# Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact

arXiv: https://arxiv.org/abs/2206.09074 · HTML: https://mlsanity.com/paper/2206.09074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 18 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Weak Supervision for Affordable Modeling of Electrocardiogram Data](https://mlsanity.com/paper/2201.02936/) (2022)
- [Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children](https://mlsanity.com/paper/2007.11346/) (2020)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)

## Abstract (arXiv)

A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact. Previous studies have utilized supervised ML techniques that require substantial amounts of hand-labeled data. However, manually harvesting such data can be costly, time-consuming, and mundane, and is a key factor limiting the widespread adoption of ML in healthcare (HC). Instead, we explore the use of multiple, individually imperfect heuristics to automatically assign probabilistic labels to unlabeled training data using weak supervision. Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.