Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

ABSTRACT:

Abstract

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism and/or deep vei

SUBMITTER: Lam BD 

PROVIDER: S-EPMC11215191 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature