# Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care

arXiv: https://arxiv.org/abs/1802.05027 · HTML: https://mlsanity.com/paper/1802.05027/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [Learning Multitask Gaussian Bayesian Networks](https://mlsanity.com/paper/2205.05343/) (2022)
- [SepAl: Sepsis Alerts On Low Power Wearables With Digital Biomarkers and On-Device Tiny Machine Learning](https://mlsanity.com/paper/2408.08316/) (2024)
- [Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms](https://mlsanity.com/paper/2504.08469/) (2025)

## Abstract (arXiv)

Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual alarms if their algorithms indicate that a patient's condition may be worsening. However, current monitoring systems are extremely sensitive to movement artefacts and technical errors. As a result, they typically trigger hundreds to thousands of false alarms per patient per day - drowning the important alarms in noise and adding to the exhaustion of clinical staff. In this setting, data is abundantly available, but obtaining trustworthy annotations by experts is laborious and expensive. We frame the problem of false alarm reduction from multivariate time series as a machine-learning task and address it with a novel multitask network architecture that utilises distant supervision through multiple related auxiliary tasks in order to reduce the number of expensive labels required for training. We show that our approach leads to significant improvements over several state-of-the-art baselines on real-world ICU data and provide new insights on the importance of task selection and architectural choices in distantly supervised multitask learning.