Understanding the brain could lead to new kinds of computational algorithms and artificial intelligences, as well as treatments for intractable disorders that affect over a billion people worldwide. However, the brain is a very complex, densely wired circuit, and understanding how it works has remained elusive. In order to map how these circuits are organized, and control their complex dynamics, we are building new tools, which include methods for physically expanding brain circuits so that we can see their building blocks, as well as molecules that make neural circuits controllable by light. Through these tools we aim to enable the systematic analysis and repair of the brain.
From flexible hybrid electronics, to integrated photonic devices, to functional fibers to smart manufacturing, the new U.S. Federal government institutes are advancing new manufacturing technologies and accelerating the pace of manufacturing innovation. This session will review MIT's involvement in these institutes and discuss opportunities for industry partners to participate in developing new products and capabilities.
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.
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
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.
Charles Fracchia, PhD, CEO & co-founder, Biobright Marilyn Matz, CEO & co-founder, Paradigm4
Farbod Hagigi, PhD, MPH, CEO & founder, ClinicalBox, Inc. Andrew Braunstein, MS, CEO, ClinLogica, Inc. Ming-Zher Poh, PhD, CEO & co-founder, Cardiio
Andy Vidan, CEO & co-founder, Composable Analytics Laila Zemrani, CEO & co-founder, Fitnescity Christine Hsieh, PhD, CEO & founder, Salubris Analytics
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.
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.