Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning
Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning
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DOI：
10.1016/j.eclinm.2020.100281
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发表时间：
2020-03-01
影响因子：
15.1
通讯作者：
Patten, Scott B.
中科院分区：
文献类型：
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作者：
Sanderson, Michael;Bulloch, Andrew G. M.;Patten, Scott B.
背景：自杀是世界范围内死亡的主要原因，并导致大量人的生命年损失。有机会评估行政卫生保健系统数据和机器学习是否可以量化临床环境中的自杀风险。方法：目的是比较预测模型的性能，该模型量化了在ED就诊90天内因副自杀而死亡的风险与行政卫生保健系统数据中可用的预测因子。建模数据集来自5个行政卫生保健数据系统。数据系统包含了加拿大艾伯塔省近407万人的几乎所有的医生就诊、门诊就诊、住院和社区药房处方。选择了101个预测因子，并对死亡季度前的8个季度（2年）进行了汇总，得出每个人总共有808个预测因子。通过10倍交叉验证验证预测模型的性能。结果：最优梯度增强树预测模型取得了很好的鉴别效果（AUC: 0.88）和校正效果，可用于临床应用。最优梯度增强树模型中最重要的5个预测因子分别来自不同的行政医疗保健数据系统。解释：来自多个行政数据系统的预测因子的组合以及个人和生态预测因子的组合导致了有希望的预测性能。需要进一步的研究来开发优化的预测模型，以便在临床环境中实施。(C) 2020由Elsevier Ltd.出版。这是一篇基于CC BY-NC-ND许可的开放获取文章。(http://creativecommens.org/licenses/by-nc-nd/4.0/)
Background: Suicide is a leading cause of death worldwide and results in a large number of person years of life lost. There is an opportunity to evaluate whether administrative health care system data and machine learning can quantify suicide risk in a clinical setting.Methods: The objective was to compare the performance of prediction models that quantify the risk of death by suicide within 90 days of an ED visit for parasuicide with predictors available in administrative health care system data.The modeling dataset was assembled from 5 administrative health care data systems. The data systems contained nearly all of the physician visits, ambulatory care visits, inpatient hospitalizations, and community pharmacy dispenses, of nearly the entire 4.07 million persons in Alberta, Canada. 101 predictors were selected, and these were assembled for each of the 8 quarters (2 years) prior to the quarter of death, resulting in 808 predictors in total for each person. Prediction model performance was validated with 10-fold cross-validation.Findings: The optimal gradient boosted trees prediction model achieved promising discrimination (AUC: 0.88) and calibration that could lead to clinical applications. The 5 most important predictors in the optimal gradient boosted trees model each came from a different administrative health care data system.Interpretation: The combination of predictors from multiple administrative data systems and the combination of personal and ecologic predictors resulted in promising prediction performance. Further research is needed to develop prediction models optimized for implementation in clinical settings. (C) 2020 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. (http://creativecommens.org/licenses/by-nc-nd/4.0/)