Trustworthy Clinical Decision Support Systems for Medical Trajectories

Author: ORCID icon
Lamp, Josephine, Computer Science - School of Engineering and Applied Science, University of Virginia
Feng, Lu, EN-Comp Science Dept, University of Virginia
Evans, David, EN-Comp Science Dept, University of Virginia

The explosion of medical sensors and wearable devices has resulted in the collection of large amounts of medical trajectories. Medical trajectories are time series that provide a nuanced look into patient conditions and their changes over time, allowing for a more fine-grained understanding of patient health. It is difficult for clinicians and patients to effectively make use of such high dimensional data, especially given the fact that there may be years or even decades worth of data per patient. Clinical Decision Support Systems (CDSS) provide summarized, filtered, and timely information to patients or clinicians to help inform medical decision-making processes. Although CDSS have shown promise for data sources such as tabular and imaging data, e.g., in electronic health records, the opportunities of CDSS using medical trajectories have not yet been realized due to challenges surrounding data use, model trust and interpretability, and privacy and legal concerns.

This dissertation develops novel machine learning frameworks for trustworthy CDSS using medical trajectories. We define trustworthiness in terms of three desiderata: (1) robust—providing reliable outputs from the CDSS even when inputs are variable, irregular or missing; (2) explainable—providing understandable, actionable explanations for CDSS predictions to clinicians or patients; and (3) privacy-preserving—providing CDSS that use data without violating patients’ privacy expectations. We develop interpretable machine learning frameworks that are robust to missing, irregular, variable and conflicting trajectories that directly address data and model challenges. Moreover, we develop privacy-preserving learning methodologies that allow for the safe sharing and aggregation of medical trajectories and directly address privacy challenges. We evaluate our frameworks across a wide selection of benchmarks and show that our techniques can learn valuable insights from trajectory data with high accuracy and strong privacy guarantees.

PHD (Doctor of Philosophy)
Machine Learning, Privacy, Clinical Decision Support Systems, Diabetes, Heart Failure, Medical Trajectories, Time Series
Issued Date: