Predicting offenses among individuals with psychiatric disorders - A machine learning approach

Watts, Devon; Moulden, Heather M.; Mамак, Мини; Upfold, Casey; Chaimowitz, Gary; Kapczinski, Flávio

doi:10.1016/j.jpsychires.2021.03.026

Cited by 24 publications

(21 citation statements)

References 37 publications

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“… Best model had a sensitivity of 84% and specificity of 86%. Sonnweber et al [ 26 ] Clinical, developmental and social factors Discriminating between violent and nonviolent offending 370 forensic offenders with schizophrenia Training (70%) and testing (30%) sets LR RF GBM KNN SVM Naive Bayes Best model had a balanced accuracy of 67.82% Sensitivity: 72.73% Specificity: 62.92% PPV: 65.98 NPV: 70.00 AUC: 0.764 Watts et al [ 27 ] Sociodemographic, clinical, behavioral, and symptom variables Type of criminal offence (violent, sexual, nonviolent) 1240 transdiagnostic patients Training (70%) and testing (30%) sets RF Elastic Net SVM Violent vs Sexual Offences: 65.27–80.31% Non-violent vs Sexual Offences: 49.56–77.62% Sexual Offences vs Violent and Non-violent: 59.82–71.58% Best models: Violent vs Sexual Offences: Sensitivity: 76.74% Specificity: 83.87% PPV: 97.06 NPV: 34.21 Non-violent vs Sexual Offences: Sensitivity: 74.60% Specificity: 80.65% PPV: 80.65% NPV: 60.98% Sexual vs Non-violent and Violent Offences: Sensitivity: 83.15% Specificity: 60.00% PPV: 95.08 NPV: 27.69 VIOLENT OUTCOMES Kirchebner et al [ 40 ] Clinical variables pertaining to childhood, adolescence, adulthood and psychiatric stressors Violent offending in schizophrenia 370 offenders with schizophrenia 5-fold cross-validation; no external validation used. Boosted Classification Trees 76.4% Sensitivity: 80.49 Specificity: 71.19 PPV: 66 NPV: 84 AUC: 0.83 Le et al [ 31 ] Text analysis from electronic mental health records Forensic risk assessment ratings as a proxy of violence to others Four NLP dictionary word lists - 6865 mental health symptom words from Unified Medical Language System, 455 DSM-IV diagnoses from UMLS repository, 6790 English positive and negative sentiment words, and 1837 high-frequency words from the Corpus Contemporary American English (COCA).…”

Section: Resultsmentioning

confidence: 99%

“…Of the studies included in the systematic review, six assessed predictors of criminal recidivism [20][21][22][23][24][25], two assessed predictors of the type of criminal offence [26,27], three assessed predictors of physical violence during inpatient stay [28][29][30], and six assessed predictors of violent offending and aggression following discharge [24,[31][32][33][34][35][36][37][38]. All studies, apart from two [21,30], used clinical input features, including socio-demographic information, questionnaires, and psychometric measures to derive predictions.…”

Section: Resultsmentioning

confidence: 99%

“…Eight studies used machine learning models to predict criminal outcomes in patients with psychiatric disorders [20][21][22][23][24][25][26][27]. Delfin and colleagues conducted the first 10-year follow-up of a cohort of forensic psychiatry patients, including 44 individuals, who underwent a single-photon emission CT scan.…”

Section: Studies Assessing Criminal Outcomesmentioning

confidence: 99%

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Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy

et al. 2022

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Although reducing criminal outcomes in individuals with mental illness have long been a priority for governments worldwide, there is still a lack of objective and highly accurate tools that can predict these events at an individual level. Predictive machine learning models may provide a unique opportunity to identify those at the highest risk of criminal activity and facilitate personalized rehabilitation strategies. Therefore, this systematic review and meta-analysis aims to describe the diagnostic accuracy of studies using machine learning techniques to predict criminal and violent outcomes in psychiatry. We performed meta-analyses using the mada, meta, and dmetatools packages in R to predict criminal and violent outcomes in psychiatric patients (n = 2428) (Registration Number: CRD42019127169) by searching PubMed, Scopus, and Web of Science for articles published in any language up to April 2022. Twenty studies were included in the systematic review. Overall, studies used single-nucleotide polymorphisms, text analysis, psychometric scales, hospital records, and resting-state regional cerebral blood flow to build predictive models. Of the studies described in the systematic review, nine were included in the present meta-analysis. The area under the curve (AUC) for predicting violent and criminal outcomes in psychiatry was 0.816 (95% Confidence Interval (CI): 70.57–88.15), with a partial AUC of 0.773, and average sensitivity of 73.33% (95% CI: 64.09–79.63), and average specificity of 72.90% (95% CI: 63.98–79.66), respectively. Furthermore, the pooled accuracy across models was 71.45% (95% CI: 60.88–83.86), with a tau squared (τ2) of 0.0424 (95% CI: 0.0184–0.1553). Based on available evidence, we suggest that prospective models include evidence-based risk factors identified in prior actuarial models. Moreover, there is a need for a greater emphasis on identifying biological features and incorporating novel variables which have not been explored in prior literature. Furthermore, available models remain preliminary, and prospective validation with independent datasets, and across cultures, will be required prior to clinical implementation. Nonetheless, predictive machine learning models hold promise in providing clinicians and researchers with actionable tools to improve how we prevent, detect, or intervene in relevant crime and violent-related outcomes in psychiatry.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Studies Assessing Criminal Outcomesmentioning

confidence: 99%

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Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy

et al. 2022

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“…Thus far, the application of ML has been rare in the field of forensic psychiatry. Previous studies have mainly explored heterogenous forensic populations, e.g., for purposes of recidivism risk prediction, and have not focused on patients with SSD in particular [ 41 , 42 ]. The authors’ former publications, which evaluated a more homogenous population of offender patients with SSD exclusively, mainly focused on providing a better understanding of complex, multifactorial phenomena, such as stress, criminal recidivism, migration experience, self-harm, and aggressive behavior [ 6 , 18 , 30 , 32 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

et al. 2022

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Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors.

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“… Bravo-Merodio et al (2019) combined the Elastic Net with different supervised learning methods to assess the quality of clinical biomarkers. Watts et al (2021) used the elastic net to select important clinical variables from risk factor data to predict different types of criminal offenses. Max-Relevance and Min-Redundancy (mRMR) is a feature selection method that uses the dependence of features and tags and the correlation between features ( Bose et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR

Zhou

Bian

et al. 2022

Front. Bioeng. Biotechnol.

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Coal miners’ occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners’ working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.

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Predicting offenses among individuals with psychiatric disorders - A machine learning approach

Cited by 24 publications

References 37 publications

Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy

Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR

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