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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
A hybrid model for automatic identification of risk factors for heart disease
Hui Yang1, Jonathan M Garibaldi1
1School of Computer Science, University of Nottingham, Nottingham, UK; Advanced Data Analysis Centre, University of Nottingham, Nottingham, UK.
Insights
This study developed an automated system to detect coronary artery disease (CAD) risk factors in medical records using natural language processing (NLP). The system achieved high accuracy, aiding in early CAD prevention and treatment.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Cardiology
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality.
- Early detection and monitoring of CAD risk factors are crucial for effective prevention and treatment.
Purpose of the Study:
- To develop an automated information extraction system for identifying heart disease risk factors in clinical records.
- To evaluate the system's performance in the 2014 i2b2/UTHealth NLP Challenge.
Main Methods:
- Utilized natural language processing (NLP) techniques including machine learning, rule-based methods, and keyword spotting.
- Applied these methods to navigate complex clinical contexts and identify diverse risk factors.
Main Results:
- The system achieved a micro-averaged F-measure of 0.915 on the challenge test data.
- Performance was competitive with the top-performing system in the challenge (F-measure of 0.927).
Conclusions:
- The developed NLP system demonstrates significant potential for automated risk factor detection in CAD.
- This technology can support clinical decision-making and improve patient outcomes for coronary artery disease.
Abstract:
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting to cope with complicated clinical contexts inherent in a wide variety of risk factors. Our system achieved encouraging performance on the challenge test data with an overall micro-averaged F-measure of 0.915, which was competitive to the best system (F-measure of 0.927) of this challenge task.
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