TY  - JOUR
PY  - 2024//
TI  - Machine learning applied to prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: a systematic review
JO  - Journal of affective disorders
A1  - Amanollahi, Mobina
A1  - Jameie, Melika
A1  - Looha, Mehdi Azizmohammad
A1  - Basti, Fatemeh A.
A1  - Cattarinussi, Giulia
A1  - Moghaddam, Hossein Sanjari
A1  - Di Camillo, Fabio
A1  - Akhondzadeh, Shahin
A1  - Pigoni, Alessandro
A1  - Sambataro, Fabio
A1  - Brambilla, Paolo
A1  - Delvecchio, Giuseppe
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - BACKGROUND: Bipolar disorder (BD) is a mental disorder associated with increased morbidity/mortality. Adverse outcome prediction helps with the management of patients with BD. <br><br>METHODS: We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023. <br><br>RESULTS: Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (5 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, type I BD, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic FC in corticostriatal circuitry. <br><br>CONCLUSIONS: ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.<p />  <p>Language: en</p>
LA  - en
SN  - 0165-0327
UR  - http://dx.doi.org/10.1016/j.jad.2024.06.061
ID  - ref1
ER  -