Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application.
Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application.
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DOI：
10.1016/j.jpsychires.2014.08.017
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发表时间：
2014-12
影响因子：
4.8
通讯作者：
Shalev, Arieh Y.
中科院分区：
文献类型：
--
作者：
Galatzer-Levy, Isaac R.;Karstoft, Karen-Inge;Statnikov, Alexander;Shalev, Arieh Y.
关键词：
有广泛的兴趣，从早期，多模式的临床和生物信息预测精神障碍的临床过程。然而，目前的计算模型构成了实现这一目标的重大障碍。创伤后应激障碍（PTSD）的早期识别创伤幸存者的风险是合理的，考虑到疾病的显着发作和丰富的推定的生物和临床风险指标。这项工作评估了机器学习（ML）预测方法识别和整合一组独特预测特征的能力，并确定其在预测创伤事件发生后10天内收集的信息中的非缓解性PTSD的准确性。收集了957名创伤幸存者的事件特征、急诊观察和早期症状数据，随访15个月。ML特征选择算法识别出一组预测因子，使所有其他预测因子变得冗余。使用支持向量机（SVM）以及其他ML分类算法来评估以下各项的预测准确性：i）ML选择的特征，ii）没有选择的所有可用特征，以及iii）单独的急性应激障碍（ASD）症状。SVM还比较了a）15个月时PTSD诊断状态与B）经验得出的非缓解性PTSD症状轨迹中成员资格的后验概率的预测。结果表示为平均受试者工作特征曲线下面积（AUC）。特征选择算法确定了16个预测因子，存在于≥95%的交叉验证试验中。从该集合预测非缓解性PTSD的准确性（AUC= 0.77）与从所有可用信息预测的准确性（AUC= 0.78）没有差异。从ASD症状预测并不比偶然性更好（AUC = 0.60）。对PTSD状态的预测不如对非缓解轨迹成员资格的预测准确（AUC= 0.71）。ML方法可以填补预测PTSD的关键空白。识别和整合独特风险指标的能力使其成为一种很有前途的方法，用于开发基于生物、心理和社会信息的复杂来源推断慢性创伤后应激精神病理学概率风险的算法。
There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder’s salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC=.77) did not differ from predicting from all available information (AUC=.78). Predicting from ASD symptoms was not better then chance (AUC =.60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC=.71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.
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