Identifying First Episodes of Psychosis in Psychiatric Patient Records using Machine Learning
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningEpidemiologySimilar Papers 제목 키워드 기반
Analysis of Risk Factor Domains in Psychosis Patient Health Records
Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is crit…
Readmission PredictionPower Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis
Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic …
EEGElectroencephalogram (EEG)SpecificityTime Expressions in Mental Health Records for Symptom Onset Extraction
For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective …
Temporal Information ExtractionA new European Portuguese corpus for the study of Psychosis through speech analysis
Psychosis is a clinical syndrome characterized by the presence of symptoms such as hallucinations, thought disorder and disorganized speech. Several studies have used machine learning, combined with speech and natural la…
Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?
Mental disorders are clinically significant patterns of behavior that are associated with stress and/or impairment in social, occupational, or family activities. People suffering from such disorders are often misjudged a…