Violence Detection in Real-Life Audio Signals Using Lightweight Deep Neural Networks

Bakhshi, Ali; García-Gómez, Joaquín; Gil-Pita, Roberto; Chalup, Stephan

doi:10.1016/j.procs.2023.08.162

Cited by 2 publications

(1 citation statement)

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“…The key aspect of ML is its capability to build predictive models, demonstrated by its ability to anticipate clinical outcomes such as suicidal ideation, impulsivity, and VB ( 19 , 21 , 22 ). This attribute renders ML a promising instrument for unraveling the intricate interplay between schizophrenia and VB, thereby aiding healthcare providers in the early identification of individuals susceptible to VB ( 23 , 24 ). This, in turn, holds the potential to optimize resource allocation, diminish lay times, and fortify the safety of both staff and patients ( 25 ).…”

Section: Introductionmentioning

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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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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