A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder
1
Issued Date
2022-08-01
Resource Type
eISSN
20754426
Scopus ID
2-s2.0-85137394887
Journal Title
Journal of Personalized Medicine
Volume
12
Issue
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Personalized Medicine Vol.12 No.8 (2022)
Suggested Citation
Kim K., Ryu J.i., Lee B.J., Na E., Xiang Y.T., Kanba S., Kato T.A., Chong M.Y., Lin S.K., Avasthi A., Grover S., Kallivayalil R.A., Pariwatcharakul P., Chee K.Y., Tanra A.J., Tan C.H., Sim K., Sartorius N., Shinfuku N., Park Y.C., Park S.C. A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder. Journal of Personalized Medicine Vol.12 No.8 (2022). doi:10.3390/jpm12081218 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/85643
Title
A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder
Author's Affiliation
Pushpagiri Institute of Medical Sciences and Research Centre
Chang Gung University School of Medicine
Faculty of Health Sciences
Siriraj Hospital
Graduate School of Medical Sciences
Hanyang University Guri Hospital
Hasanuddin University
National University Hospital
Hanyang University College of Medicine
Seinan Gakuin University
Inje University
Singapore Institute of Mental Health
Hallym University, College of Medicine
Postgraduate Institute of Medical Education & Research, Chandigarh
Tunku Abdul Rahman Institute of Neurosciences
Association for the Improvement of Mental Health Programmes
Tapei City Hospital
Presbyterian Medical Center
Chang Gung University School of Medicine
Faculty of Health Sciences
Siriraj Hospital
Graduate School of Medical Sciences
Hanyang University Guri Hospital
Hasanuddin University
National University Hospital
Hanyang University College of Medicine
Seinan Gakuin University
Inje University
Singapore Institute of Mental Health
Hallym University, College of Medicine
Postgraduate Institute of Medical Education & Research, Chandigarh
Tunku Abdul Rahman Institute of Neurosciences
Association for the Improvement of Mental Health Programmes
Tapei City Hospital
Presbyterian Medical Center
Other Contributor(s)
Abstract
Psychotic symptoms are rarely concurrent with the clinical manifestations of depression. Additionally, whether psychotic major depression is a subtype of major depression or a clinical syndrome distinct from non-psychotic major depression remains controversial. Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, we developed a machine-learning-algorithm-based prediction model for concurrent psychotic symptoms in patients with depressive disorders. The advantages of machine learning algorithms include the easy identification of trends and patterns, handling of multi-dimensional and multi-faceted data, and wide application. Among 1171 patients with depressive disorders, those with psychotic symptoms were characterized by significantly higher rates of depressed mood, loss of interest and enjoyment, reduced energy and diminished activity, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, psychomotor agitation or retardation, disturbed sleep, diminished appetite, and greater proportions of moderate and severe degrees of depression compared to patients without psychotic symptoms. The area under the curve was 0.823. The overall accuracy was 0.931 (95% confidence interval: 0.897–0.956). Severe depression (degree of depression) was the most important variable in the prediction model, followed by diminished appetite, subthreshold (degree of depression), ideas or acts of self-harm or suicide, outpatient status, age, psychomotor retardation or agitation, and others. In conclusion, the machine-learning-based model predicted concurrent psychotic symptoms in patients with major depression in connection with the “severity psychosis” hypothesis.