A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder
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...
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MDPI AG
2022-07-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/12/8/1218 |
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| author | Kiwon Kim Je il Ryu Bong Ju Lee Euihyeon Na Yu-Tao Xiang Shigenobu Kanba Takahiro A. Kato Mian-Yoon Chong Shih-Ku Lin Ajit Avasthi Sandeep Grover Roy Abraham Kallivayalil Pornjira Pariwatcharakul Kok Yoon Chee Andi J. Tanra Chay-Hoon Tan Kang Sim Norman Sartorius Naotaka Shinfuku Yong Chon Park Seon-Cheol Park |
| author_facet | Kiwon Kim Je il Ryu Bong Ju Lee Euihyeon Na Yu-Tao Xiang Shigenobu Kanba Takahiro A. Kato Mian-Yoon Chong Shih-Ku Lin Ajit Avasthi Sandeep Grover Roy Abraham Kallivayalil Pornjira Pariwatcharakul Kok Yoon Chee Andi J. Tanra Chay-Hoon Tan Kang Sim Norman Sartorius Naotaka Shinfuku Yong Chon Park Seon-Cheol Park |
| author_sort | Kiwon Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:54:56Z |
| format | Article |
| id | doaj.art-fbb951bb8b8641819513a770dbf00723 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T09:54:56Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-fbb951bb8b8641819513a770dbf007232023-12-01T23:52:36ZengMDPI AGJournal of Personalized Medicine2075-44262022-07-01128121810.3390/jpm12081218A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive DisorderKiwon Kim0Je il Ryu1Bong Ju Lee2Euihyeon Na3Yu-Tao Xiang4Shigenobu Kanba5Takahiro A. Kato6Mian-Yoon Chong7Shih-Ku Lin8Ajit Avasthi9Sandeep Grover10Roy Abraham Kallivayalil11Pornjira Pariwatcharakul12Kok Yoon Chee13Andi J. Tanra14Chay-Hoon Tan15Kang Sim16Norman Sartorius17Naotaka Shinfuku18Yong Chon Park19Seon-Cheol Park20Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul 05355, KoreaDepartment of Neurosurgery, Hanyang University College of Medicine, Seoul 05355, KoreaDepartment of Psychiatry, Inje University Haeundae Paik Hospital, Busan 47392, KoreaDepartment of Psychiatry, Presbyterian Medical Center, Jeonju 54987, KoreaUnit of Psychiatry, Department of Public Health and Medicinal Administration, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR 999078, ChinaDepartment of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, JapanDepartment of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, JapanDepartment of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung & Chang Gung University School of Medicine, Taoyuan 83301, TaiwanPsychiatry Center, Tapei City Hospital, Taipei 300, TaiwanDepartment of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh 133301, IndiaDepartment of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh 133301, IndiaPushpagiri Institute of Medical Sciences, Tiruvalla 689101, IndiaDepartment of Psychiatry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10400, ThailandTunku Abdul Rahman Institute of Neurosciences, Kuala Lumpur 5600, MalaysiaDepartment of Psychiatry, Faculty of Medicine, Hasanuddin University, Makassar 90245, IndonesiaDepartment of Pharmacology, National University Hospital, Singapore 119074, SingaporeInstitute of Mental Health, Buangkok Green Medical Park, Singapore 539747, SingaporeAssociation for the Improvement of Mental Health Programmes, 1211 Geneva, SwitzerlandDepartment of Social Welfare, School of Human Sciences, Seinan Gakuin University, Fukuoka 814-8511, JapanDepartment of Psychiatry, Hanyang University College of Medicine, Seoul 04763, KoreaDepartment of Psychiatry, Hanyang University College of Medicine, Seoul 04763, KoreaPsychotic 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.https://www.mdpi.com/2075-4426/12/8/1218psychotic symptomsdepressive disordersmajor depressionmachine learningprecision medicine |
| spellingShingle | Kiwon Kim Je il Ryu Bong Ju Lee Euihyeon Na Yu-Tao Xiang Shigenobu Kanba Takahiro A. Kato Mian-Yoon Chong Shih-Ku Lin Ajit Avasthi Sandeep Grover Roy Abraham Kallivayalil Pornjira Pariwatcharakul Kok Yoon Chee Andi J. Tanra Chay-Hoon Tan Kang Sim Norman Sartorius Naotaka Shinfuku Yong Chon Park Seon-Cheol Park A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder Journal of Personalized Medicine psychotic symptoms depressive disorders major depression machine learning precision medicine |
| title | A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder |
| title_full | A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder |
| title_fullStr | A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder |
| title_full_unstemmed | A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder |
| title_short | A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder |
| title_sort | machine learning algorithm based prediction model for psychotic symptoms in patients with depressive disorder |
| topic | psychotic symptoms depressive disorders major depression machine learning precision medicine |
| url | https://www.mdpi.com/2075-4426/12/8/1218 |
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