# Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features

arXiv: https://arxiv.org/abs/1706.04206 · HTML: https://mlsanity.com/paper/1706.04206/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

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

- [Causality extraction from medical text using Large Language Models (LLMs)](https://mlsanity.com/paper/2407.10020/) (2024)
- [Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management](https://mlsanity.com/paper/2104.00660/) (2021)

## Abstract (arXiv)

This paper advances the state of the art in text understanding of medical guidelines by releasing two new annotated clinical guidelines datasets, and establishing baselines for using machine learning to extract condition-action pairs. In contrast to prior work that relies on manually created rules, we report experiment with several supervised machine learning techniques to classify sentences as to whether they express conditions and actions. We show the limitations and possible extensions of this work on text mining of medical guidelines.