Advances in digital technology have transformed fundamental understandings of health today. From the emergence of mobile computing, wearables, and the quantified self to the rise of genomics, personalized medicine, and ever-expanding collections of patient and population data, disruptions to traditional models of service, management, and research have created opportunities for innovation in every facet of the modern health industry. The 2016 MIT Digital Health Conference will present leading-edge developments with an emphasis on analytics and translation toward commercialization, exploring strategies for leveraging rapidly evolving technologies to optimize outcomes for individuals, companies, and society.
Karl Koster is the Executive Director of MIT Corporate Relations. MIT Corporate Relations includes the MIT Industrial Liaison Program and MIT Startup Exchange.
In that capacity, Koster and his staff work with the leadership of MIT and senior corporate executives to design and implement strategies for fostering corporate partnerships with the Institute. Koster and his team have also worked to identify and design a number of major international programs for MIT, which have been characterized by the establishment of strong, programmatic linkages among universities, industry, and governments. Most recently these efforts have been extended to engage the surrounding innovation ecosystem, including its vibrant startup and small company community, into MIT's global corporate and university networks.
Koster is also the Director of Alliance Management in the Office of Strategic Alliances and Technology Transfer (OSATT). OSATT was launched in Fall 2019 as part of a plan to reinvent MIT’s research administration infrastructure. OSATT develops agreements that facilitate MIT projects, programs and consortia with industrial, nonprofit, and international sponsors, partners and collaborators.
He is past chairman of the University-Industry Demonstration Partnership (UIDP), an organization that seeks to enhance the value of collaborative partnerships between universities and corporations.
He graduated from Brown University with a BA in geology and economics, and received an MS from MIT Sloan School of Management. Prior to returning to MIT, Koster worked as a management consultant in Europe, Latin America, and the United States on projects for private and public sector organizations.
Sheryl Greenberg initiates and promotes the interactions and development of relationships between academic and industrial entities to facilitate the transfer of new ideas and technologies between MIT and companies, and has created numerous successful partnerships. By understanding the business, technology, and commercial problems within a company, and understanding the technologies and expertise of MIT researchers, Greenberg identifies appropriate resources and expertise to foster new technology applications and collaborative opportunities.
Prior to MIT, Greenberg created and directed the Office of Technology Transfer at Brandeis University. In the process of managing intellectual property protection, marketing, and licensing, she has promoted the successful commercialization of technologies as diverse as new chemicals and manufacturing, biotechnology, food compositions, software, and medical devices. She facilitated the founding and funding of new companies, as well as creating a profitable technology transfer program. She also facilitated the patenting, marketing, and licensing of Massachusetts General Hospital technologies. In addition to her cellular, biochemical, and genetic research experience in academic and corporate environments, she has also created intellectual property for medical uses. Greenberg has been an independent intellectual property and business development consultant, is a U.S. Patent Agent, and has previously served the Juvenile Diabetes Research Foundation as Co-Chair of the Islet Research Program Advisory Committee and grant reviewer. She currently also mentors startup companies and facilitates partnering them with large life science and healthcare companies.
John Roberts has been Executive Director of MIT Corporate Relations (Interim) since February 2022. He obtained his Ph.D. in organic chemistry at MIT and returned to the university after a 20-year career in the pharmaceutical industry, joining the MIT Industrial Liaison Program (ILP) in 2013. Prior to his return, John worked at small, medium, and large companies, holding positions that allowed him to exploit his passions in synthetic chemistry, project leadership, and alliance management while growing his responsibilities for managing others, ultimately as a department head. As a program director at MIT, John built a portfolio of ILP member companies, mostly in the pharmaceutical industry and headquartered in Japan, connecting them to engagement opportunities in the MIT community. Soon after returning to MIT, John began to lead a group of program directors with a combined portfolio of 60-80 global companies. In his current role, John oversees MIT Corporate Relations which houses ILP and MIT Startup Exchange.
Program Director, MIT Corporate Relations Industrial Liaison Program
Erik Vogan joined the Office of Corporate Relations on June 1, 2015.
Erik brings to the Office of Corporate Relations numerous years of experience in big data and analytics, business development and partnering, and research and technology development, particularly in the areas of biotechnology and life sciences. Prior to joining the Office of Corporate Relations, Erik worked as a consultant to Boston-area venture capital and biotechnology companies and was a cofounder of Krypton Immuno-oncology.
At Beryllium Discovery Corporation, Erik was Vice President of Drug Discovery, leading functions in Business Development and Research. At Permeon Biologics, Erik founded the research laboratory and served as Director, Protein Sciences. Prior to that, Erik held positions as Head of Structural Biology at Acceleron Pharma and Senior Scientist at Wyeth Research.
Erik earned his B.S. in Genetics at the University of California, Davis and his Ph.D. in Biochemistry at Brandeis University working with Gregory Petsko, followed by postdoctoral work in the laboratory of Stephen C. Harrison at Harvard Medical School and Children's Hospital, Boston. Erik recently completed his MBA at MIT’s Sloan School of Management.
He has numerous patents, publications, and presentations to his credit.
Digital disruption may occur differently in health than in other industries due to complex & layered incentive structures. With this in mind, we’ll present some thoughts on how digital healthcare firms may successfully catalyze change in the US healthcare markets. First and foremost, entrepreneurs should apply technology to real, extant problems in healthcare, rather than develop technologies and search for applications. Second, founders should be sure their management team possesses deep knowledge about the specific problem that the company seeks to tackle from day 1. Third, in developing business strategy and company structure, entrepreneurs may consider that healthcare clients typically prefer services to self-serve technologies, including both enterprise and consumers. Finally, entrepreneurs should choose their investors wisely, since backers can offer valuable resources beyond financing, and few investors have deep expertise in healthcare and technology.
Aaron is a General Partner at dRx Capital, the joint investment company of Novartis and Qualcomm.
Before dRx, Aaron has worked on technology strategy for clinical research within Novartis. This work has led him to conclude that mobile, cloud, and Health IT technologies provide transformative opportunities in clinical research and healthcare industries more broadly. Aaron has led projects building upon these themes across multiple Business Units within Novartis, often by collaborations or investments in early-stage partner companies.
Previously, Aaron led laboratory teams at Novartis, investigating the biological bases for drug toxicity. Aaron studied medicine at Tufts University, cell and microbiology at the University of Pennsylvania and the Karolinska Institute, and completed his undergraduate studies at Cornell University.
While building wearables to measure emotional stress, we learned that deep brain activation during seizures could show up as a change in electrical signals measured on the wrist. This unexpected finding led us to develop a wristband, “Embrace” that today is worn to alert to neurological events that might be potentially life-threatening. This talk will tell the story of Empatica’s development of a product that wins design prizes for its appearance, looks like a cool consumer timepiece, and yet is collecting clinical quality data and running analytics based on sophisticated machine learning to advance personalized health.
Professor of Media Arts and Sciences, MIT Media Lab Director, Affective Computing Research, MIT Media Lab Faculty Chair, MIT Mind+Hand+Heart Co-founder, Empatica, Inc. Co-founder Affectiva, Inc.
Rosalind Picard, Sc.D., is a scientist, inventor, entrepreneur, author, professor and engineer. She is best-known for her book, Affective Computing, which proposed and described how to give skills of emotional intelligence to computers -- including voice assistants, robots, agents, and many kinds of interactive technologies. While trying to create ways to objectively measure data related to emotion, she pioneered wearable technologies to monitor and analyze physiological data in daily life, giving rise to new research and inventions at the intersection of wearables, physiology, and physical and mental health.
Picard is a named inventor on over a hundred patents, with impact that earned her recognition as both a member of the National Academy of Engineering and as a Fellow of the National Academy of Inventors. Her contributions include wearable and non-contact sensors, algorithms, and systems for sensing, recognizing, and responding respectfully to human affective information. Her inventions have applications in autism, epilepsy, depression, PTSD, sleep, stress, dementia, autonomic nervous system disorders, human and machine learning, health behavior change, market research, customer service, and human-computer interaction, and are in use by thousands of research teams worldwide as well as in many products and services.
She is founder and director of the MIT Media Lab’s Affective Computing Research Group, where she teaches and mentors students in research. Her research and engineering contributions have been recognized internationally, also with election as a fellow to the IEEE, the ACM, the AAAC and the APA. Picard is the recipient of the 2022 International Lombardy Prize for Computer Science Research, which is described by many as the “Nobel prize in computer science". The Lombardy prize includes an award of a million euros, which Picard donated to research.
Picard has co-founded two successful businesses, Empatica providing FDA-cleared biomarkers, a platform for clinical trial data collection from wearables, and the first FDA-cleared smartwatch to detect seizures, and Affectiva, providing Emotion-AI technologies (now part of Smart Eye, AB). She serves on the Board of Directors of Empatica.
Picard interacts regularly with industry and has consulted for many companies including Apple, AT&T, BT, Harman, HP, i.Robot, Merck, Motorola, and Samsung. Her group's achievements have been featured in forums for the general public such as The New York Times, The London Independent, National Public Radio, Scientific American Frontiers, ABC's Nightline and World News Tonight, Time, Vogue, Wired, Forbes, Voice of America Radio, New Scientist, and BBC programs such as "Hard Talk" and BBC Horizon with Michael Mosley.
Matteo Lai is the co-founder and CEO of Empatica, a company focused on wearable computing for the medical space, based in Cambridge, MA and Milan, Italy. Matteo studied Engineering and trained as an Architect, holds a Double MSc degree in Architecture and a Master in Innovation Management.
Despite billions invested in cancer research, our understanding of the disease, treatment, and prevention remains limited. Natural language processing (NLP) can offer new insights by mining the rich but underutilized information encoded in physicians’ observations and clinical findings, which are still primarily recorded as free-form text. NLP-based models can make a difference in clinical practice by improving models of disease progression, preventing over-treatment, and narrowing down on a cure.
Professor of Computer Science and Engineering Microsoft Faculty Fellow MIT Department of Electrical Engineering and Computer Science
Regina Barzilay is a Professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her main research area is natural language processing. Her current interests include statistical text generation, discourse modeling, paraphrasing, summarization and multi-modal information access. From October 2002 through June 2003, Regina was a postdoctoral associate in the Cornell Natural Language Processing Group. She received her Ph.D. in Computer Science from Columbia University in 2003. Regina received her M.S. in 1998 and B.A. in 1992, both from Ben-Gurion University, Israel.
Charles Fracchia, PhD, CEO & co-founder, Biobright Marilyn Matz, CEO & co-founder, Paradigm4
Trond heads up the Startup Initiative at MIT’s Industrial Liaison Program (ILP), facilitating productive relationships between industry and MIT’s startup ecosystem. He is a former Senior Lecturer at the MIT Sloan School of Management. Trond is a serial entrepreneur with Scandinavian roots, and is currently the Founder of Yegii, Inc., the insight network, and Managing Director of Tautec Consulting.
Trond is a leading expert on technology development across industries such as IT, Energy, and Healthcare. His knowledge spans entrepreneurship, strategy frameworks, policy making, action learning, virtual teamwork, knowledge management, standardization, and e-government. He wrote the book Leadership From Below (2008). Trond speaks six languages and is a frequent public speaker on business, technology, and wine.
Trond was a Strategy/business development executive at Oracle Corp. (2008-12), and a policy maker in the EU (2004-8) where he built the ePractice.eu web platform with 120,000 members. He has worked with multinational companies, with mid-caps and startups in Brazil, China, Colombia, France, Indonesia, Norway, the UK, and the US. He has a PhD in Multidisciplinary Technology Studies from the Norwegian University of Science and Technology.
Receptor Tyrosine Kinases (RTKs) are critical for normal human physiology, but can be oncogenic when highly expressed or mutated in a wide array of human cancers. To define the critical components in these networks, we have developed mass spectrometry based methods enabling the absolute quantification of tyrosine phosphorylation sites in RTK signaling networks at high temporal resolution following stimulation with different ligands or inhibitors, in vitro and in vivo. Quantitative phosphorylation data generated in this analysis provides insight into the occupancy of multiple tyrosine phosphorylation sites on the receptor, highlights mechanisms of differential regulation in response to different ligands, and highlights resistance mechanisms to selected inhibitors.
Professor of Biological Engineering MIT Department of Biological Engineering
Forest White is an expert in phosphoproteomics, the measurement of how proteins are phosphorylated in cells and tissues. Protein phosphorylation is a key mode of energy transport that affects cellular signaling and is key in all cellular processes. He holds a doctorate in analytical chemistry from Florida State University, and worked as a post-doctoral associate in proteomics and immunology with Don Hunt at University of Virginia. Before coming to MIT, White spent 2-1/2 years developing phosphoproteomics methods and equipment for MDS Proteomics.
His research will focus on global mapping of protein phosphorylation events in the cell. His intent is to catalogue novel phosphorylation sites, and determine what they do, and to combine all this information into methods to compare quantitatively the phosphorylation states of cells over a time course. This research will allow researchers to watch signal transduction cascades firing off over time, enabling comparisons between, for example, normal and cancerous cells, or normal and diabetic tissues. The method could study processes like apoptosis (cell death) as well. By seeing the different signal transduction cascades in cells, new drug targets could be identified, and by studying the alteration in the cascades in treated cells, new ways to modulate cell signaling could be studied to develop improved drugs for a wide range of diseases.
Thus this research has promise as a means to improve new drug discovery and preclinical evaluation of drug metabolism and toxicology.
White's group will house the skills of proteomics, analytical chemistry (primarily mass spectroscopy), molecular and cell biology, medicinal chemistry, bioinformatics and statistics. He is also co-supervising a student working with Profs. Wittrup and Lauffenburger on EGF receptor signaling research, a related topic. He will be an active contributor to CSBi's research.
The ability to create increasingly complex genomic data generated directly from patient tumors may impact our understanding of cancer and affect clinical decisions about cancer treatment. As the quantity of genomic data generated from individual cancer patients greatly expands, innovations will be needed to successfully implement large-scale genomics at the point-of-care. These include new ways to 1) interpret large-scale data from individual patients and 2) understand why patients respond (or don't respond) to existing and emerging cancer therapies such as targeted therapies, chemotherapies, and immunotherapies. Dr. Van Allen will explore how the emerging discipline of clinical computational oncology is powering new approaches for the clinical interpretation of large-scale genomic data and how these data are helping physicians understand why certain patients benefit from cancer therapies when others do not. While still in its infancy, this new field of clinical computational oncology may drive the widespread implementation of precision cancer medicine in the years to come.
Dr. Van Allen is an Assistant Professor of Medicine at Harvard Medical School, a clinician at Dana-Farber/Partners Cancer Care, and an Associate Member at the Broad Institute of MIT and Harvard. His research focuses on computational cancer genomics, the application of new technologies such as massively parallel sequencing to precision cancer medicine, and resistance to targeted therapeutics. As both a computational biologist and medical oncologist, he has specific expertise in clinical computational oncology and the development of algorithms to analyze and interpret genomic data for clinically focused questions. Overall, his research will make important contributions to the field of precision cancer medicine and resistance to targeted therapeutics via expertise and study in translational and clinical bioinformatics.
Originally from Los Angeles, CA, he studied Symbolic Systems at Stanford University, obtained his M.D. from UCLA, and completed a residency in internal medicine at UCSF before coming to Boston and completing a medical oncology fellowship at the Dana-Farber/Partners Cancer Care program.
ILP members, many of them Fortune 1000 companies, increasingly want to meet with MIT startups, to scout, to discuss, to partner, to invest, and more. Responding to that need, ILP’s Startup Initiative will boost our current database of near 1000 MIT startups. Going forward, the intent is to provide a web platform to gather real time developments, advertise opportunities and do more but also better matching. We are currently seeking feedback from the wider MIT innovation ecosystem on how we should proceed. There will be a stand at the Startup Exhibit where we can take questions and you can give your input. We're looking for input from both MIT startups and ILP members.
Cardiovascular disease remains the leading cause of death in the industrialized world. Although research into the etiology and treatment of cardiac disease remains a focus of numerous research groups, the accurate identification of patients who are at risk of adverse events following a heart attack remains a major challenge in clinical cardiology. In this talk I will describe how sophisticated computational biomarkers, which integrate a diverse array of clinical information, can be used to identify patients who are at elevated risk of death after a cardiac event. This work demonstrates that computational biomarkers can provide useful and powerful insights that can help guide clinical decision making.
Professor of Electrical Engineering and Computer Science Professor of Health Sciences and Technology MIT Department of Electrical Engineering and Computer Science
Professor Collin M. Stultz is a principal investigator in the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT). Professor Stultz conducts research to understand conformational changes in macromolecules and the effect of structural transitions on common human diseases. His research group employs an interdisciplinary approach that utilizes techniques drawn from computational chemistry, signal processing, and basic biochemistry.
Professor Stutlz received the AB from Harvard College in 1988, and the MD from Harvard Medical School as well as the PhD in Biophysics from MIT in 1997. An alumnus of the Harvard-MIT program in Health Sciences and Technology (HST), Professor Stultz is on the faculty of both HST and MIT’s Department of Electrical Engineering and Computer Science. He is a member of the American Society for Biochemistry and Molecular Biology and the Federation of American Societies for Experimental Biology. Among his honors are being a recipient of the Burroughs Wellcome Fund Career Award in Biomedical Sciences and the James Tolbert Shipley Prize.
Henry Ellis Warren (1894) professor of Electrical Engineering and Computer Science MIT Department of Electrical Engineering and Computer Science
Polina Golland is a Henry Ellis Warren (1894) professor of Electrical Engineering and Computer Science at MIT and a principal investigator at MIT CSAIL. Her primary research interest is in developing novel techniques for medical image analysis and understanding. With her students, Golland has demonstrated novel approaches to image segmentation, shape analysis, functional image analysis and population studies. She has served as an associate editor of the IEEE Transactions on Medical Imaging and of the IEEE Transactions on Pattern Analysis. Golland is currently on the editorial board of the Journal of Medical Image Analysis. She is a Fellow of the International Society for Medical Image Computing and Computer Assisted Interventions.
I will review our work in extracting clinically relevant characterizations of anatomy and pathology from medical images in two domains. First, joint modeling of image, genetic and clinical data is used to gain insight into the patterns of disease in large heterogeneous clinical populations. Examples include studies of white matter disease in stroke patients from brain MRI, of genetically defined patterns of emphysema in COPD patients as observed in chest CT, and others. The second family of applications aims to provide accurate delineations of pathology and make predictions in medical scans of individual patients. Examples include functional imaging of the placenta and cardiac image analysis for surgical planning.
Farbod Hagigi, PhD, MPH, CEO & founder, ClinicalBox, Inc. Andrew Braunstein, MS, CEO, ClinLogica, Inc. Ming-Zher Poh, PhD, CEO & co-founder, Cardiio
As our understanding of health has improved, we now realize that our long-term health is rooted in our human behavior. The largest burden of diseases, including diabetes, cardiometabolic syndrome, obesity, and substance abuse, are often the accumulated result of many small decisions that we make throughout our daily lives, such as what we eat, what time we sleep or wake, what route we take to work, and what social habits we follow.
From this perspective, it is important to create technology that can not only diagnose disease, but rather prevent disease by helping to promote healthy behaviors. Just as we use a GPS guidance system when we travel on a journey, our group at MIT develops technologies and systems that can be used by people as personal navigation aids for their behavior, which we informally call “GPS for the brain”. Such systems will comprise a wide range of technologies that already exist in the so-called “Internet of Things (IoT),” such as phones, TV’s, lights, refrigerators and other home appliances.
Wearable sensors have a valuable role to play in these future health systems; however, since most of the world’s population may never use wearable sensors (for many reasons), there is also a practical need to deploy non-contact methods of monitoring our physiology and behavior (such as smart cameras, microwave radars, and even olfactory sensors) embedded into our everyday environment. While much of this sensor technology has already been developed in recent decades, there remains a great deal of work over the next decade in creating computer models and algorithms that can better understand, predict, and motivate human behavior.
Research Scientist, MIT D-Lab Assistant Professor, UMass Medical School, Department of Psychiatry
Rich Fletcher is currently a research scientist at MIT D-Lab and an assistant professor at University of Massachusetts Medical School department of Psychiatry. Dr. Fletcher directs the Mobile Technology Group within the MIT D-Lab (www.mobiletechnologylab.com) which develops a variety of mobile sensors, analytic tools, and diagnostic algorithms to study problems in global health and behavior medicine. Dr. Fletcher has worked for over 20 years in the field of wireless sensors and RFID, including 5 years with the US Air Force and 15 years at the MIT Media Lab, producing over a dozen US patents and several spin-off companies. In the field of medicine, Rich Fletcher currently leads several research studies funded by NIH, the Bill and Melinda Gates Foundation, USAID, Vodafone and the Tata Trust. Bridging together the fields of engineering and medicine, Dr. Fletcher’s research utilizes a variety of mobile technologies and wearable sensors for use in behavior monitoring as well as psychological and behavioral interventions. In 2009, Dr. Fletcher patented one of the first mHealth “just in time intervention” (JITAI) systems, and is currently part of several clinical studies with partner hospitals in the US and India studying various aspect of behavior change in the context of health. Dr. Fletcher has roots in Colombia (South America) and is also an MIT alumnus, with degrees in Physics, Electrical Engineering and Information Technology from MIT.
For a little over a dozen years, our group has been developing, integrating, and testing various bihormonal (insulin and glucagon) bionic pancreas technologies for autonomous regulation of glycemia in people with type 1 diabetes (T1D). The technology has evolved over the years from a crude and clumsy system of interconnected pumps and sensors cobbled together around a laptop computer, to a system that runs on an iPhone, which wireless communicates with two infusion pumps and a sensor, and, finally, to its ultimate embodiment as a dual-chamber infusion pump, a sensor, and mathematical algorithms all housed within a single compact integrated device, which we call the iLet (in homage to the pancreatic islets of Langerhans which contain the alpha and beta cells that secrete glucagon and insulin).
The laptop version of our bionic pancreas was tested first in a diabetic swine model of T1D at Boston University (BU) between 2005 and 2009 and then in inpatient clinical trials with our collaborators at the Massachusetts General Hospital (MGH) between 2008 and 2012 in adults and adolescents with T1D. Between 2013 and 2016 we conducted outpatient clinical trials of the iPhone version of our bionic pancreas together with our clinical collaborators at MGH, Stanford, the University of North Carolina, and the University of Massachusetts. Results of these studies will be presented along with our plans for the final pivotal trials of the iLet and the pathway ahead for regulatory approval.
Edward Damiano is a Professor of Biomedical Engineering at Boston University. His expertise and training are in the areas of mechanical and biomedical engineering, applied mechanics, and applied mathematics. His basic scientific research has combined fluid dynamics with intravital microscopy to study blood flow in the microcirculation and to elucidate mechanisms by which the lining of blood vessels determines vascular health and disease. In particular, his lab has focused on the endothelial glycocalyx, which consists of a complex mucopolysaccharide and macromolecular assembly that is situated at the interface between the luminal vascular wall and flowing blood. Beyond his basic science research is his interest in translational research.
Ever since his 17-year-old son, David, developed type 1 diabetes (T1D) as an infant, he has been committed to creating and integrating blood-glucose control technologies with a vision of building a dual-hormonal (insulin and glucagon) bionic pancreas that his son could take to college. This work began over a dozen years ago with the design and development of mathematical dosing algorithms for blood-glucose control. He and his group ran those dosing algorithms on a laptop computer back then, and began testing them in experiments in diabetic swine in 2005. Working with his clinical collaborators at the Massachusetts General Hospital, they progressed through in-patient clinical trials in adults and adolescents with T1D from 2008–2012. From 2013–2016, his team at BU and his clinical collaborators conducted six outpatient clinical trials in adults and children 6 years and older with T1D testing a mobile version of their bionic pancreas, which integrated an iPhone with their mathematical dosing algorithms, two infusion pumps, and a continuous glucose monitor. With $2.5MM in donations from over 1,000 gifts from the T1D community, his engineering team, along with their industrial collaborators in the medical device industry, have recently built the first fully integrated biohormonal bionic pancreas that does not rely upon smartphone technology. They call their device the iLet, in homage to the pancreatic islets of Langerhans. They recently received FDA approval to begin clinical trails testing the iLet in the outpatient setting. His goal is to begin the final pivotal trial testing the iLet in 2017.
Daisy Zhuo is a cofounding partner of Interpretable AI. She has extensive experience developing business solutions using advanced predictive and prescriptive analytics and AI systems in a variety of industries, including healthcare, banking, insurance, and information technology. She holds a PhD in operations research from MIT, during which she developed a range of cutting-edge machine learning techniques such as Optimal Imputation and Robust Classifications, with publications in top academic journals.
Nataly Youssef is the President and Chief Analytics Officer at MyA Health, a personalized healthcare decision support application, as well as the Head of Healthcare Analytics at P2 Analytics. Her expertise lies in the area of prescriptive analytics, namely the use of robust optimization to manage risk and maximize outcomes. She participated in developing a MOOC -The Analytics Edge - and taught executive MBAs at the Massachusetts Institute of Technology (MIT). Nataly received her Ph.D. in Operations Research and Analytics from MIT and her M.S. in Industrial Engineering and Risk Management from Texas A&M University.
We present an example of ongoing research in the space of analytics-driven personalized healthcare and showcase an example of a healthcare technology startup spun off of our research endeavor.
The first part of the talk discusses an ongoing research work on personalized diabetes management. Current clinical guidelines for managing type 2 diabetes do not differentiate based on patient-specific factors. We present a data-driven approach for personalized diabetes management that improves health outcomes relative to the standard of care. We modeled outcomes under thirteen pharmacological therapies based on electronic medical records from 1999 to 2014 for 10,806 type 2 diabetes patients from Boston Medical Center. We developed a recommendation algorithm that prescribes a regimen if the expected improvement from switching regimens exceeds a threshold. For patient visits in which the algorithmic recommendation differed from the standard of care, the mean post-treatment glycated hemoglobin (HbA1c) under the algorithm was lower than standard of care by 0.44% +/- 0.03% (p << 001), from 8.37% under the standard of care to 7.93% under our algorithm. A personalized approach to diabetes management yielded substantial improvements in HbA1c outcomes relative to the standard of care. Our prototyped dashboard visualizing the recommendation algorithm can be used by providers to inform diabetes care and improve outcomes.
The second part of the talk presents an overview of MyA Health, a spinoff based on similar research efforts aimed at personalizing health care down to the individual. MyA is powered by a wealth of data sources encompassing historical claims, electronic medical records, wellness and biometric data, wearable device records, and consumer lifestyle data. The backend of MyA is empowered by a high-dimensional analytics engine with: (1) a suite of predictive machine learning algorithms to predict future healthcare costs, disease progression and outcome variability; and (2) robust optimization algorithms to optimize and personalize healthcare decisions that will best mitigate an individual’s financial burden and maximize their healthcare outcomes. To the consumer, MyA is an individual’s healthcare advisor that personalizes decisions ranging from what health plan is best to cover their risk to what drug/treatment is likely to benefit them the most. MyA is unique in that it takes the totality of data sources available to make personalized recommendations, a concept that is made possible given the healthcare data digitization revolution and the increasing adoption of wearable wellness and health monitoring devices.
Principal Research Scientist MIT Laboratory for Information and Decision Systems
Kalyan is a principal research scientist in the Laboratory for Information and Decision Systems (LIDS, MIT). Previously he was a research scientist at CSAIL (CSAIL, MIT). His primary research interests are in machine learning and building large scale statistical models that enable discovery from large amounts of data. His research is at the intersection of big data, machine learning, and data science. He directs a research group called Data to AI in the new MIT Institute for Data Systems and Society (IDSS). The group is interested in big data science and machine learning, and is focused on how to solve foundational issues preventing artificial intelligence and machine learning solutions from reaching their full potential for societal applications.
This talk is focused on the methods and technologies to answer the question ‘Why does it take a long time to process, analyze and derive insights from the data?’ Dr. Veeramachaneni is leading the ‘Human Data Interaction’ Project to develop methods that are at the intersection of data science, machine learning, and large scale interactive systems. With significant achievements in storage , processing, retrieval, and analytics, the answer to this question now lies in developing technologies that are based on intricately understanding the complexities in how scientists, researchers, analysts interact with data to analyze, interpret, and derive models from it. In this talk, Dr. Veeramachaneni will present how his team is building systems to transform this interaction for the signals domain using an example of physiological signals. Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months.
In this talk, he will describe a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining predictive models from these waveforms. BeatDB radically shrinks the time an investigation takes by: (a) supporting fast, flexible investigations by offering a multi-level parameterization, (b) allowing the user to define the condition to predict, the features, and many other investigation parameters (c) pre-computing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates.
Andy Vidan, CEO & co-founder, Composable Analytics Laila Zemrani, CEO & co-founder, Fitnescity Christine Hsieh, PhD, CEO & founder, Salubris Analytics
John Moore, MD, PhD, is the co-founder and CEO of Twine Health. Moore’s passion for a better healthcare system started during his medical training where he was frustrated to learn that the best diagnostic and treatment capabilities did not result in healthier and engaged people. To be successful, Moore realized patients had to be in control of their own care, but also recognized the clear need for expert support. Moore came up with the idea for Twine Health during six years at the MIT Media Lab where he studied the healthcare delivery model and created a revolutionary approach to care: technology-supported apprenticeship. Bringing together advances in health psychology, learning science and human-computer interaction, Twine is designed to become the primary tool for teamwork between patients and clinicians. Before attending medical school, Moore received a BS in Biomedical engineering, and was a Fulbright Scholar.
For the past decade technologists have been on a mission to disrupt medicine the same way they’ve disrupted practically every other societal system - from the bottom up, by the consumers of healthcare. This would entail replacing much of what doctors do by AI and big data in the cloud and, ultimately, the “Uber-ization of Healthcare”. The result would magically be lower costs and better outcomes.
But my recent work in the digital health space has shown that we technologists can’t approach medicine the same way we’ve approached media and music. Healthcare is a different beast. Anyone who thinks that apps and data alone are going to convince people to change their health-related behaviors - which is the only way to lower costs and improve outcomes at scale - is simply ignoring human nature.
Twine Health was founded on the belief that the real opportunity for technology in healthcare is to strengthen, not weaken, critical human-to-human relationships in the system. We have developed a Collaborative Healthcare IT platform, based on six years of research at the MIT Media Lab, with that principle in mind. We have demonstrated that when patients with chronic conditions like hypertension and diabetes are empowered to take the lead in their health, but with the continuous support and caring of their clinical team, costs drop dramatically and outcomes are greatly improved.
Leo Anthony Celi has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. As clinical research director and principal research scientist at MIT Laboratory of Computational Physiology (LCP), he brings together clinicians and data scientists to support research using data routinely collected in the intensive care unit (ICU). His group built and maintains the Medical Information Mart for Intensive Care (MIMIC) database. This public-access database has been meticulously de-identified and is freely shared online with the research community. It is an unparalleled research resource; over 2000 investigators from more than 30 countries have free access to the clinical data under a data use agreement. In 2016, LCP partnered with Philips eICU Research Institute to host the eICU database with more than 2 million ICU patients admitted across the United States. The goal is to scale the database globally and build an international collaborative research community around health data analytics.
Leo founded and co-directs Sana, a cross-disciplinary organization based at the Institute for Medical Engineering and Science at MIT, whose objective is to leverage information technology to improve health outcomes in low- and middle-income countries. At its core is an open-source mobile tele-health platform that allows for capture, transmission, and archiving of complex medical data (e.g. images, videos, physiologic signals such as ECG, EEG and oto-acoustic emission responses), in addition to patient demographic and clinical information. Sana is the inaugural recipient of both the mHealth (Mobile Health) Alliance Award from the United Nations Foundation and the Wireless Innovation Award from the Vodafone Foundation in 2010. The software has since been implemented around the globe including India, Kenya, Lebanon, Haiti, Mongolia, Uganda, Brazil, Ethiopia, Argentina, and South Africa.
He is one of the course directors for HST.936—global health informatics to improve quality of care, and HST.953—secondary analysis of electronic health records, both at MIT. He is an editor of the textbook for each course, both released under an open access license. The textbook Secondary Analysis of Electronic Health Records came out in October 2016 and was downloaded over 48,000 times in the first two months of publication. The course “Global Health Informatics to Improve Quality of Care” was launched under MITx in February 2017.
Leo was featured as a designer in the Smithsonian Museum National Design Triennial “Why Design Now?” held at the Cooper-Hewitt Museum in New York City in 2010 for his work in global health informatics. He was also selected as one of 12 external reviewers for the National Academy of Medicine 2014 report “Investing in Global Health Systems: Sustaining gains, transforming lives.”
The Agency for Healthcare Research and Quality was established in 1989 in response to an Institute of Medicine report that pointed out ?escalating healthcare costs, wide variations in medical practice patterns, and evidence that some health services are of little or no value?. More than 25 years later, there has been surprisingly little progress in these three areas. The interest in applying machine learning to clinical practice is increasing yet the practical application of these techniques has been less than desirable. There is a persistent gap between the clinicians required to understand the context of the data and the engineers who are critical to extracting useable information from the increasing amount of healthcare data that is being generated.
Principal Research Scientist Leader, Evolutionary Design and Optimization Group Director, The Alfa Group: Any Scale Learning for All MIT Computer Science and Artificial Intelligence Laboratory
Una-May O'Reilly is a Fellow (elected in 2004) of the International Society of Genetic and Evolutionary Computation, now ACM SIGEVO. She is a recipient of the EvoStar Award for Outstanding Achievements in Evolutionary Computation in Europe. She holds a B.Sc. from the University of Calgary, and a M.C.S. and Ph.D. (1995) from Carleton University, Ottawa, Canada. She joined MIT's Artificial Intelligence Laboratory in 1996.
Currently a principal research scientist at CSAIL, Una-May is founder and co-leader of the AnyScale Learning For All (ALFA) group at CSAIL, MIT (Computer Science and Artificial Intelligence Laboratory). ALFA focuses on scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics. The group has projects in clinical medicine knowledge discovery, wind energy and MOOC technology.
Una-May serves as Vice-Chair of ACM SIGEVO. She served as chair of the largest international Evolutionary Computation Conference, GECCO, in 2005. She has served on the GECCO business committee, co-led the 2006 and 2009 Genetic Programming: Theory to Practice Workshops and co-chaired EuroGP, the largest conference devoted to Genetic Programming. In 2013 she inaugurated the Women in Evolutionary Computation group at GECCO.
Una-May is the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), and editor for Evolutionary Computation (MIT Press), and action editor for the Journal of Machine Learning Research.
Una-May holds multiple patents (some pending): One is for a genetic algorithm technique applicable to internet-based name suggestions. It was brought to practice in Nymbler.com. Another, brought to practice in the DELPHI system, is for a dataset-size invariant approach to accelerating machine learning model search. A third is for a copula-based wind energy assessment method that supports advanced software for analyzing, valuing, and financing renewable energy projects. Two others, associated with the STEALTH project, relate to taxable income measurement and extraction, as well as the assessment of tax audit likelihood. The methods can identify effective, economically-valid strategies that simultaneously reduce or minimize tax liability and the likelihood of being audited. Reciprocally, they can identify effective tax auditing policies to help facilitate the efficient allocation of limited auditing resources.
Dr. O’Reilly holds advisory roles with Evervest, Aspiring Minds, PatternEx and is a co-founder of ProvidentiaTax.
Can data series from a broad patient population be relevant and reliable tools in predicting individual outcomes when compared to personal wellness sensor data? Or, simply put from a patient perspective, “Can what happen to them, happen to me?” Retrieving and making use of “like-me” signal data based on similarity presents challenges far beyond digital marketing’s effectiveness in making targeted book and movie recommendations. By investigating and understanding those unique challenges, our research group has developed an approach based upon locality sensitive hashing (LSH). We will provide an update on our progress towards adapting LSH for fast and accurate Signal Like-Me capability.