Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.

ABSTRACT:

Importance

To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify on a large scale.

Objective

To develop and compare 2 natural language processing methods, a rules-based approach and a machine learning (ML) approach, for identifying bleeding events in clinical notes.

Design, setting, and participants

This diagnostic study used deidentified notes from the Medical Information Mart for Intensive Care, which spans 2001 to 2012. A training set of 990 notes and a test set of 660 notes were randomly selected. Physicians classified each note as present or absent for a clinically relevant bleeding

SUBMITTER: Taggart M 

PROVIDER: S-EPMC6324448 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature