Risk factor detection for heart disease by applying text analytics in electronic medical records.
In the United States, about 600,000 people die of heart disease every year. The annual cost of care services, medications, and lost productivity reportedly exceeds 108.9 billion dollars. Effective disease risk assessment is critical to prevention, care, and treatment planning. Recent advancements in text analytics have opened up new possibilities of using the rich information in electronic medical records (EMRs) to identify relevant risk factors. The 2014 i2b2/UTHealth Challenge brought together researchers and practitioners of clinical natural language processing (NLP) to tackle the identification of heart disease risk factors reported in EMRs. We participated in this track and developed an NLP system by leveraging existing tools and resources, both public and proprietary. Our system was a hybrid of several machine-learning and rule-based components. The system achieved an overall F1 score of 0.9185, with a recall of 0.9409 and a precision of 0.8972.
Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States. Electronic address: manabu.torii@kp.org.
Manabu
M
Torii
Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States.
Jung-Wei
JW
Fan
Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States.
Wei-Li
WL
Yang
Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States.
Theodore
T
Lee
Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States.
Matthew T
MT
Wiley
Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States.
Daniel S
DS
Zisook
Medical Informatics, Kaiser Permanente Southern California, 11975 El Camino Real, Suite 105, San Diego, CA, United States.
Yang
Y
Huang
D000368:Aged; D002140:California / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology*; D015331:Cohort Studies; D015897:Comorbidity; D016494:Computer Security; D003219:Confidentiality; D057225:Data Mining / Q000379:methods*; D048909:Diabetes Complications / Q000175:diagnosis / Q000453:epidemiology*; D057286:Electronic Health Records / Q000458:organization & administration*; D005260:Female; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D033262:Narration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D018570:Risk Assessment / Q000379:methods; D018875:Vocabulary, Controlled
Medical records; Natural language processing; Risk assessment; Text classification