Can Unified Medical Language System-based semantic representation improve automated identification of patient safety incident reports by type and severity?

ABSTRACT:

Objective

The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity.

Materials and methods

Binary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected from a state-wide reporting system. Generalizability was evaluated on different and independent hospital-level reporting system. Concepts were extracted from report narratives using the UMLS Metathesaurus, and their relevance and frequency were used as semantic features. Performance was evaluated by F-score, Hamming loss, and exact match score and was compared

SUBMITTER: Wang Y 

PROVIDER: S-EPMC7566533 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature