Modeling Individual and Population traits from Clinical Temporal Data
Physiological data are routinely recorded in intensive care, but their use for rapid assessment of illness severity has been limited. The data is high-dimensional, noisy, and changes rapidly; moreover, small changes that occur in a patient's physiology over long periods of time are difficult to detect, yet can lead to catastrophic outcomes. A physician’s ability to recognize complex patterns across these high-dimensional measurements is limited. We propose a nonparametric Bayesian method for discovering informative representations in such continuous time series that aids both exploratory data analysis and feature construction. When applied to data from premature infants in the neonatal ICU (NICU), our model obtains novel clinical insights. Based on these insights, we devised the Physiscore, a novel risk prediction score that combines patterns from continuous physiological signals to predict infants at risk for developing major complications in the NICU. Using only 3 hours of non-invasive data from birth, Physiscore very successfully predicts morbidity in preterm infants. Physiscore performed consistently better than other neonatal scoring systems, including the Apgar, which is the current standard of care, and SNAP, a machine learning based score that requires multiple invasive tests. This work was published on the cover of Science Translational Medicine (Science's new journal aimed at translational medicine work), and was covered by numerous press sources.
Suchi Saria received her PhD'11 in Computer Science with a machine learning and clinical informatics focus from Stanford University with Daphne Koller as her mentor. Saria is an Assistant Professor at Johns Hopkins University in the departments of Computer Science within the school of Engineering and Health Policy within the Bloomberg school of Public Health. She is also visiting Harvard Medical School as an NSF Computing Innovation fellow this year. She has won various awards including, a Best Student Paper and a Best Student Paper Finalist award, the Rambus Fellowship, the Microsoft full scholarship and the National Science Foundation Computing Innovation Fellowship. Her research interests lie in developing novel machine learning and data-driven solutions for improving health care delivery both at the point of care and for analysis by policy makers. Her thesis work has been featured in national and international press outlets including CBS Radio, France's national newspaper Le Monde and NIH's Medline Plus.