The impact of machine learning in predicting risk of violence: A systematic review

Parmigiani, Giovanna; Barchielli, Benedetta; Casale, Simona; Mancini, Toni; Ferracuti, Stefano

doi:10.3389/fpsyt.2022.1015914

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…Previous research has shown an acceptable power for ML models in predicting VB in populations broader than SSD patients ( 68 , 69 ). In this article, we reviewed the role of ML in predicting VB in patients with SSD.…”

Section: Discussionmentioning

confidence: 99%

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

Parsaei,

Arvin,

Taebi

et al. 2024

Front. Psychiatry

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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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Section: Discussionmentioning

confidence: 99%

“…patients (68,69). In this article, we reviewed the role of ML in predicting VB in patients with SSD.…”

Section: Key Findingsmentioning

confidence: 99%

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

Parsaei,

Arvin,

Taebi

et al. 2024

Front. Psychiatry

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“…Cell phones are readily trackable, as are healthcare clinicians via proximity badge readers. Integrating these data with patient and visitor data are ideal for an ML/AI approach to assess HV risk or track its occurrence (68). Systems such as the Evolv weapons detection system can provide actionable data, especially when coupled with AI-driven facial recognition software outside as well as inside the healthcare facility (69).…”

Section: Role Of Machine Learning/augmented Intelligence In Violence ...mentioning

confidence: 99%

In-Hospital Violence and Its Impact on Critical Care Practitioners

Bass,

Chang,

Winkle

et al. 2024

Critical Care Medicine

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Objectives: To provide a narrative review of hospital violence (HV) and its impact on critical care clinicians. Data Sources: Detailed search strategy using PubMed and OVID Medline for English language articles describing HV, risk factors, precipitating events, consequences, and mitigation strategies. Study Selection: Studies that specifically addressed HV involving critical care medicine clinicians or their practice settings were selected. The time frame was limited to the last 15 years to enhance relevance to current practice. Data Extraction: Relevant descriptions or studies were reviewed, and abstracted data were parsed by setting, clinician type, location, social media events, impact, outcomes, and responses (agency, facility, health system, individual). Data Synthesis: HV is globally prevalent, especially in complex care environments, and correlates with a variety of factors including ICU stay duration, conflict, and has recently expanded to out-of-hospital occurrences; online violence as well as stalking is increasingly prevalent. An overlap with violent extremism and terrorism that impacts healthcare facilities and clinicians is similarly relevant. A number of approaches can reduce HV occurrence including, most notably, conflict management training, communication initiatives, and visitor flow and access management practices. Rescue training for HV occurrences seems prudent. Conclusions: HV is a global problem that impacts clinicians and imperils patient care. Specific initiatives to reduce HV drivers include individual training and system-wide adaptations. Future methods to identify potential perpetrators may leverage machine learning/augmented intelligence approaches.

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“…Moreover, computational methods have helped to understand the impact of different sources of information in neurodegenerative disorders ( 36 ) or elucidating individual and contextual factors determining complex behaviors such as violence ( 37 ).…”

Section: Computational Models Applied In Neuroscience and Psychiatrymentioning

confidence: 99%

Understanding mental health through computers: An introduction to computational psychiatry

Martinez

Santamaría-García²

2023

Front. Psychiatry

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Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

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The impact of machine learning in predicting risk of violence: A systematic review

Cited by 13 publications

References 32 publications

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

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

In-Hospital Violence and Its Impact on Critical Care Practitioners

Understanding mental health through computers: An introduction to computational psychiatry

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