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

Digital Therapeutics: A threat or opportunity to pharmaceuticals


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

 

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

 

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

 

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

 

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

 

References:

 

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

 

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

 

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

 

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

 

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

 

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  • World Medical Innovation Forum, Partners Innovations, ARTIFICIAL INTELLIGENCE | APRIL 8–10, 2019 | Westin, BOSTON

https://worldmedicalinnovation.org/agenda/

Aviva Lev-Ari, PhD, RN Founder, LPBI Group

will cover this event in Real Time

@AVIVA1950

@pharma_BI

 

 

Monday, April 8, 2019

7:00 am – 8:00 am
7:00 am – 5:00 pm
8:00 am – 9:40 am
Bayer Ballroom

First Look

Nine rapid fire presentations on the applications of AI in Clinical Care

Moderator: Giles Boland, MD
  • Chair, Department of Radiology, BWH; Philip H. Cook Professor of Radiology, HMS
Moderator: Trung Do
  • VP, Business Development, Innovation, PHS
9:40 am – 9:55 am
9:55 am – 11:35 am
Bayer Ballroom

First Look

Nine rapid fire presentations on the applications of AI in Clinical Care

11:45 am – 1:00 pm

Discovery Café Sessions

Lunch with Top Leading Experts: Intensive sessions addressing cutting-edge artificial intelligence topics.

Applying AI to Save Lives During the Opioid Crisis

The U.S. is in the throes of a devastating epidemic of opioid addiction and overdose — some 130 people die in this country every day from opioids, says the National Institute on Drug Abuse. With a total economic cost of more than $78 billion a year, academic and industry organizations are harnessing AI to develop new tools that can help alleviate this national crisis. This session will discuss some of the AI-based strategies that academic and industry teams are leveraging to help clinical and public health officials better predict, identify, and treat opioid addiction, as well as some of the concerns around data privacy.

Moderator: Thomas Sequist, MD, Chief Quality & Safety Officer, PHS

Bob Burgin, CEO, Amplifire Healthcare Alliance

Carm Huntress, CEO, RxRevu Inc

Sarah Wakeman, MD, Medical Director, Substance Use Disorder Initiative, MGH; Assistant Professor, Medicine, HMS

Scott Weiner, MD, Director, Brigham Comprehensive Opioid Response and Education (B-CORE) Program, BWH; Assistant Professor, HMS

 

Community Hospitals: Key Component in Healthcare Transformation

Community hospitals are the largest sources of patient care in the U.S. As such, they represent a critical frontier in the transformation of health care. How are these organizations using AI and digital technologies to drive transformation? What are the key distinctions from academic medical centers? This session will address these and other critical topics that impact community hospitals and their essential, though often overlooked, role in health care.

Moderator: Michael Jaff, DO, President, NWH, PHS, Professor of Medicine, HMS

Fabien Beckers, PhD, CEO, Arterys

Joanna Geisinger, CEO, TORq Interface

Lee Schwamm, MD, Director, Center for TeleHealth and Exec Vice Chair, Neurology, MGH; Professor, Neurology, HMS

Tal Wenderow, CEO, Beyond Verbal

 

Digital Management of Diabetes

Across the full spectrum of patient care, the management of diabetes has been flooded with new technology and treatment options for both type 1 and type 2 diabetes – there is a range of new devices and software, including automatic insulin infusion systems, glucose sensors, AI-based algorithms and decision support tools, with artificial pancreas on the horizon. This session will focus on these areas as well as clinical use cases that highlight the value of AI.

Moderator: Deborah Wexler, MD, Clinical Director, Diabetes Center, MGH; Associate Professor, HMS

Marie McDonnell, MD, Section Chief and Director, Diabetes Program, BWH; Lecturer, HMS

Joshua Riff, MD, CEO, Onduo

 

Emergency Medicine

 

Mental Health and the Promise of AI

Moderator: Sabine Wilhelm, PhD, Chief of Psychology; Director, OCD and Related Disorders Program, MGH; Professor, Psychology, HMS

Thomas McCoy, MD, Director of Research, Center for Quantitative Health, MGH; Assistant Professor, Psychiatry & Medicine, HMS

Christopher Molaro, CEO, Neuroflow

David Silbersweig, MD, Chairman, Department of Psychiatry, BWH; Stanley Cobb Professor of Psychiatry, HMS

 

From Startup to Impact (Pharma and Diagnostics)

With all the hype surrounding AI, this session will focus on what really matters. Impact! Who is really moving the needle in life sciences today? This session will introduce you to five leading companies who will share their client stories over lunch.

Moderator: James Brink, MD, Radiologist-in-Chief, MGH; Juan M. Taveras Professor of Radiology, HMS

1:00 pm – 1:15 pm
1:15 pm – 1:45 pm
Bayer Ballroom
1:45 pm – 2:35 pm
Bayer Ballroom

AMC AI Strategy: AI from the Top

  • Board Member, PHS; President Emerita and Professor of Neuroscience, MIT
  • Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS
  • Chief Academic Officer, PHS; Laurie Carrol Guthart Professor of Medicine, Academic Dean for Partners, HMS; 2019 Forum Co-Chair
  • Chief Clinical Officer, PHS; Professor of Medicine, HMS; 2019 Forum Co-Chair
2:35 pm – 3:25 pm
Bayer Ballroom

RWE and Trial Optimization in the AI Era

Moderator: Thomas Lynch, MD
  • EVP and CSO, R&D, Bristol-Myers Squibb
  • CMO, CSO, SVP Oncology, Flatiron Health
  • EVP MA&PV and Bayer CMO, Bayer AG
  • Chief Architect, Microsoft Healthcare
  • CEO, My Own Med Inc.
  • Executive Director, Clinical Trials Office, PHS; Associate Professor of Medicine, HMS
3:25 pm – 4:15 pm
Bayer Ballroom

AI Driven Value-Based Care

Moderator: Timothy Ferris, MD
  • CEO, MGPO; Professor of Medicine, HMS
  • CEO, American Heart Association
  • EVP, President, Network Solutions Change Healthcare
  • Vice Chairman, Investment Banking and Managing Director Lazard Freres
  • CEO, NHS England
4:15 pm – 5:05 pm
Bayer Ballroom

Cardiovascular Care: Reinvented Through AI

  • Vice Chair for Scientific Innovation, Department of Medicine, BWH; Associate Professor of Medicine, HMS
  • President, Bayer Pharma Americas Region, Bayer
  • Director, Cardiac Imaging MR PET CT Program, MGH; Professor, Medicine, HMS
5:15 pm – 6:15 pm

Tuesday, April 9, 2019

7:00 am – 8:00 am
7:00 am – 5:00 pm
7:50 am – 8:00 am
Bayer Ballroom

Opening Remarks

  • Chief Innovation Officer, PHS; President, Partners HealthCare International
8:00 am – 8:50 am
Bayer Ballroom

Implementing AI in Cancer Care

  • Associate Surgeon, BWH; Richard E. Wilson Professor of Surgery in the Field of Surgical Oncology, HMS
  • Chief, Breast Imaging Division, MGH; Professor of Radiology, HMS
  • President and Co-Founder, LunaDNA
  • Delta Electronics Professor, Electrical Engineering and Computer Science Department, MIT
  • Director, Cancer Genome Analysis, Broad Institute; Professor of Pathology, HMS
  • CEO, insitro
8:50 am – 9:40 am
Bayer Ballroom

Imagining Medicine in the Year 2054

In 1984 Isaac Asimov was asked to predict what life in 2019 would be like. Using the same aperture, we as what will health care look like 35 years from now? What capabilities will clinicians have that they now struggle with? And what will be the biggest challenges? Current trends suggest that we will see some significant gains in the areas of cancer immunotherapy, gene therapy for devastating rare diseases, and treatments for common neuropsychiatric conditions, including schizophrenia and depression. Panelists will draw on their visionary perspective and will reflect on what to expect and why.

Moderator: Keith Flaherty, MD
  • Director, Clinical Research, MGH; Professor of Medicine, HMS
  • Vice Chair for Scientific Innovation, Department of Medicine, BWH; Associate Professor of Medicine, HMS
  • Director, Cellular Immunotherapy Program, MGH; Assistant Professor, Medicine, HMS
  • Vice-Chair, Neurology, Director, Genetics and Aging Research Unit, MGH; Joseph P. and Rose F. Kennedy Professor of Neurology, HMS
  • CEO, Biogen
9:40 am – 10:10 am
10:10 am – 10:40 am
Bayer Ballroom
10:40 am – 11:30 am
Bayer Ballroom

CEO Roundtable

Moderator: Anne Klibanski, MD
  • Chief Academic Officer, PHS; Laurie Carrol Guthart Professor of Medicine, Academic Dean for Partners, HMS; 2019 Forum Co-Chair
  • EVP and Chief Commercial Officer, Bristol-Myers Squibb
  • CEO, Philips
  • EVP, Head, Pharmaceuticals Research & Development, Bayer AG
  • CEO, Siemens Healthineers
  • CEO, GE Healthcare
11:45 am – 1:00 pm

Discovery Cafe Sessions

Lunch with Top Leading Experts: Intensive sessions addressing cutting-edge artificial intelligence topics.

Back Office of the Provider Future

Moderator: Peter Markell, EVP, Administration and Finance, CFO and Treasurer, PHS

Kent Ivanoff, CEO, VisitPay

Connie Moser, Chief Operating Officer, Verge Health

 

Chief Digital Strategy Officer Roundtable

With the advent of healthcare AI-enabled technologies, this session brings together several chief digital health officers from a range of organizations. The discussion will address key tradeoffs in sequencing technology across academic medical centers; what technologies are being prioritized; and how consumer expectations are impacting the future delivery model of healthcare.

Moderator: Alistair Erskine, MD, Chief Digital Health Officer, PHS

Michael Anderes, Chief Innovation and Digital Health Officer, Froedtert Health; President, Inception Health

Adam Landman, MD, VP and CIO, Brigham Health; Associate Professor of Emergency Medicine, HMS

Aimee Quirk, CEO, innovationOchsner

Richard Zane, MD, Chief Innovation Officer, UCHealth; Professor and Chair,Department of Emergency Medicine, University of Colorado School of Medicine

 

Innovation Fellows: A New Model of Collaboration

The Innovation Fellows Program provides short-term, experiential career development opportunities for future leaders in health care focused on accelerating collaborative innovation between science and industry. It facilitates personnel exchanges between Harvard Medical School staff from Partners’ hospitals and participating biopharmaceutical, device, venture capital, digital health, payor and consulting firms. A successful example of open innovation, Fellows and Hosts learn from each other as they collaborate on projects ranging from clinical development to digital health & artificial intelligence to new care delivery models and industry disruption. Come listen to the experience and insights of our panelists, including Fellows, Industry Partners and hospital leadership, and learn how this new model of collaboration can deliver value and lead to broader relationships between industry and academia.

 

Last Mile: Fully Implementing AI in Healthcare

Moderator: Keith Dreyer, DO, PhD, Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS

Katherine Andriole, PhD, Director of Research Strategy and Operations, MGH & BWH CCDS; Associate Professor, Radiology, HMS

Samuel Aronson, Executive Director, IT, Personalized Medicine, PHS

Seth Hain, VP of R&D, Epic

Jonathan Teich, MD, PhD, Chief Medical Information Officer, InterSystems; Emergency Medicine, BWH

 

Reimagining Disease Management

Moderator: Sree Chaguturu, MD, Chief Population Health Officer, PHS; Assistant Professor, Medicine, HMS

Murray Brozinsky, Chief Strategy Officer, Conversa

Jean Drouin, MD, CEO, Clarify Health Solutions

Sandhya Rao, MD, Senior Medical Director, Population Health, PHS; Assistant Professor, Medicine, HMS

 

Standards and Regulation: The Emerging AI Framework

 

From Startup to Impact (Provider Solutions)

With all the hype surrounding AI, this session will focus on what really matters. Impact! Who is really moving the needle for healthcare providers today? This session will introduce you to five leading companies who will share their client stories over lunch.

Moderator: Meredith Fisher, PhD, Partner, Partners Innovation Fund, PHS

 

1:00 pm – 1:10 pm
1:10 pm – 2:00 pm
Bayer Ballroom

China: AI Enabled Healthcare Leadership

Moderator: James Bradner, MD
  • President, Novartis Institutes for Biomedical Research
  • CEO, Infervision
  • Managing Partner, Qiming Venture Partners
2:00 pm – 2:30 pm
Bayer Ballroom

1:1 Fireside Chat: Mark Benjamin, CEO, Nuance

Moderator: Peter Slavin, MD
  • President, MGH; Professor, Health Care Policy, HMS
  • CEO, Nuance Communications
2:30 pm – 3:00 pm
3:00 pm – 3:50 pm
Bayer Ballroom

Getting to the AI Investment Decision

  • VP, Venture & Managing Partner, Partners Innovation Fund, PHS
  • Managing Director, Bain Capital Life Sciences
  • Managing Partner, Polaris Partners
  • SVP, Strategy, Commercialization & Innovation, Amgen
  • Managing Director, Healthcare Group, Goldman Sachs
  • Partner, Google Ventures
3:50 pm – 4:20 pm
Bayer Ballroom
4:20 pm – 5:10 pm
Bayer Ballroom

Consumer Healthcare and New Models of Care Delivery

Moderator: Diana Nole
  • CEO, Wolters Kluwer Health
  • President, Global Strategy Group, Samsung; Founder, CareVisor
  • VP and Global CTO, Sales, Dell EMC
  • President, Health Platforms, Verily Life Sciences
  • VP and Chief Health Officer, IBM Corporation
  • SVP, Head of Innovation and Health Equity Microsoft Healthcare
5:15 pm – 6:15 pm

Wednesday, April 10, 2019

7:00 am – 12:00 pm
7:30 am – 9:30 am
Bayer Ballroom

Innovation Discovery Grant Awardee Presentations

Twelve clinical AI teams culled through the Innovation Discovery Grant program present their work illustrating how AI can be used to improve patient health and healthcare delivery. This session is designed for investors, entrepreneurs, investigators, and others who are interested in commercializing AI opportunities that are currently in development with support from the Innovation Office.

Moderator: David Louis, MD
  • Pathologist-in-Chief, MGH; Benjamin Castleman Professor of Pathology, HMS
9:30 am – 10:00 am
10:00 am – 10:30 am
Bayer Ballroom

1:1 Fireside Chat: Stefan Oelrich, Member of the Board of Management; President, Pharmaceutical, Bayer AG

Moderator: Betsy Nabel, MD
  • President, Brigham Health; Professor of Medicine, HMS
  • Member of the Board of Management, Bayer AG; President, Pharmaceutical, Bayer AG
10:30 am – 11:00 am
Bayer Ballroom

1:1 Fireside Chat: Deepak Chopra, MD, Founder, The Chopra Foundation

Moderator: Rudolph Tanzi, PhD
  • Vice-Chair, Neurology, Director, Genetics and Aging Research Unit, MGH; Joseph P. and Rose F. Kennedy Professor of Neurology, HMS
  • Founder, The Chopra Foundation
11:00 am – 11:50 am
Bayer Ballroom

Using AI to Predict and Monitor Human Performance and Neurological Disease

  • Chief of Neurology, Co-Director, Neurological Clinical Research Institute, MGH; Julieanne Dorn Professor of Neurology, HMS
  • Chief Scientist, Dolby Laboratories
  • Global Therapeutic Head, Neuroscience Janssen Research & Development
  • EVP and CMO, Biogen
  • CEO, Kitman Labs
11:50 am – 12:50 pm
Bayer Ballroom

Disruptive Dozen: 12 Technologies that will reinvent AI in the Next 12 Months

The culture of innovation throughout Partners HealthCare naturally fosters robust discussions about new “disruptive” technologies and which ones will have the biggest impact on health care. The Disruptive Dozen was created to identify and rank the technologies that Partners faculty feel will break through over the next decade to significantly improve health care. This year, the Disruptive Dozen focuses on relevant advances and opportunities in artificial intelligence (AI).

Moderator: Jeffrey Golden, MD
  • Chair, Department of Pathology, BWH; Ramzi S. Cotran Professor of Pathology, HMS
  • Associate Chief, Infection Control Unit, MGH; Assistant Professor, Medicine, HMS
1:00 pm – 1:10 pm
Bayer Ballroom

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R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018

Reporter: Aviva Lev-Ari, PhD, RN

 

Looking Back at Google’s Research Efforts in 2018

Tuesday, January 15, 2019
2018 was an exciting year for Google’s research teams, with our work advancing technology in many ways, including fundamental computer science research results and publications, the application of our research to emerging areas new to Google (such as healthcare and robotics), open source software contributions and strong collaborations with Google product teams, all aimed at providing useful tools and services. Below, we highlight just some of our efforts from 2018, and we look forward to what will come in the new year. For a more comprehensive look, please see our publications in 2018.
SOURCE & VIDEOS

Perspectives on AI

  • Ethical Principles and AI
  • AI for Social Good
  • Research Outreach
  • New Places, New Faces
  • Looking Forward to 2019

Theoretical Innovations in AI

  • Algorithms and Theory
  • Software Systems

R&D for Artificial Intelligence Application @Google in 2018 included the following Application Types:

  • Assistive Technology
  • Quantum computing
  • Natural Language Understanding
  • Perception
  • Computational Photography
  • AutoML
  • Tensor Processing Units (TPUs)
  • Open Source Software and Datasets
  • Robotics

Applications of AI to Other Fields

SOURCES

https://ai.googleblog.com/2019/01/looking-back-at-googles-research.html

Google Publication in 2018

 publications in 2018.

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Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?

Reporter: Stephen J. Williams, Ph.D.

 

Boston Healthcare sponsored a Webinar recently entitled ” Role of Informatics in Precision Medicine: Implications for Innovators”.  The webinar focused on the different informatic needs along the Oncology Care value chain from drug discovery through clinicians, C-suite executives and payers. The presentation, by Joseph Ferrara and Mark Girardi, discussed the specific informatics needs and deficiencies experienced by all players in oncology care and how innovators in this space could create value. The final part of the webinar discussed artificial intelligence and the role in cancer informatics.

 

Below is the mp4 video and audio for this webinar.  Notes on each of the slides with a few representative slides are also given below:

Please click below for the mp4 of the webinar:

 

 


  • worldwide oncology related care to increase by 40% in 2020
  • big movement to participatory care: moving decision making to the patient. Need for information
  • cost components focused on clinical action
  • use informatics before clinical stage might add value to cost chain

 

 

 

 

Key unmet needs from perspectives of different players in oncology care where informatics may help in decision making

 

 

 

  1.   Needs of Clinicians

– informatic needs for clinical enrollment

– informatic needs for obtaining drug access/newer therapies

2.  Needs of C-suite/health system executives

– informatic needs to help focus of quality of care

– informatic needs to determine health outcomes/metrics

3.  Needs of Payers

– informatic needs to determine quality metrics and managing costs

– informatics needs to form guidelines

– informatics needs to determine if biomarkers are used consistently and properly

– population level data analytics

 

 

 

 

 

 

 

 

 

 

 

 

What are the kind of value innovations that tech entrepreneurs need to create in this space? Two areas/problems need to be solved.

  • innovations in data depth and breadth
  • need to aggregate information to inform intervention

Different players in value chains have different data needs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Depth: Cumulative Understanding of disease

Data Depth: Cumulative number of oncology transactions

  • technology innovators rely on LEGACY businesses (those that already have technology) and these LEGACY businesses either have data breath or data depth BUT NOT BOTH; (IS THIS WHERE THE GREATEST VALUE CAN BE INNOVATED?)
  • NEED to provide ACTIONABLE as well as PHENOTYPIC/GENOTYPIC DATA
  • data depth more important in clinical setting as it drives solutions and cost effective interventions.  For example Foundation Medicine, who supplies genotypic/phenotypic data for patient samples supplies high data depth
  • technologies are moving to data support
  • evidence will need to be tied to umbrella value propositions
  • Informatic solutions will have to prove outcome benefit

 

 

 

 

 

How will Machine Learning be involved in the healthcare value chain?

  • increased emphasis on real time datasets – CONSTANT UPDATES NEED TO OCCUR. THIS IS NOT HAPPENING BUT VALUED BY MANY PLAYERS IN THIS SPACE
  • Interoperability of DATABASES Important!  Many Players in this space don’t understand the complexities integrating these datasets

Other Articles on this topic of healthcare informatics, value based oncology, and healthcare IT on this OPEN ACCESS JOURNAL include:

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Broad Institute launches Merkin Institute for Transformative Technologies in Healthcare

HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Paradoxical Findings in HealthCare Delivery and Outcomes: Economics in MEDICINE – Original Research by Anupam “Bapu” Jena, the Ruth L. Newhouse Associate Professor of Health Care Policy at HMS

Google & Digital Healthcare Technology

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

The Future of Precision Cancer Medicine, Inaugural Symposium, MIT Center for Precision Cancer Medicine, December 13, 2018, 8AM-6PM, 50 Memorial Drive, Cambridge, MA

Live Conference Coverage @Medcity Converge 2018 Philadelphia: Oncology Value Based Care and Patient Management

2016 BioIT World: Track 5 – April 5 – 7, 2016 Bioinformatics Computational Resources and Tools to Turn Big Data into Smart Data

The Need for an Informatics Solution in Translational Medicine

 

 

 

 

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Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

  1. Recent achievements in next-generation artificial intelligence
  2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
  3. Open source blockchain Exonium and its application for healthcare marketplace
  4. A blockchain-based platform allowing patients to have control of their data and manage access
  5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

  • Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
  • Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
  1. Prediction of drug properties(2, 3) and toxicities(4)
  2. Biomarker development (5)
  3. Cancer diagnosis (6)
  4. First FDA approved system based on deep learning Arterys Cardio DL
  • Other promising systems of deep learning include:
    • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
    • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
    • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
    • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

  • Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
  • Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
  • Blockchain uses a consensus algorithm even though end users may have their own privacy key
  • Applied cryptography measures and routines are used to decentralize authentication (publicly available)
  • Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

References

  1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
  2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
  3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
  4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
  5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
  6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
  7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
  8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

From VentureBeat.com
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

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Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address

Reporter: Stephen J. Williams, PhD

8:30 AM -9:15

Practical Applications of AI in Cancer

We are far from machine learning dictating clinical decision making, but AI has important niche applications in oncology. Hear from a panel of innovative startups and established life science players about how machine learning and AI can transform different aspects in healthcare, be it in patient recruitment, data analysis, drug discovery or care delivery.

Moderator: Ayan Bhattacharya, Advanced Analytics Specialist Leader, Deloitte Consulting LLP
Speakers:
Wout Brusselaers, CEO and Co-Founder, Deep 6 AI @woutbrusselaers ‏
Tufia Haddad, M.D., Chair of Breast Medical Oncology and Department of Oncology Chair of IT, Mayo Clinic
Carla Leibowitz, Head of Corporate Development, Arterys @carlaleibowitz
John Quackenbush, Ph.D., Professor and Director of the Center for Cancer Computational Biology, Dana-Farber Cancer Institute

Ayan: working at IBM and Thompon Rueters with structured datasets and having gone through his own cancer battle, he is now working in healthcare AI which has an unstructured dataset(s)

Carla: collecting medical images over the world, mainly tumor and calculating tumor volumetrics

Tufia: drug resistant breast cancer clinician but interested in AI and healthcareIT at Mayo

John: taking large scale datasets but a machine learning skeptic

moderator: how has imaging evolved?

