Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review

BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (M...
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Autor*in:	Mohammadamin Parsaei [verfasserIn] Alireza Arvin [verfasserIn] Morvarid Taebi [verfasserIn] Homa Seyedmirzaei [verfasserIn] Giulia Cattarinussi [verfasserIn] Fabio Sambataro [verfasserIn] Alessandro Pigoni [verfasserIn] Paolo Brambilla [verfasserIn] Giuseppe Delvecchio [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	artificial intelligence machine learning schizophrenia schizophrenia spectrum disorder violent behavior

Übergeordnetes Werk:	In: Frontiers in Psychiatry - Frontiers Media S.A., 2010, 15(2024)
Übergeordnetes Werk:	volume:15 ; year:2024

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpsyt.2024.1384828

Katalog-ID:	DOAJ099751534

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520			\|a BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.
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allfields	10.3389/fpsyt.2024.1384828 doi (DE-627)DOAJ099751534 (DE-599)DOAJ3155d7f7addc44649f2eaf694dad9755 DE-627 ger DE-627 rakwb eng RC435-571 Mohammadamin Parsaei verfasserin aut Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field. artificial intelligence machine learning schizophrenia schizophrenia spectrum disorder violent behavior Psychiatry Alireza Arvin verfasserin aut Morvarid Taebi verfasserin aut Homa Seyedmirzaei verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Fabio Sambataro verfasserin aut Fabio Sambataro verfasserin aut Alessandro Pigoni verfasserin aut Alessandro Pigoni verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Giuseppe Delvecchio verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 15(2024) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:15 year:2024 https://doi.org/10.3389/fpsyt.2024.1384828 kostenfrei https://doaj.org/article/3155d7f7addc44649f2eaf694dad9755 kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1384828/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024
spelling	10.3389/fpsyt.2024.1384828 doi (DE-627)DOAJ099751534 (DE-599)DOAJ3155d7f7addc44649f2eaf694dad9755 DE-627 ger DE-627 rakwb eng RC435-571 Mohammadamin Parsaei verfasserin aut Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field. artificial intelligence machine learning schizophrenia schizophrenia spectrum disorder violent behavior Psychiatry Alireza Arvin verfasserin aut Morvarid Taebi verfasserin aut Homa Seyedmirzaei verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Fabio Sambataro verfasserin aut Fabio Sambataro verfasserin aut Alessandro Pigoni verfasserin aut Alessandro Pigoni verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Giuseppe Delvecchio verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 15(2024) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:15 year:2024 https://doi.org/10.3389/fpsyt.2024.1384828 kostenfrei https://doaj.org/article/3155d7f7addc44649f2eaf694dad9755 kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1384828/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024
allfields_unstemmed	10.3389/fpsyt.2024.1384828 doi (DE-627)DOAJ099751534 (DE-599)DOAJ3155d7f7addc44649f2eaf694dad9755 DE-627 ger DE-627 rakwb eng RC435-571 Mohammadamin Parsaei verfasserin aut Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field. artificial intelligence machine learning schizophrenia schizophrenia spectrum disorder violent behavior Psychiatry Alireza Arvin verfasserin aut Morvarid Taebi verfasserin aut Homa Seyedmirzaei verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Fabio Sambataro verfasserin aut Fabio Sambataro verfasserin aut Alessandro Pigoni verfasserin aut Alessandro Pigoni verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Giuseppe Delvecchio verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 15(2024) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:15 year:2024 https://doi.org/10.3389/fpsyt.2024.1384828 kostenfrei https://doaj.org/article/3155d7f7addc44649f2eaf694dad9755 kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1384828/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024
allfieldsGer	10.3389/fpsyt.2024.1384828 doi (DE-627)DOAJ099751534 (DE-599)DOAJ3155d7f7addc44649f2eaf694dad9755 DE-627 ger DE-627 rakwb eng RC435-571 Mohammadamin Parsaei verfasserin aut Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field. artificial intelligence machine learning schizophrenia schizophrenia spectrum disorder violent behavior Psychiatry Alireza Arvin verfasserin aut Morvarid Taebi verfasserin aut Homa Seyedmirzaei verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Fabio Sambataro verfasserin aut Fabio Sambataro verfasserin aut Alessandro Pigoni verfasserin aut Alessandro Pigoni verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Giuseppe Delvecchio verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 15(2024) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:15 year:2024 https://doi.org/10.3389/fpsyt.2024.1384828 kostenfrei https://doaj.org/article/3155d7f7addc44649f2eaf694dad9755 kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1384828/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024
allfieldsSound	10.3389/fpsyt.2024.1384828 doi (DE-627)DOAJ099751534 (DE-599)DOAJ3155d7f7addc44649f2eaf694dad9755 DE-627 ger DE-627 rakwb eng RC435-571 Mohammadamin Parsaei verfasserin aut Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field. artificial intelligence machine learning schizophrenia schizophrenia spectrum disorder violent behavior Psychiatry Alireza Arvin verfasserin aut Morvarid Taebi verfasserin aut Homa Seyedmirzaei verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Giulia Cattarinussi verfasserin aut Fabio Sambataro verfasserin aut Fabio Sambataro verfasserin aut Alessandro Pigoni verfasserin aut Alessandro Pigoni verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Paolo Brambilla verfasserin aut Giuseppe Delvecchio verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 15(2024) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:15 year:2024 https://doi.org/10.3389/fpsyt.2024.1384828 kostenfrei https://doaj.org/article/3155d7f7addc44649f2eaf694dad9755 kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1384828/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024
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Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. 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callnumber-first	R - Medicine
author	Mohammadamin Parsaei
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topic_title	RC435-571 Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review artificial intelligence machine learning schizophrenia schizophrenia spectrum disorder violent behavior
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title	Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review
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title_full	Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review
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author_browse	Mohammadamin Parsaei Alireza Arvin Morvarid Taebi Homa Seyedmirzaei Giulia Cattarinussi Fabio Sambataro Alessandro Pigoni Paolo Brambilla Giuseppe Delvecchio
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title_sort	machine learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review
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title_auth	Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review
abstract	BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.
abstractGer	BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.
abstract_unstemmed	BackgroundSchizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.
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title_short	Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review
url	https://doi.org/10.3389/fpsyt.2024.1384828 https://doaj.org/article/3155d7f7addc44649f2eaf694dad9755 https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1384828/full https://doaj.org/toc/1664-0640
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author2	Alireza Arvin Morvarid Taebi Homa Seyedmirzaei Giulia Cattarinussi Fabio Sambataro Alessandro Pigoni Paolo Brambilla Giuseppe Delvecchio
author2Str	Alireza Arvin Morvarid Taebi Homa Seyedmirzaei Giulia Cattarinussi Fabio Sambataro Alessandro Pigoni Paolo Brambilla Giuseppe Delvecchio
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up_date	2024-07-04T00:11:46.849Z
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Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.MethodsWe performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.ResultsWe included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.ConclusionML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. 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