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使用机器学习预测丹麦精神病院出院后 30 天内的自杀。

Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.

机构信息

Department of Epidemiology, Boston University School of Public Health, Massachusetts, USA.

Department of Psychological and Brain Sciences, Boston University, Massachusetts, USA.

出版信息

Br J Psychiatry. 2021 Aug;219(2):440-447. doi: 10.1192/bjp.2021.19.

DOI:10.1192/bjp.2021.19
PMID:33653425
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8457342/
Abstract

BACKGROUND

Suicide risk is high in the 30 days after discharge from psychiatric hospital, but knowledge of the profiles of high-risk patients remains limited.

AIMS

To examine sex-specific risk profiles for suicide in the 30 days after discharge from psychiatric hospital, using machine learning and Danish registry data.

METHOD

We conducted a case-cohort study capturing all suicide cases occurring in the 30 days after psychiatric hospital discharge in Denmark from 1 January 1995 to 31 December 2015 (n = 1205). The comparison subcohort was a 5% random sample of all persons born or residing in Denmark on 1 January 1995, and who had a first psychiatric hospital admission between 1995 and 2015 (n = 24 559). Predictors included diagnoses, surgeries, prescribed medications and demographic information. The outcome was suicide death recorded in the Danish Cause of Death Registry.

RESULTS

For men, prescriptions for anxiolytics and drugs used in addictive disorders interacted with other characteristics in the risk profiles (e.g. alcohol-related disorders, hypnotics and sedatives) that led to higher risk of postdischarge suicide. In women, there was interaction between recurrent major depression and other characteristics (e.g. poisoning, low income) that led to increased risk of suicide. Random forests identified important suicide predictors: alcohol-related disorders and nicotine dependence in men and poisoning in women.

CONCLUSIONS

Our findings suggest that accurate prediction of suicide during the high-risk period immediately after psychiatric hospital discharge may require a complex evaluation of multiple factors for men and women.

摘要

背景

精神科医院出院后 30 天内的自杀风险很高,但对高风险患者的特征仍知之甚少。

目的

使用机器学习和丹麦登记数据,研究精神科医院出院后 30 天内自杀的性别特异性风险特征。

方法

我们进行了一项病例队列研究,该研究捕获了丹麦 1995 年 1 月 1 日至 2015 年 12 月 31 日期间精神科医院出院后 30 天内发生的所有自杀案例(n=1205)。对照子队列是丹麦 1995 年 1 月 1 日出生或居住的所有人群的 5%随机样本,他们在 1995 年至 2015 年期间首次入院精神科医院(n=24559)。预测因子包括诊断、手术、处方药物和人口统计学信息。结果是在丹麦死因登记处记录的自杀死亡。

结果

对于男性,抗焦虑药和用于成瘾障碍的药物与风险特征中的其他特征(例如与酒精相关的障碍、催眠药和镇静剂)相互作用,导致出院后自杀风险增加。对于女性,复发性重度抑郁症与其他特征(例如中毒、低收入)相互作用,导致自杀风险增加。随机森林确定了重要的自杀预测因子:男性的与酒精相关的障碍和尼古丁依赖,以及女性的中毒。

结论

我们的研究结果表明,准确预测精神科医院出院后高风险期内的自杀可能需要对男性和女性的多个因素进行复杂评估。

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Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.利用行政数据预测退伍军人健康管理系统中精神病住院后的自杀情况。
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JAMA Psychiatry. 2020 Jan 1;77(1):25-34. doi: 10.1001/jamapsychiatry.2019.2905.
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Developing a practical suicide risk prediction model for targeting high-risk patients in the Veterans health Administration.开发一个实用的自杀风险预测模型,以针对退伍军人健康管理局的高危患者。
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