Nan-Wei Gong and figur8 are spurring the growth of wearable technology within the sports medicine and digital health sectors, where they aim to commercialize digitized 3-D body movement technologies.
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In this talk, I will present an overview of my research in the past decade on large scale optimization for machine learning and collective behavior in networked,natural, engineering, and social systems. These collective phenomena include social aggregation phenomena as well as emergence of consensus, swarming, and synchronization in complex network of interacting dynamic systems such as mobile robots and sensors. A common underlying theme in this line of study is to understand how a desired global behavior can emerge from purely local interactions. The evolution of these ideas into social systems has lead to development of a new theory of collective decision making among people and organizations. Examples include participation decisions in uprisings, social cascades, investment decisions in public goods, and decision making in large organizations. I will investigate distributed strategies for information aggregation, social learning and detection problems in networked systems where heterogeneous agents with different observations (with varying quality and precision) coordinate to learn a true state (e.g., finding aggregate statistics or detecting faults and failure modes in spatially distributed wireless sensor networks, or deciding suitability of a political candidate, quality of a product, and forming opinions on social issues of the day in social networks) using a stream of private observations and interaction with neighboring agents. I will end the talk with a a new vision for research and graduate education at the interface of information and decision systems, data science and social sciences.
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.
2016 MIT Digital Health Conference