# Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU

arXiv: https://arxiv.org/abs/1806.02878 · HTML: https://mlsanity.com/paper/1806.02878/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 22 of 22)

- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://mlsanity.com/paper/2106.00750/) (2021)
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- [What Do You See in this Patient? Behavioral Testing of Clinical NLP Models](https://mlsanity.com/paper/2111.15512/) (2021)
- [FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction](https://mlsanity.com/paper/2406.11928/) (2024)
- [Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data](https://mlsanity.com/paper/2104.02932/) (2021)
- [Large Language Model as a Universal Clinical Multi-task Decoder](https://mlsanity.com/paper/2406.12738/) (2024)
- [Enhancing In-Hospital Mortality Prediction Using Multi-Representational Learning with LLM-Generated Expert Summaries](https://mlsanity.com/paper/2411.16818/) (2024)
- [Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions](https://mlsanity.com/paper/2110.08918/) (2021)
- [Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data](https://mlsanity.com/paper/2102.11872/) (2021)
- [ExpertNet: A Symbiosis of Classification and Clustering](https://mlsanity.com/paper/2201.06344/) (2022)
- [GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data](https://mlsanity.com/paper/2205.04892/) (2022)
- [Time Associated Meta Learning for Clinical Prediction](https://mlsanity.com/paper/2303.02570/) (2023)
- [Length of Stay prediction for Hospital Management using Domain Adaptation](https://mlsanity.com/paper/2306.16823/) (2023)
- [UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks](https://mlsanity.com/paper/2408.07075/) (2024)
- [PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/2509.05478/) (2025)
- [Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning](https://mlsanity.com/paper/2606.05258/) (2026)
- [Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts](https://mlsanity.com/paper/2606.21885/) (2026)
- [Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection](https://mlsanity.com/paper/2610.19178/) (2026)

## Abstract (arXiv)

Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific groups. In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task. We demonstrate how to discover relevant groups in an unsupervised way with a sequence-to-sequence autoencoder. We show that using these groups in a multi-task framework leads to better predictive performance of in-hospital mortality both across groups and overall. We also highlight the need for more granular evaluation of performance when dealing with heterogeneous populations.