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Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark
The British Journal of Psychiatry ( IF 8.7 ) Pub Date : 2021-03-03 , DOI: 10.1192/bjp.2021.19
Tammy Jiang 1 , Anthony J Rosellini 2 , Erzsébet Horváth-Puhó 3 , Brian Shiner 4 , Amy E Street 5 , Timothy L Lash 6 , Henrik T Sørensen 7 , Jaimie L Gradus 1
Affiliation  

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.



中文翻译:

使用机器学习预测丹麦精神病院出院后 30 天内的自杀情况

背景

从精神病院出院后 30 天内的自杀风险很高,但对高风险患者概况的了解仍然有限。

目标

使用机器学习和丹麦登记数据检查精神病院出院后 30 天内特定性别的自杀风险状况。

方法

我们进行了一项病例队列研究,收集了1995年1月1日至2015年12月31日丹麦精神病院出院后30天内发生的所有自杀病例(n = 1205)。比较子队列是 1995 年 1 月 1 日在丹麦出生或居住的所有人员的 5% 随机样本,这些人在 1995 年至 2015 年间首次入院精神病院 ( n = 24 559)。预测因素包括诊断、手术、处方药物和人口统计信息。结果是自杀死亡,记录在丹麦死因登记处。

结果

对于男性来说,抗焦虑药和治疗成瘾性疾病的药物与风险概况中的其他特征(例如酒精相关疾病、安眠药和镇静剂)相互作用,导致出院后自杀的风险更高。在女性中,复发性重度抑郁症与其他特征(例如中毒、低收入)之间存在相互作用,导致自杀风险增加。随机森林确定了重要的自杀预测因素:男性的酒精相关疾病和尼古丁依赖以及女性的中毒。

结论

我们的研究结果表明,在精神病院出院后立即准确预测高危期的自杀可能需要对男性和女性的多个因素进行复杂的评估。

更新日期:2021-03-03
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