Background

Accurate identification of persons at risk of suicide is challenging because suicide is a rare outcome with a multifactorial origin. The purpose of this study was to predict suicide among persons with depression using machine learning methods.

Methods

A case-cohort study was conducted in Denmark between January 1, 1995 and December 31, 2015. Cases were all persons who died by suicide and had an incident depression diagnosis in Denmark (n = 2,774). The comparison subcohort was a 5% random sample of all individuals in Denmark at baseline, restricted to persons with an incident depression diagnosis during the study period (n = 11,963). Classification trees and random forests were used to predict suicide.

Results

In men with depression, there was a high risk of suicide among those who were prescribed other analgesics and antipyretics (i.e., non-opioid analgesics such as acetaminophen), prescribed hypnotics and sedatives, and diagnosed with a poisoning (n = 96; risk = 81%). In women with depression, there was an elevated risk of suicide among those who were prescribed other analgesics and antipyretics, anxiolytics, and hypnotics and sedatives, but were not diagnosed with poisoning nor cerebrovascular diseases (n = 338; risk = 58%).

Discussion

Psychiatric disorders and their associated medications were strongly indicative of suicide risk. Notably, anti-inflammatory medications (e.g., acetaminophen) prescriptions, which are used to treat chronic pain and illnesses, were associated with suicide risk in persons with depression. Machine learning may advance our ability to predict suicide deaths.

中文翻译：

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抑郁症男性和女性的自杀预测：一项基于人群的研究

背景

准确识别有自杀风险的人具有挑战性，因为自杀是一种罕见的结果，其原因是多因素的。本研究的目的是使用机器学习方法预测抑郁症患者的自杀行为。

方法

1995 年 1 月 1 日至 2015 年 12 月 31 日期间在丹麦进行了一项病例队列研究。病例是丹麦所有死于自杀并被诊断为抑郁症的人 (n = 2,774)。比较亚群是丹麦基线时所有个体的 5% 随机样本，仅限于在研究期间被诊断为抑郁症的人 (n = 11,963)。分类树和随机森林用于预测自杀。

结果

在患有抑郁症的男性中，服用其他镇痛剂和退热剂（即非阿片类镇痛剂，如对乙酰氨基酚）、服用催眠剂和镇静剂并被诊断为中毒的人有很高的自杀风险（n = 96；风险 = 81%）。在患有抑郁症的女性中，服用其他镇痛药和解热药、抗焦虑药、催眠药和镇静剂但未诊断出中毒或脑血管疾病的女性自杀风险增加（n = 338；风险 = 58%）。

讨论

精神疾病及其相关药物强烈表明有自杀风险。值得注意的是，用于治疗慢性疼痛和疾病的抗炎药物（例如，对乙酰氨基酚）处方与抑郁症患者的自杀风险相关。机器学习可能会提高我们预测自杀死亡的能力。