Advancing delirium classification: A clinical notes-based natural language processing-supported machine learning model

Abstract

Objective: The study of the epidemiology of delirium in hospitalized patients is challenging. We aimed to identify the presence or absence of delirium from clinical text notes using natural language processing (NLP) techniques and machine learning (ML) models. Materials and methods: We developed a delirium predictive model using 942 clinical notes from hospitalized patients with an ICD-10 delirium hospital discharge code. Moreover, we implemented ML models using a) delirium-suggestive words from an expert-defined dictionary or b) free text in clinical notes. Both strategies considered positive and negative delirium-associated words. Results: At the note level, for the dictionary method, the ..