Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery

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Consistent Individualized Feature Attribution for Tree Ensembles (2018, background): “While model agnostic individualized explanation methods [2, 9, 16, 21, 27] can be applied to trees [17], they are significantly slower than treespecific methods and have sampling variability (see Section 5.”
Explainable AI for Trees: From Local Explanations to Global Understanding (2019, background): “It is particularly important in medical applications, where the patterns uncovered by a model are often even more important than the model’s prediction performance [64, 42].”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “An early application of explainability in biomedicine includes [354], an approach that integrates high ﬁdelity data from a hospital’s information management system ( e.g. , data from patient monitors and anesthesia machines, medications, laboratory re-sults, and electronic medical records) to…”
From Local Explanations to Global Understanding with Explainable AI for Trees (2020, background): “Explaining predictions from tree models is particularly important in medical applications, where the patterns a model uncovers can be more important than the model’s prediction performance 15,16 .”