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.
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.
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.
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.