Description
- Abstract:
- Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR phenotyping. Most STB phenotyping studies have used structured EHR data, but some are beginning to incorporate unstructured clinical text. In this study, we used a publicly accessible natural language processing (NLP) program for biomedical text (MetaMap) and iterative elastic net regression to extract and select predictive text features from the discharge summaries of 810 inpatient admissions of interest. Initial sets of 5,866 and 2,709 text features were reduced to 18 and 11, respectively. The two models fit with these features obtained an area under the receiver operating characteristic curve of 0.866-0.895 and an area under the precision-recall curve of 0.800-0.838, demonstrating the approach’s potential to identify textual features to incorporate in phenotyping models.
- Notes:
- Thesis (Sc. M.)--Brown University, 2020
Access Conditions
- Rights
- In Copyright
- Restrictions on Use
- All rights reserved. Collection is open to the Brown community for research.
Citation
Buckland, Ryan,
"Selection of Clinical Text Features for Classifying Suicide Attempts"
(2020).
Biostatistics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/0sj5-ca83
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Biostatistics Theses and Dissertations
Theses and Dissertations for the Biostatistics department....