Work

Extracting diagnoses from discharge summaries.

Year: 2005

Type: article

Abstract: We have developed a program for extracting the diagnoses and procedures from the past medical history and discharge diagnoses in the discharge summary of a case and coding these using SNOMED-CT in the UMLS. The program uses a limited amount of natural language processing. Rather, it makes use of the relatively standard structure of the discharge summary, a small dictionary to divide the text into phrases, and the extensive collection of phrases for concepts in the UMLS to do the coding. With this approach the program finds 240 of 250 desired concepts with 19 false positives in 23 discharge summaries.

Source: PubMed Central

Author: William Long

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

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: National Institute of Biomedical Imaging and Bioengineering

Award: Integrating Data, Models, and Reasoning in Critical Care