Automated Knowledge Acquisition from Clinical Narrative Reports

Overview

Journal AMIA Annu Symp Proc

Specialty Medical Informatics

Date 2008 Nov 13

PMID 18999156

Citations 22

Authors

Xiaoyan Wang

Amy Chused

Noemie Elhadad

Carol Friedman

Marianthi Markatou

Affiliations

Soon will be listed here.

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.

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