Machine Learning
The Clinical Excellence Research Center studies applications of machine learning to electronic health record data and to administrative claims data for improving clinical care. These efforts use machine learning to provide forecasts, such as patients likely to incur high medical costs in the future, and patients at high risk of mortality as well as fundamental methods that enable the making of such forecasts via models that are stable of time, sites, and populations.
Clinical Excellence Research Center's faculty study the interplay of the AI model’s output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action determines the clinical usefulness of model-guided care.
Publications and News
- – arXiv.org
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
CERC researchers propose a framework for rigorously evaluating the performance of a model in the context of the subsequent actions it triggers is necessary to identify models that are clinically useful.
- – NEJM Catalyst
Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems
Based on research inspired by the framework of Machine Learning Models in a clinical setting, Stanford Health Care has developed a mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan.
- – Nature Digital Medicine
Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
Machine learning (ML) algorithms present an opportunity to develop improved and more generalizable risk prediction models. By leveraging large-scale data—from electronic health records (EHRs), for instance—such algorithms can determine combinations of variables that reliably predict an outcome.
- – PubMed
Improving palliative care with deep learning
Automatic screening and notification saves the palliative care team the burden of time consuming chart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients ...