The Data Pulse

Prescription digital therapeutics are emerging as an entirely new therapeutic modality. Corey McCann, President and CEO of Pear Therapeutics, chats with me about what they are, how they evolve as more data are collected, and the subsequent changes that are made to the treatment paradigms of neurological and psychological disorders. We also discuss what it took to get FDA approval for "software as a therapy," what skills are essential to succeeding in the space, and the growing presence of remote and asynchronous care. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

What does an individual's digital presence reveal about imminent infectious disease outbreaks, obesity prevalence, and the spread of medical misinformation? How can Google Maps images reveal socioeconomic factors that contribute to disparities in health? When and how does community context matter? Elaine Nsoesie, Professor at Boston University School of Public Health, shares how she has led data-driven efforts to address these questions in the context of various diseases and communities around the world. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Molecular diagnostics are emerging as a precise way to detect diseases early on in prognosis. In today's episode, I chat with Harvard+MIT PhD Student Ava Soleimany on the role of data science in activity-based molecular diagnostics for early cancer detection and how the confluence of classification techniques with feature representation of cancer biomarkers can enable non-invasive, early detection. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Climate change has been noted as perhaps the greatest public health crisis of our times--Professor Francesca Dominici of Harvard University agrees. We discuss her data-driven efforts to demonstrate the negative health impacts from air pollutants, particularly for resource-poor communities, and how the current pandemic has further elucidated these disparities. She shares the steps necessary to move from academic science to changing policy, as well as her work in bridging the gender representation gap in STEM. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Deep learning has shown tremendous advances in making predictions from imaging data. Today I talk with Andy Beck, Co-Founder and CEO of PathAI, about deep learning specifically applied to pathology to both diagnose and lead to treatment options for patients, as well as the global implications that deploying such a technology could have. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

In today's conversation, Gaurav Singal, who is the outgoing Chief Data Officer of Foundation Medicine, discusses the advent of molecularly-driven oncology in changing the paradigm of cancer diagnosis and treatment through precision medicine, the importance of using observational data, and the unpredictable nature of one's career that may yield a convergence of what were once disparate fields. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Gene therapies have emerged as a promising new modality for curing genetically-defined diseases; however, the naturally occuring variation remains limited. I chat with Sam Sinai and Jeff Gerold, Co-Founder/Lead ML Scientist and Head of Data Science, respectively, of Dyno Therapeutics about the role machine learning can play in better identifying and designing gene therapy vectors for a suite of traditional bottlenecks in the gene therapy development workflow, as well as the importance of building an environment where cross-talk between different domains is seamless. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Deep learning can be applied to tasks that involve a breadth of data types. I talk with Kyle Swanson, currently a Marshall Scholar at Cambridge University and previously at MIT, about his projects with Regina Barzilay on predicting breast cancer from mammograms, with performance on par with radiologists, and on designing antibiotics in a high-throughput manner using a new class of neural networks, identifying a compound that traditional chemists may have overlooked. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Humans tend towards bias, but are our algorithms objective? Today I discuss fairness and bias in machine learning for healthcare with Professor Maryzeh Ghassemi of the University of Toronto. We delve into the ways in which bias pops up in the data that are used to train computational models, the particular dangers of systemic inequalities in healthcare being perpetuated by algorithms, and some of the steps needed to combat these deeply rooted issues. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Today I speak with Vineeta Agarwala, General Partner at Andreessen Horowitz and a physician at Stanford. She shares the importance of capturing time-course, evolving data on patients that range from the molecular to the clinical levels ("deep clinical annotation"), as well as key ingredients of a successful tech-biotech hybrid startup. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary