The International Conference on Learning Representations (ICLR) 2021 has accepted a paper representing landmark research from the van der Schaar Lab. The paper is entitled Clairvoyance: A Pipeline Toolkit for Medical Time Series.
ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science. The Clairvoyance work is one of five conference papers from the van der Schaar Lab already accepted for ICLR 2021.
Clairvoyance is a ground-breaking package, primarily developed to aid clinical research and decision support, that represents the culmination of years of research, development, and real-world testing. As a unified, end-to-end pipeline for time-series data, clairvoyance is unmatched in its capability and versatility: it can also be used to great effect in non-medical contexts, thanks to its ability to facilitate complex inference workflows in a transparent, reproducible and efficient manner.
“Clairvoyance is an enormously important project for our team, and is the result of years of work across a number of different areas,” says CCAIM Director, Mihaela van der Schaar. “It’s the first of its kind: an end-to-end pipeline that can produce personalized and interpretable predictions and recommendations using time-series data. I have no doubt that clairvoyance will prove useful in driving clinical decision-making research, and I also believe it can offer a lot of benefits to the machine learning community.”
For a closer look at Clairvoyance and its enormous potential, see this in-depth explainer on the van der Schaar website.
Or read the paper itself: Clairvoyance: A Pipeline Toolkit for Medical Time Series
ABSTRACT: 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.
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