

Read Full Post »

Older Posts »