Suchi Saria

Saria is from Darjeeling.[6] She earned her Bachelor's degree at Mount Holyoke College.[7] She was awarded a full scholarship from Microsoft. In 2004 she joined Stanford University as a Rambus Corporation Fellow.[7] She earned her Master of Science and Doctor of Philosophy[8] degrees at Stanford University, supervised by Daphne Koller and advised by Anna Asher Penn and Sebastian Thrun. At Stanford University, Saria developed a model that could predict preemie outcomes with a 90% accuracy.[9] The model used data from monitors, birth weight and length of time spent in the womb to predict whether a preemie would develop an illness.[10] The output value PhyiScore could be used to reduce the $26 billion per year spent by US health care on preterm birth.[11] She worked in the startup Aster Data Systems.[12]

Saria believes that big data can be used to personalise healthcare.[13][14] She is considered an expert in computational statistics and their applications to the real world.[7] She uses Bayesian and probabilistic modelling.[6] In 2014 Saria was funded by a $1.5 million Gordon and Betty Moore Foundation project that looked to make intensive care units safer.[15] The project used data collected at patients' bedsides along with noninvasive 3D sensors that monitor care in patient's hospital rooms.[16] The sensors collect information on steps that might have been missed by doctors; like washing hands.[16]

Saria uses big data to manage chronic diseases.[17] She is part of a National Science Foundation (NSF) award that looks at scleroderma. She uses machine learning to analyse medical records and identify similar patterns of disease progression.[17] The system works out which treatments have been effectively used for various symptoms to aid doctors in choosing treatment plans for specific patients.[17] She has developed another algorithm that can be used to predict and treat Septic shock.[18] The algorithm used 16,000 items of patient health records and generates a targeted real-time warning (TREWS) score.[19] She collaborated with David N. Hager to use the algorithm in clinics, and it was correct 86% of the time. Saria modified the algorithm to avoid missing high risk patients- for example, those who have suffered from septic shock previously and who have sought successful treatment.[20] She was described by XRDS magazine as being a Pioneer in transforming healthcare.[21] In 2016 Saria spoke at about using machine learning for medicine at TEDxBoston.[22] The talk has been viewed over 100,170 times.[23]