CAREER:Real-Time Nonparametric Machine Learning for Healthcare with Guarantees

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This NSF CAREER project is on developing new real-time machine learning models for healthcare. A key emphasis is on predicting durations at the individual patient level such as, time until death, time until recovering from a disease, and hospital length of stay. The models developed will not only take advantage of recent advances in deep learning but will also come with rigorous accuracy guarantees that say when, why, and how well they work. These models are "nonparametric" in that they are guaranteed to work under very few assumptions on the underlying data. Moreover, these models will be used to assist medical transport and clinical exam room scheduling systems that rely on predictions of how long patients spend in different stages of their clinical visits. This research has the potential to make patient care more personalized and to improve the efficiency of hospitals' resource use. In conducting this research, much of the work is in not only bringing together machine learning and healthcare communities, but also providing these communities with educational resources (workshops, tutorials, and a new book) that will better shape the mathematical and statistical foundations of the relatively new field of study at the intersection of machine learning and healthcare. To grow the community at this intersection, the PI plans to teach a new course on machine learning for healthcare and continue mentoring students at both undergraduate and graduate levels. Lessons learned will inform the PI's outreach efforts at the high school and college levels that bring computer science and probability concepts to a diverse audience.Despite numerous machine learning methodological advances in recent years, few machine learning models are deployed in clinical settings. The few that are, tend to be many-decades-old or capitalize on deep learning success stories in imaging applications. However, many healthcare prediction tasks remain challenging, where state-of-the-art models (deep-learning-based or not) struggle to produce accurate predictions. For example, many survival analysis problems (predicting time-to-event outcomes such as time until death, time until hospital discharge, etc) are hard. To complicate matters, in many such problems, collecting data for training or validation on real patients could be costly and require long-term studies (e.g., for predicting time until death for cancer patients, these durations could be on the time scale of years). To help practitioners decide on which models to use, reliability assurances in the form of statistical guarantees on prediction models would be extremely valuable. Moreover, for these models to assist in time-sensitive high-stakes decisions, they must scale to real-time clinical data streams. This project aims to develop a family of real-time machine learning models for healthcare that not only take advantage of state-of-the-art machine learning advances in deep neural networks and tree ensembles, but also come with rigorous accuracy and uncertainty guarantees. These guarantees hold even though the models do not make parametric assumptions on the distribution of the data. Moreover, the models developed will be tested in clinical settings for scheduling hospital resource use with the ultimate goal of deployment in hospital systems. A heavy emphasis is on survival analysis problems, which are extremely common in healthcare but less well-known within the machine learning community. The proposed research tightly integrates with an education plan of teaching key concepts at the intersection of machine learning and healthcare. This includes making statistical guarantees of machine learning algorithms more accessible and usable by practitioners through workshops and tutorials, introducing survival analysis to a general machine learning audience with a new book, and teaching students at the undergraduate and graduate level fundamental concepts of machine learning for healthcare by developing a new course.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.