# Classifying the reported ability in clinical mobility descriptions

arXiv: https://arxiv.org/abs/1906.03348 · HTML: https://mlsanity.com/paper/1906.03348/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes](https://mlsanity.com/paper/2311.15946/) (2023)
- [Low-resource classification of mobility functioning information in clinical sentences using large language models](https://mlsanity.com/paper/2312.10202/) (2023)

## Abstract (arXiv)

Assessing how individuals perform different activities is key information for modeling health states of individuals and populations. Descriptions of activity performance in clinical free text are complex, including syntactic negation and similarities to textual entailment tasks. We explore a variety of methods for the novel task of classifying four types of assertions about activity performance: Able, Unable, Unclear, and None (no information). We find that ensembling an SVM trained with lexical features and a CNN achieves 77.9% macro F1 score on our task, and yields nearly 80% recall on the rare Unclear and Unable samples. Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling new modalities at test time. Our findings establish a strong baseline for this novel task, and identify intriguing areas for further research.