Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods

Huang, Fang-Fang; Yang, Xiang-Yun; Luo, Jia; Yang, Xiao-Jie; Meng, Fan-Qiang; Wang, Peng-Chong; Li, Zhan-Jiang

doi:10.1186/s12888-023-05299-2

Cited by 5 publications

(1 citation statement)

References 66 publications

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“…We used a more traditional statistical parametric model such as the Logistic Regression (LR) and three ML models to predict patient admission or readmission: Decision Trees (DT) (30), Random Forest (RF) (31), and Support Vector Machine (SVM) (32) (Figure 2). Each of these techniques has been applied in various ways in different mental disorders, including dementia, autism spectrum disorders, and obsessive compulsive disorder (33)(34)(35). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).…”

Section: Logistic Regression and Machine Learning Algorithmsmentioning

confidence: 99%

Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence

Palacios-Ariza,

Morales-Mendoza,

Murcia

et al. 2023

Front. Psychiatry

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IntroductionBipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. Patient admissions are preventable events with a considerable impact on global functioning and social adjustment. While machine learning (ML) approaches have proven prediction ability in other diseases, little is known about their utility to predict patient admissions in this pathology.AimTo develop prediction models for hospital admission/readmission within 5 years of diagnosis in patients with BD using ML techniques.MethodsThe study utilized data from patients diagnosed with BD in a major healthcare organization in Colombia. Candidate predictors were selected from Electronic Health Records (EHRs) and included sociodemographic and clinical variables. ML algorithms, including Decision Trees, Random Forests, Logistic Regressions, and Support Vector Machines, were used to predict patient admission or readmission. Survival models, including a penalized Cox Model and Random Survival Forest, were used to predict time to admission and first readmission. Model performance was evaluated using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC) and concordance index.ResultsThe admission dataset included 2,726 BD patients, with 354 admissions, while the readmission dataset included 352 patients, with almost half being readmitted. The best-performing model for predicting admission was the Random Forest, with an accuracy score of 0.951 and an AUC of 0.98. The variables with the greatest predictive power in the Recursive Feature Elimination (RFE) importance analysis were the number of psychiatric emergency visits, the number of outpatient follow-up appointments and age. Survival models showed similar results, with the Random Survival Forest performing best, achieving an AUC of 0.95. However, the prediction models for patient readmission had poorer performance, with the Random Forest model being again the best performer but with an AUC below 0.70.ConclusionML models, particularly the Random Forest model, outperformed traditional statistical techniques for admission prediction. However, readmission prediction models had poorer performance. This study demonstrates the potential of ML techniques in improving prediction accuracy for BD patient admissions.

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