BETTER MODELS FOR HIGH-STAKES TASKS

The intersection of machine learning and healthcare has the potential to transform medical diagnosis, treatment, and research. Machine learning models can analyze vast amounts of medical data and identify patterns that may be too complex for human analysis. However, one of the major challenges in this field is building trust between users and the model. Due to things like high false alarm rate and the black box nature of machine learning models, patients and medical professionals need to understand how the model arrives at its recommendations. In this work, we present several methods that aim to improve machine learning models in high-stakes environments like healthcare. Our work unifies two sub-fields of machine learning, explainable AI, and uncertainty quantification. First we develop a model-agnostic approach to deliver instance-level explanations using influence functions. Next, we show that these influence functions function are fairly robust across domains. Then, we develop an efficient method that reduces model uncertainty while modeling data uncertainty via Bayesian Neural Networks. Finally, we show that when combined our methods deliver significant utility beyond traditional methods while retaining a high level of performance via a real world deployment. Overall, the integration of uncertainty quantification and explainable AI can help overcome some of the major challenges of machine learning in healthcare. Together, they can provide healthcare professionals with powerful tools for improving patient outcomes and advancing medical research

Opening Access to Visual Exploration of Audiovisual Digital Biomarkers: an OpenDBM Analytics Tool

Digital biomarkers (DBMs) are a growing field and increasingly tested in the therapeutic areas of psychiatric and neurodegenerative disorders. Meanwhile, isolated silos of knowledge of audiovisual DBMs use in industry, academia, and clinics hinder their widespread adoption in clinical research. How can we help these non-technical domain experts to explore audiovisual digital biomarkers? The use of open source software in biomedical research to extract patient behavior changes is growing and inspiring a shift toward accessibility to address this problem. OpenDBM integrates several popular audio and visual open source behavior extraction toolkits. We present a visual analysis tool as an extension of the growing open source software, OpenDBM, to promote the adoption of audiovisual DBMs in basic and applied research. Our tool illustrates patterns in behavioral data while supporting interactive visual analysis of any subset of derived or raw DBM variables extracted through OpenDBM.Comment: 6 pages, 2 figures, 2022 IEEE VIS Workshop - Visualization in BioMedical A