Carla: ten times images but not ten times radiologists so stressed field needs help with image analysis; they have seen measuring lung tumor volumetrics as a therapeutic diagnostic has worked

moderator: how has AI affected patient recruitment?

Tufia: majority of patients are receiving great care but AI can offer profiles and determine which patients can benefit from tertiary care;

John: 1980 paper on no free lunch theorem; great enthusiasm about optimization algortihisms fell short in application; can extract great information from e.g. images

moderator: how is AI for healthcare delivery working at mayo?

Tufia: for every hour with patient two hours of data mining. for care delivery hope to use the systems to leverage the cognitive systems to do the data mining

John: problem with irreproducible research which makes a poor dataset:  also these care packages are based on population data not personalized datasets; challenges to AI is moving correlation to causation

Carla: algorithisms from on healthcare network is not good enough, Google tried and it failed

John: curation very important; good annotation is needed; needed to go in and develop, with curators, a systematic way to curate medial records; need standardization and reproducibility; applications in radiometrics can be different based on different data collection machines; developed a machine learning model site where investigators can compare models on a hub; also need to communicate with patients on healthcare information and quality information

Ayan: Australia and Canada has done the most concerning AI and lifescience, healthcare space; AI in most cases is cognitive learning: really two types of companies 1) the Microsofts, Googles, and 2) the startups that may be more pure AI

 

Final Notes: We are at a point where collecting massive amounts of healthcare related data is simple, rapid, and shareable.  However challenges exist in quality of datasets, proper curation and annotation, need for collaboration across all healthcare stakeholders including patients, and dissemination of useful and accurate information

 

9:15 AM–9:45 AM

Opening Keynote: Dr. Joshua Brody, Medical Oncologist, Mount Sinai Health System

The Promise and Hype of Immunotherapy

Immunotherapy is revolutionizing oncology care across various types of cancers, but it is also necessary to sort the hype from the reality. In his keynote, Dr. Brody will delve into the history of this new therapy mode and how it has transformed the treatment of lymphoma and other diseases. He will address the hype surrounding it, why so many still don’t respond to the treatment regimen and chart the way forward—one that can lead to more elegant immunotherapy combination paths and better outcomes for patients.

Speaker:
Joshua Brody, M.D., Assistant Professor, Mount Sinai School of Medicine @joshuabrodyMD

Director Lymphoma therapy at Mt. Sinai

  • lymphoma a cancer with high PD-L1 expression
  • hodgkin’s lymphoma best responder to PD1 therapy (nivolumab) but hepatic adverse effects
  • CAR-T (chimeric BCR and TCR); a long process which includes apheresis, selection CD3/CD28 cells; viral transfection of the chimeric; purification
  • complete remissions of B cell lymphomas (NCI trial) and long term remissions past 18 months
  • side effects like cytokine release (has been controlled); encephalopathy (he uses a hand writing test to see progression of adverse effect)

Vaccines

  •  teaching the immune cells as PD1 inhibition exhausting T cells so a vaccine boost could be an adjuvant to PD1 or checkpoint therapy
  • using Flt3L primed in-situ vaccine (using a Toll like receptor agonist can recruit the dendritic cells to the tumor and then activation of T cell response);  therefore vaccine does not need to be produced ex vivo; months after the vaccine the tumor still in remission
  • versus rituximab, which can target many healthy B cells this in-situ vaccine strategy is very specific for the tumorigenic B cells
  • HoWEVER they did see resistant tumor cells which did not overexpress PD-L1 but they did discover a novel checkpoint (cannot be disclosed at this point)

 

 

 

 

 

 

 

 

 

Please follow on Twitter using the following #hashtags and @pharma_BI

#MCConverge

#AI

#cancertreatment

#immunotherapy

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

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Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

 

Reporter: Aviva Lev-Ari, PhD, RN

 

BOSTON – May 10, 2018 Vyasa Analytics, a provider of deep learning software and analytics for life sciences and healthcare organizations, today announces three pre-built deep learning analytics modules for its Cortex software at Bio-IT World Conference & Expo. Cortex enables the secure, scalable application of deep learning-based artificial intelligence (AI) analytics to enterprise data, identifying patterns, relationships and concepts across disparate data sources.

