Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study.

ABSTRACT: This work aimed to identify pre-existing health conditions of patients with traumatic brain injury (TBI) and develop predictive models for the first TBI event and its external causes by employing a combination of unsupervised and supervised learning algorithms. We acquired up to five years of pre-injury diagnoses for 488,107 patients with TBI and 488,107 matched control patients who entered the emergency department or acute care hospitals between April 1st, 2002, and March 31st, 2020. Diagnoses were obtained from the Ontario Health Insurance Plan (OHIP) database which contains province-wide claims data by physicians in Ontario, Canada for inpatient and outpatient services. A screening process was conducted on the OHIP diagnostic codes to limit the subsequent analysis to codes that were pre

SUBMITTER: Zulbayar S 

PROVIDER: S-EPMC10785655 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature