Machine learning-based delirium prediction in surgical in-patients: a prospective validation study.

ABSTRACT:

Objective

Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium.

Materials and methods

738 in-patients of a vascular surgery, a trauma surgery and an orthopedic surgery department were screened for delirium using the DOS scale twice a day over their hospital stay. Concurrently, delirium risk was predicted by the ML algorithm in real-time for all patients at admission and evening of admission. The pre

SUBMITTER: Jauk S 

PROVIDER: S-EPMC11408728 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature