Towards Phenotyping of Clinical Trial Eligibility Criteria.


BACKGROUND: Medical plaintext documents contain important facts about patients, but they are rarely available for structured queries. The provision of structured information from natural language texts in addition to the existing structured data can significantly speed up the search for fulfilled inclusion criteria and thus improve the recruitment rate. OBJECTIVES: This work is aimed at supporting clinical trial recruitment with text mining techniques to identify suitable subjects in hospitals. METHOD: Based on the inclusion/exclusion criteria of 5 sample studies and a text corpus consisting of 212 doctor's letters and medical follow-up documentation from a university cancer center, a prototype was developed and technically evaluated using NLP procedures (UIMA) for the extraction of facts from medical free texts. RESULTS: It was found that although the extracted entities are not always correct (precision between 23% and 96%), they provide a decisive indication as to which patient file should be read preferentially. CONCLUSION: The prototype presented here demonstrates the technical feasibility. In order to find available, lucrative phenotypes, an in-depth evaluation is required.

PubMed ID: 29726450

Projects: NLP4CR - Natural Language Processing for Clinical Research

Publication type: Journal article

Journal: Stud Health Technol Inform

Human Diseases: Breast cancer

Citation: Stud Health Technol Inform. 2018;248:293-299.

Date Published: 5th May 2018

Registered Mode: by PubMed ID

Authors: M. Lobe, S. Staubert, C. Goldberg, I. Haffner, A. Winter

help Submitter

Views: 3662

Created: 6th May 2019 at 13:53

Last updated: 7th Dec 2021 at 17:58

help Attributions


Related items

Powered by
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies