Technology can be used to assist physicians in diagnosis, decision-making, and treatment personalization, as well as help individuals make informed decisions and stick to their plans.
Technology affects health outcomes in several ways. Patient diagnosis can be improved using artificial intelligence. Information systems can be used to help physicians track information, access research, and remember to adhere to protocols ranging from treatment decisions to handwashing. Individual treatment plans can be personalized. Digital decision tools can help guide individuals to make more informed decisions about their own health. Technology can be used to help remind and nudge patients to take drugs, exercise, or adhere to treatment regimes.
Read about some of the health projects that the lab is currently working on.
Stable Learning Establishes Some Common Ground between Causal Inference and Machine Learning
Observational studies of the efficacy of medical treatments are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We develop a framework for using unstructured clinical text based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to cancer datasets from the Stanford Cancer Institute. The uncovered terms can also be interpreted by oncologists for clinical insights. The method can enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.
Evaluating the Benefits of Commonly Prescribed Drugs to Prevent Poor Outcomes from Respiratory Distress
Applying recently developed machine learning and causal inference methods to historical health claims data allows the evaluation of the impact of certain medications on outcomes for patients hospitalized with respiratory conditions. The methods incorporate new techniques to estimate causal effects across distinct, proprietary data sources without merging the datasets. The results can be used to suggest candidates for clinical trials in the fight against COVID-19.
Accelerating Health Technology Innovation to Address COVID-19
Accelerating the development of health technology such as treatments, diagnostics, and vaccines is crucial to ending the COVID-19 crisis as quickly as possible. This project evaluates alternative methods for speeding up the process, including the design of incentives for development and manufacturing, as well as approaches for optimizing clinical trials using shared control groups and adaptive experiments.
Adaptive Experiments to Help Patients Make Informed Choices About Contraception
Lab researchers are working with Yaounde Gynecology, Obstetrics and Pediatrics Hospital in Cameroon to help women make informed choices about contraceptives. Adaptive experiments identify effective and efficient strategies, including price subsidies, for providing information through a tablet application. The experiment accelerates learning in an environment of many potential alternatives and improves expected patient outcomes by allocating more and more patients to the tablet design that works best.
Learn firsthand from health researchers and practitioners in interviews conducted by the lab.