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. 2015 Dec;58 Suppl(Suppl):S171-S182.
doi: 10.1016/j.jbi.2015.09.006. Epub 2015 Sep 12.

A hybrid model for automatic identification of risk factors for heart disease

Hui Yang  1 , Jonathan M Garibaldi  2
Affiliations

A hybrid model for automatic identification of risk factors for heart disease

Hui Yang et al. J Biomed Inform. 2015 Dec.

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.

Keywords: Clinical text mining; Heart disease; Hybrid model; Machine learning; Natural language processing; Risk factors; Rule-based approach.

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Figures

Figure 1
Figure 1
Example of clinical note with detected clinical evidence related to heart disease risk factors
Figure 2
Figure 2
Document-level annotation for the risk factors detected from the free text (Figure 1)
Figure 3
Figure 3
System framework for the risk factor detection task (CRF – Conditional Random Field, NB – Naive Bayes, ME – Maximum Entropy)
Figure 4
Figure 4
The relationship between the instance number and system performance in risk indicators
See this image and copyright information in PMC

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