Work

Identifying peripheral arterial disease cases using natural language processing of clinical notes

Year: 2016

Type: conference-paper

Abstract: Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide. Ascertaining PAD status from clinical notes by manual chart review is labor intensive and time consuming. In this paper, we describe a natural language processing (NLP) algorithm for automated ascertainment of PAD status from clinical notes using predetermined criteria. We developed and evaluated our system against a gold standard that was created by medical experts based on manual chart review. Our system ascertained PAD status from clinical notes with high sensitivity (0.96), positive predictive value (0.92), negative predictive value (0.99) and specificity (0.98). NLP approaches can be used for rapid, efficient and automated ascertainment of PAD cases with implications for patient care and epidemiologic research.

Source: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...)

DOI: 10.1109/bhi.2016.7455851

Authors: Naveed Afzal, Sunghwan Sohn, Sara Abram, Hongfang Liu, Iftikhar J. Kullo, Adelaide M. Arruda-Olson

Institution: Mayo Clinic

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green