DeepAI AI Chat
Log In Sign Up

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

by   Hossein Hematialam, et al.

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.


page 1

page 2

page 3

page 4


AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into Practice

Background and Objectives: Clinical Practice Guidelines (CPGs) represent...

Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines

We introduce a formal distinction between contradictions and disagreemen...

Universal Dependency Treebank for Odia Language

This paper presents the first publicly available treebank of Odia, a mor...

On Regulating AI in Medical Products (OnRAMP)

Medical AI products require certification before deployment in most juri...

Towards Ontological Conversation Interpretation: A Method for Ontology Creation from Medical Guidelines

The automated capturing and summarization of medical consultations aims ...

A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns

Management of chronic diseases such as heart failure, diabetes, and chro...

Prolog Coding Guidelines: Status and Tool Support

The importance of coding guidelines is generally accepted throughout dev...