Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes.

Journal of Biomedical Informatics
Abdulrahman Khalifa, Stéphane M Meystre

Abstract

The 2014 i2b2 natural language processing shared task focused on identifying cardiovascular risk factors such as high blood pressure, high cholesterol levels, obesity and smoking status among other factors found in health records of diabetic patients. In addition, the task involved detecting medications, and time information associated with the extracted data. This paper presents the development and evaluation of a natural language processing (NLP) application conceived for this i2b2 shared task. For increased efficiency, the application main components were adapted from two existing NLP tools implemented in the Apache UIMA framework: Textractor (for dictionary-based lookup) and cTAKES (for preprocessing and smoking status detection). The application achieved a final (micro-averaged) F1-measure of 87.5% on the final evaluation test set. Our attempt was mostly based on existing tools adapted with minimal changes and allowed for satisfying performance with limited development efforts.

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Citations

Oct 31, 2015·Journal of Biomedical Informatics·Özlem Uzuner, Amber Stubbs
Jul 27, 2015·Journal of Biomedical Informatics·Amber StubbsÖzlem Uzuner
Feb 23, 2019·Health Informatics Journal·Kimberly ShoenbillEneida A Mendonca
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Nov 16, 2019·American Journal of Transplantation : Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons·Lisa B VanWagnerDonald M Lloyd-Jones
Jul 14, 2017·Hepatology : Official Journal of the American Association for the Study of Liver Diseases·Lisa B VanWagnerDonald M Lloyd-Jones
Aug 10, 2020·Journal of Biomedical Informatics·Sunyang FuHongfang Liu
Mar 20, 2021·Journal of Diabetes Science and Technology·Alexander Turchin, Luisa F Florez Builes
Oct 4, 2015·Journal of Biomedical Informatics·Nai-Wen ChangWen-Lian Hsu

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