A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The goal of this study was to utilize prospective survey data and ensemble machine learning to develop algorithms predicting adult onset internalizing disorders. The data were from Waves 1-2 of the National Epidemiological Survey on Alcohol and Related Conditions (n = 34,653). Outcomes were incident occurrence of DSM-IV generalized anxiety, panic, social phobia, depression, and mania between Waves 1-2. In total, 213 risk factors (features) were operationalized based on their presence/occurrence at the time of or before Wave 1. For each of the five internalizing disorder outcomes, super learning was used to generate a composite algorithm from several linear and non-linear classifiers (e.g., random forests, k-nearest neighbors). AUCs achieved by the cross-validated super learner ensembles were in the range of 0.76 (depression) to 0.83 (mania), and were higher than AUCs achieved by the individual algorithms. Individuals in the top 10% of super learner predicted risk accounted for 37.97% (depression) to 53.39% (social anxiety) of all incident cases. Thus, the algorithms achieved acceptable-to-excellent prediction accuracy with a high concentration of incident cases observed among individuals predicted to be highest risk. In parallel with the development of effective preventive interventions, further validation, expansion, and dissemination of algorithms predicting internalizing disorder onset/trajectory could be of great value.

中文翻译：

---

开发预测成人发作内在性疾病的算法：一种整体学习方法。

越来越多的文献正在利用机器学习方法来开发心理病理风险算法，该算法可用于提供预防性干预。但是，开发用于使疾病内在化的算法的努力受到限制。这项研究的目的是利用前瞻性调查数据和整体机器学习来开发预测成人发作内在性疾病的算法。数据来自全国酒精和相关疾病流行病学调查的1-2波（n = 34,653）。结果是在第1-2波之间发生DSM-IV普遍性焦虑，恐慌，社交恐惧症，抑郁和躁狂事件。根据第1浪时或之前的存在/发生情况，总共操作了213个风险因子（特征）。对于五种内在化疾病结局中的每一个，超级学习被用来从多个线性和非线性分类器（例如，随机森林，k近邻）生成合成算法。交叉验证的超级学习者合奏获得的AUC在0.76（抑郁）至0.83（躁狂）的范围内，并且高于单个算法获得的AUC。在超级学习者中，前10％的人预测的风险占所有事件病例的37.97％（抑郁）至53.39％（社会焦虑）。因此，该算法在被预测为最高风险的个体中观察到大量的事件案例，从而实现了可接受的至卓越的预测精度。在开发有效的预防性干预措施的同时，进一步进行验证，扩展，