Identifying psychosis episodes in psychiatric admission notes via rule-based methods, machine learning, and pre-trained language models.

ABSTRACT: Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admiss

SUBMITTER: Hua Y 

PROVIDER: S-EPMC12686399 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature