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.
2016 MIT Digital Health Conference
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.
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.
Panel Discussion on Corporate Agility and Situational Awareness
Tomer Srulevich Chief Business Officer, Endor