Work

Automated knowledge acquisition from clinical narrative reports.

Year: 2008

Type: article

Abstract: Knowledge of associations between biomedical entities, such as disease-symptoms, is critical for many automated biomedical applications. In this work, we develop automated methods for acquisition and discovery of medical knowledge embedded in clinical narrative reports. MedLEE, a Natural Language Processing (NLP) system, is applied to extract and encode clinical entities from narrative clinical reports obtained from New York-Presbyterian Hospital (NYPH), and associations between the clinical entities are determined based on statistical methods adjusted by volume tests. We focus on two types of entities, disease and symptom, in this study. Evaluation based on a random sample of disease-symptom associations indicates an overall recall of 90% and a precision of 92%. In conclusion, the preliminary study demonstrated that this method for knowledge acquisition of disease-symptom pairs from clinical reports is effective. The automated method is generalizable, and can be applied to detect other clinical associations, such as between diseases and medications.

Source: PubMed Central

Authors: Xiaoyan Wang, Amy Chused, Noémie Elhadad, Carol Friedman, Marianthi Markatou

Institution: Columbia University

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: U.S. National Library of Medicine

Awards: Capturing and linking genomic and clinical information, A Biomedical Natural Language Processing Resource