Background

Suicidal behaviour is difficult to detect in the general practice. Machine learning (ML) algorithms using routinely collected data might support General Practitioners (GPs) in the detection of suicidal behaviour. In this paper, we applied machine learning techniques to support GPs recognizing suicidal behaviour in primary care patients using routinely collected general practice data.

Methods

This case-control study used data from a national representative primary care database including over 1.5 million patients (Nivel Primary Care Database). Patients with a suicide (attempt) in 2017 were selected as cases (N = 574) and an at risk control group (N = 207,308) was selected from patients with psychological vulnerability but without a suicide attempt in 2017. RandomForest was trained on a small subsample of the data (training set), and evaluated on unseen data (test set).

Results

Almost two-third (65%) of the cases visited their GP within the last 30 days before the suicide (attempt). RandomForest showed a positive predictive value (PPV) of 0.05 (0.04–0.06), with a sensitivity of 0.39 (0.32–0.47) and area under the curve (AUC) of 0.85 (0.81–0.88). Almost all controls were accurately labeled as controls (specificity = 0.98 (0.97–0.98)). Among a sample of 650 at-risk primary care patients, the algorithm would label 20 patients as high-risk. Of those, one would be an actual case and additionally, one case would be missed.

Conclusion

In this study, we applied machine learning to predict suicidal behaviour using general practice data. Our results showed that these techniques can be used as a complementary step in the identification and stratification of patients at risk of suicidal behaviour. The results are encouraging and provide a first step to use automated screening directly in clinical practice. Additional data from different social domains, such as employment and education, might improve accuracy.

中文翻译：

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将机器学习应用于全科医生的健康记录数据来预测自杀倾向。

背景

在一般实践中自杀行为很难被发现。使用常规收集的数据的机器学习 (ML) 算法可能会支持全科医生 (GP) 检测自杀行为。在本文中，我们应用机器学习技术来支持全科医生使用常规收集的全科医疗数据来识别初级保健患者的自杀行为。

方法

这项病例对照研究使用了来自全国代表性初级保健数据库（包括超过 150 万患者）的数据（Nivel 初级保健数据库）。选择 2017 年自杀（企图）的患者作为病例（N = 574），并从心理脆弱但 2017 年没有自杀企图的患者中选择风险对照组（N = 207,308）。RandomForest 在一个小型样本上进行了训练数据的子样本（训练集），并根据未见过的数据（测试集）进行评估。

结果

近三分之二 (65%) 的病例在自杀（自杀未遂）前的最后 30 天内去看了全科医生。RandomForest 显示的阳性预测值 (PPV) 为 0.05 (0.04–0.06)，灵敏度为 0.39 (0.32–0.47)，曲线下面积 (AUC) 为 0.85 (0.81–0.88)。几乎所有对照都被准确地标记为对照（特异性 = 0.98 (0.97–0.98)）。在 650 名高危初级保健患者的样本中，该算法会将 20 名患者标记为高风险。其中，一个是实际案例，另外一个是遗漏案例。

结论

在这项研究中，我们利用一般实践数据应用机器学习来预测自杀行为。我们的结果表明，这些技术可以用作识别和分层有自杀行为风险的患者的补充步骤。结果令人鼓舞，并为直接在临床实践中使用自动筛查提供了第一步。来自不同社会领域（例如就业和教育）的额外数据可能会提高准确性。