Natural language processing techniques to detect delirium in hospitalized patients from clinical notes: a systematic review.

ABSTRACT: Delirium affects 50% of hospitalized older adults and 80% of ICU patients, yet detection rates are as low as 20-30% with traditional methods. Natural language processing (NLP) offers potential for automated detection from clinical notes. We systematically reviewed NLP techniques, searching eight databases through March 2025. From 912 records, 13 studies met inclusion criteria, analyzing over 450,000 patients. Studies employed rule-based methods (38%), machine learning (31%), deep learning (15%), topic modeling (8%), and semi-supervised learning (8%). Sensitivity ranged from 28.5% to 99.1%, with transformer models achieving the highest performance (AUROC 0.984). However, 61.5% of studies showed high risk of bias due to inadequate validation practices. Only one study conducted external valid

SUBMITTER: Shankar R 

PROVIDER: S-EPMC12635077 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature