论文标题
关于我们在先前的计算精神病学数据驱动的方法项目以及它们如何危害临床实践中这些发现的翻译的错误
On mistakes we made in prior Computational Psychiatry Data driven approach projects and how they jeopardize translation of those findings in clinical practice
论文作者
论文摘要
在对七个不同的机器学习模型在检测抑郁任务上的性能进行比较之后，表明特征的选择至关重要，我们将方法和结果与其他研究人员的发表工作进行了比较。最后，我们总结了最佳实践，以便可以将这种有用的分类解决方案转化为临床实践，以高精度和更好的接受度转化为临床实践。
After performing comparison of the performance of seven different machine learning models on detection depression tasks to show that the choice of features is essential, we compare our methods and results with the published work of other researchers. In the end we summarize optimal practices in order that this useful classification solution can be translated to clinical practice with high accuracy and better acceptance.
