Text mining of outpatient narrative notes to predict the risk of psychiatric hospitalization.

ABSTRACT: The primary purpose of this paper is to investigate whether text mining of the outpatient narrative notes for patients with severe and persistent mental illness (SPMI) can strengthen the predictions concerning the probability of an upcoming hospital readmission. A five-year study of all clinical notes for SPMI patients at the outpatient clinic of a tertiary hospital was conducted. The clinical notes were studied using ensemble classification i.e., entity recognition. Confounding variables pertaining to the patient's health status were extracted by text mining. A mixed effects logistic regression model was used for estimating the re-hospitalization risk during a clinic visit. The factors included frequency and continuity of outpatient visits, alterations in medication prescriptions, the usa

SUBMITTER: Verter V 

PROVIDER: S-EPMC11842738 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature