Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, Canada; Neuroscience Graduate Program, McMaster University, Hamilton, Canada.
Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, Canada.
J Psychiatr Res. 2021 Jun;138:146-154. doi: 10.1016/j.jpsychires.2021.03.026. Epub 2021 Mar 29.
Actuarial risk estimates are considered the gold-standard way to assess whether psychiatric patients are likely to commit prospective criminal offenses. However, these risk estimates cannot individually predict the type of criminal offense a patient will subsequently commit, and often simply assess the general likelihood of crime occurring in a group sample. In order to advance the predictive utility of risk assessments, better statistical strategies are required.
To develop a machine learning model to predict the type of criminal offense committed in a large transdiagnostic sample of psychiatry patients, at an individual level.
Machine learning algorithms (Random Forest, Elastic Net, SVM), were applied to a representative and diverse sample of 1240 patients in the forensic mental health system. Clinical, historical, and sociodemographic variables were considered as potential predictors and assessed in a data-driven way. Separate models were created for each type of criminal offense, and feature selection methods were used to improve the interpretability and generalizability of our findings.
Sexual offenses can be predicted from nonviolent and violent offenses at an individual level with a sensitivity of 82.44% and specificity of 60.00%, using only 36 variables. Furthermore, in a binary classification model, sexual and violent offenses can be predicted at an individual level with 83.26% sensitivity and 77.42% specificity using only 20 clinical variables. Likewise, non-violent and sexual offenses can be individually predicted with 74.60% sensitivity and 80.65% specificity using 30 clinical variables.
The current results suggest that machine learning models can show greater accuracy than gold-standard risk assessment tools (AUCs 0.70-0.80). However, unlike existing risk tools, this approach allows for the prediction of cases at an individual level, which is more clinically useful. Despite this, it is important to note that a large subset of patients in the sample were involved in the criminal system in the past, prior to an official diagnosis. Therefore, many of the variables that predict offenses may be derived from the issues of prior offenses. Irrespective of this, the accuracy of prospective models is expected to only improve with further refinement.
精算风险估计被认为是评估精神病患者是否有可能犯下未来犯罪的黄金标准方法。然而，这些风险估计不能单独预测患者随后将犯下的犯罪类型，并且通常只是评估群体样本中犯罪发生的一般可能性。为了提高风险评估的预测效用，需要更好的统计策略。
开发一种机器学习模型，以预测在大型跨诊断精神病患者样本中个体的犯罪类型。
机器学习算法（随机森林、弹性网络、支持向量机）应用于法医精神卫生系统中 1240 名代表性和多样化的患者样本。临床、历史和社会人口统计学变量被视为潜在的预测因子，并以数据驱动的方式进行评估。为每种犯罪类型创建单独的模型，并使用特征选择方法提高我们发现的可解释性和通用性。
可以从非暴力和暴力犯罪中预测性犯罪，个体水平的敏感性为 82.44%，特异性为 60.00%，仅使用 36 个变量。此外，在二元分类模型中，仅使用 20 个临床变量，可以个体水平预测性和暴力犯罪的敏感性为 83.26%，特异性为 77.42%。同样，可以使用 30 个临床变量分别预测非暴力和性犯罪，敏感性为 74.60%，特异性为 80.65%。
目前的结果表明，机器学习模型可以比黄金标准风险评估工具（AUCs 0.70-0.80）显示更高的准确性。然而，与现有的风险工具不同，这种方法允许在个体水平上预测病例，这更具临床意义。尽管如此，重要的是要注意，样本中的很大一部分患者在正式诊断之前就已经涉及到刑事系统。因此，预测犯罪的许多变量可能来自于先前犯罪的问题。无论如何，随着进一步的改进，前瞻性模型的准确性预计只会提高。
