Interpretable machine learning (ML) refers to machine learning models that can provide reasons or explanations for why certain patient-level predictions are made. In high-stakes domains outside of healthcare, merely providing traditional ML metrics like accuracy, precision, recall etc. are not sufficient. In healthcare, explanations for model results and patient-level risk predictions is imperative. Clinical providers and other decision makers note interpretability as a priority for implementation and use since black box machine learning models seldom engender trust. Decisions based on machine learning predictions could inform diagnoses, clinical care pathways, and patient risk stratification, among many others. It follows, that for decisions of such import, clinicians and other staff desire to know the “reasons” behind the prediction. This workshop will cover the definitions, nuances, challenges, and requirements for the design of interpretable and explainable ML models and systems in healthcare with an emphasis on the clinical application of this framework. We will discuss many uses in which interpretable machine learning models are needed in healthcare. Additionally, we explore the landscape of recent advances to address the challenges of model interpretability in healthcare and also broadly describe how to select the most appropriate interpretable ML algorithm for a given problem, given certain clinically-oriented constraints. We will engage our audience by having interactive quizzes throughout the tutorial. Based on the practice domains of our audience, we will also encourage participants to share their stories and learnings from applying ML in healthcare and how interpretability / explainability may have played a role. If the size of our audience is appropriate, we may also break the audience into groups to discuss the desiderata of explainable ML, its advantages, and potential pitfalls. We will also share additional resources and bibliography with the participants at the conclusion of the session for further learning.
Describe the new knowledge and additional skills the participant will gain after attending your presentation.: The participant will leave the workshop with a clearer understanding of the role of explainability in applied machine learning. The participant will be able to articulate the desiderata of explainable machine learning and have a good overview of the models used.
Carly Eckert (Presenter)
Muhammad Ahmad (Presenter)