 

The new Neural Concept Recognition, Image Analytics and ChemVector analytics modules in Cortex enable life sciences organizations to quickly and easily apply deep learning analytics to large data streams of text, images and chemical structures. Like all deep learning analytical modules in Cortex’s library, these new modules allow users to ask complex questions of their data and use the answers to gain critical insights.

 

“Life sciences and healthcare organizations are using AI tools to advance research and development and deliver better patient care. Deep learning algorithms provide a set of powerful approaches that help us apply analytics more effectively and comprehensively across large scale data sources,” said Dr. Christopher Bouton, founder and CEO of Vyasa. “The idea of AI has been around for decades, but we are now experiencing a perfect storm of GPU-based computing power, deep learning algorithm advances and highly scalable data sources that enables paradigm-shifting machine learning and analytics capabilities.”

 

Vyasa will be demoing three deep learning analytics modules for Cortex at Bio-IT World 2018 in Boston from May 15 to 17, including:

 

  • Neural Concept Recognition. This module can be trained on text concepts (e.g. drugs, diseases, pathways, conditions, side effects, genes) in structured and unstructured data. Users can ask Cortex complex questions across large scale data sets, and discover unexpected relationships between concept types. Concept recognition analytics is applicable to a wide range of use cases from competitive intelligence, to drug repurposing and EHR analytics.

 

  • Life Sciences R&D Specialized Image Analytics. Deep learning enables novel, powerful forms of image analytics, capable of being trained to detect patterns and objects in large scale image data sources. With just a few clicks in Cortex, the user can connect large streams of image data and apply analytics to those sources. Vyasa has finely-tuned this analysis for life sciences images, and it is ideal for cell assay screening, drug manufacturing and post-market screening for counterfeit packaging and tablets.
  • ChemVector de novo Compound Design. This proprietary Cortex module applies deep learning to chemical structures. Users can drag and drop one or more SDF files containing SMILES strings into Cortex, and Cortex can identify and generate novel compounds that optimize critical variables such as log-p, molecular weight and synthetic viability. ChemVector can be used with a range of other chemistry-specific analytical modules also available in Cortex.

 

 

Dr. Bouton, Vyasa’s founder and CEO, received his BA in Neuroscience (Magna Cum Laude) from Amherst College in 1996 and his Ph.D. in Molecular Neurobiology from Johns Hopkins University in 2001. Previously Dr. Bouton was the CEO of Entagen a software company founded in 2008 that provided innovative Big Data products including Extera and TripleMap. Entagen’s technologies won numerous awards including the “Innovative Technology of the Year Award for Big Data” from the Massachusetts Technology Leadership Council in 2012 and Entagen was recognized as a Gartner “Cool Vendor” in the Life Sciences in 2013. Entagen was acquired by Thomson Reuters in 2013. Dr. Bouton is an author on over a dozen scientific papers and book chapters and his work has been covered in a number of industry news articles.

 

Visit Vyasa and demo Cortex at booth #632, and watch the explainer video at www.vyasa.com.

About Vyasa Analytics

Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations. Cortex is Vyasa’s secure, highly scalable software platform for collaborative knowledge discovery and data analytics. Using Vyasa’s proprietary Neural Concept Recognition technology, Cortex identifies trends and patterns across disparate data sources, empowering project teams to gain insights and drive better decision making. Learn more at www.vyasa.com.

 

 

Angela Zmyslinski
Account Executive
azmyslinski@matternow.com
Office – 401-330-2800

     

SOURCE

From: Angela Zmyslinski <azmyslinski@matternow.com>

Date: Thursday, May 10, 2018 at 2:39 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: RE: Demo deep learning software for life sciences at Bio-IT World 2018

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