Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.

ABSTRACT:

Background

Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.

Methods

We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We catego

SUBMITTER: Sri-Iesaranusorn P 

PROVIDER: S-EPMC10333495 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature