Clairvoyance: A Pipeline Toolkit for Medical Time Series
Daniel Jarrett Jinsung Yoon Ioana Bica Zhaozhi Qian Ari Ercole Mihaela van der Schaar
Time-series learning is the bread and butter of data-driven *clinical
decision support*, and the recent explosion in ML research has demonstrated
great potential in various healthcare settings. At the same time, medical
time-series problems in the wild are challenging due to their highly
*composite* nature: They entail design choices and interactions among
components that preprocess data, impute missing values, select features, issue
predictions, estimate uncertainty, and interpret models. Despite exponential
growth in electronic patient data, there is a remarkable gap between the
potential and realized utilization of ML for clinical research and decision
support. In particular, orchestrating a real-world project lifecycle poses
challenges in engineering (i.e. hard to build), evaluation (i.e. hard to
assess), and efficiency (i.e. hard to optimize). Designed to address these
issues simultaneously, Clairvoyance proposes a unified, end-to-end,
autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical
standard, and (iii) interface for optimization. Our ultimate goal lies in
facilitating transparent and reproducible experimentation with complex
inference workflows, providing integrated pathways for (1) personalized
prediction, (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 pipeline
paradigm on core tasks in the healthcare journey. To the best of our knowledge,
Clairvoyance is the first to demonstrate viability of a comprehensive and
automatable pipeline for clinical time-series ML.