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Time-series learning is the bread and butter of data-driven *clinicaldecision support*, and the recent explosion in ML research has demonstratedgreat potential in various healthcare settings. At the same time, medicaltime-series problems in the wild are challenging due to their highly*composite* nature: They entail design choices and interactions amongcomponents that preprocess data, impute missing values, select features, issuepredictions, estimate uncertainty, and interpret models. Despite exponentialgrowth in electronic patient data, there is a remarkable gap between thepotential and realized utilization of ML for clinical research and decisionsupport. In particular, orchestrating a real-world project lifecycle poseschallenges in engineering (i.e. hard to build), evaluation (i.e. hard toassess), and efficiency (i.e. hard to optimize). Designed to address theseissues simultaneously, Clairvoyance proposes a unified, end-to-end,autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empiricalstandard, and (iii) interface for optimization. Our ultimate goal lies infacilitating transparent and reproducible experimentation with complexinference workflows, providing integrated pathways for (1) personalizedprediction, (2) treatment-effect estimation, and (3) information acquisition.Through illustrative examples on real-world data in outpatient, general wards,and intensive-care settings, we illustrate the applicability of the pipelineparadigm on core tasks in the healthcare journey. To the best of our knowledge,Clairvoyance is the first to demonstrate viability of a comprehensive andautomatable pipeline for clinical time-series ML.

Clairvoyance: A Pipeline Toolkit for Medical Time Series