Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.
Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.
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DOI：
10.1038/mp.2015.198
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发表时间：
2016-10
影响因子：
11
通讯作者：
Zaslavsky AM
中科院分区：
文献类型：
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作者：
Kessler RC;van Loo HM;Wardenaar KJ;Bossarte RM;Brenner LA;Cai T;Ebert DD;Hwang I;Li J;de Jonge P;Nierenberg AA;Petukhova MV;Rosellini AJ;Sampson NA;Schoevers RA;Wilcox MA;Zaslavsky AM
重性抑郁症（MDD）病程的异质性使临床决策复杂化。虽然使用症状特征或生物标志物来开发临床有用的预后亚型的努力取得了有限的成功，但最近的一份报告显示，在世界卫生组织世界精神卫生（WMH）调查中，从关于终身MDD受访者的事件发作特征和合并症的自我报告中开发的机器学习（ML）模型预测了MDD的持续性，慢性化和严重程度，具有良好的准确性。我们报告了一个独立的前瞻性全国家庭样本的模型验证结果，该样本包括1,056名基线时患有终身MDD的受访者。将WMH ML模型应用于这些基线数据，以生成预测的结局评分，并将其与基线后10-12年评估的观察评分进行比较。ML模型的预测精度也与传统的逻辑回归模型进行了比较。基于ML的受试者工作特征曲线下面积（AUC）（高慢性为0.63，其他前瞻性结局为0.71 - 0.76）始终高于logistic模型（0.62 - 0.70），尽管后者模型包括更多的预测因子。34.6-38.1%的随后具有高持续性-慢性性的受访者和40.8-55.8%的严重程度指标处于基线ML预测风险分布的前20%，而只有0.9%的随后住院的受访者和1.5%的自杀企图处于ML预测风险分布的最低20%。这些结果证实了临床上有用的MDD风险分层模型可以从基线患者自我报告中生成，并且ML方法在开发此类模型时改进了传统方法。
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.