An integrated framework for suicide risk prediction

T. Tran, Dinh Q. Phung, Wei Luo, R. Harvey, M. Berk, S. Venkatesh
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引用次数: 44

Abstract

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient's clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in l1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.
自杀风险预测的综合框架
自杀是社会关注的一大问题。尽管社会对此给予了极大的关注,并具有非常实质性的医学和法律意义,但目前还没有令人满意的方法可以可靠地预测未来的企图或完成自杀。我们提出了一个集成的机器学习框架来应对这一挑战。我们提出的框架包括一个新的特征提取方案,一个嵌入式特征选择过程,一组风险分类器,最后是一个风险校准过程。对于时间特征提取,我们将患者的临床病史转换为一个时间图像,其中应用了一组单边滤波器。然后在极值理论下将部分响应转化为中级特征,并在11范数框架中进行选择。然后应用一组概率有序风险分类器来计算风险概率,并进一步对特征进行重新排序。最后,对预测的风险进行校准。我们与澳大利亚合作伙伴一起,对为心理健康队列收集的数据进行了全面研究,实验验证了我们提出的框架优于医生使用的风险评估工具。
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