Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.
J Healthc Eng. 2022 Feb 17;2022:9270502. doi: 10.1155/2022/9270502. eCollection 2022.
In order to solve the problems of high misevaluation rate and low work efficiency in the process of mental health intelligent evaluation, a method of mental health intelligent evaluation system oriented to the decision tree algorithm is proposed. First, the current research status of mental health intelligent evaluation was analyzed and the framework of mental health intelligent evaluation system was constructed. Then, the mental health intelligent evaluation data were collected and the decision tree algorithm was used to analyze and classify the mental health intelligent evaluation data to obtain the mental health intelligent evaluation results. Finally, specific simulation experiments are used to analyze the feasibility and superiority of the mental health intelligent evaluation system. The experimental results show that the recall rate of each system increases with the increasing number of iterations, and the system has the highest recall rate. Also, it is stable after the number of iterations reaches 20, with good recall and adaptive scheduling performance. The recall rate of comparison system 1 and comparison system 2 fluctuates greatly, and the recall rate is lower than that of the system in this paper. It is proved that the method of the mental health intelligent evaluation system of the decision tree algorithm can effectively solve the problem and improve the accuracy of the mental health intelligent evaluation. The efficiency of mental health intelligent evaluation is improved, and the system stability is better, which can meet the actual requirements of current mental health intelligent evaluation.
为了解决心理健康智能评估过程中高估率高和工作效率低的问题，提出了一种面向决策树算法的心理健康智能评估系统方法。首先，分析了心理健康智能评估的当前研究现状，构建了心理健康智能评估系统的框架。然后，收集心理健康智能评估数据，并使用决策树算法对心理健康智能评估数据进行分析和分类，以获得心理健康智能评估结果。最后，通过具体的仿真实验分析了心理健康智能评估系统的可行性和优越性。实验结果表明，每个系统的召回率随着迭代次数的增加而增加，系统具有最高的召回率。并且，在迭代次数达到 20 次后，其召回率稳定，具有良好的召回和自适应调度性能。对比系统 1 和对比系统 2 的召回率波动较大，且低于本文提出的系统的召回率。证明了决策树算法的心理健康智能评估系统方法能够有效地解决问题，提高心理健康智能评估的准确性。提高了心理健康智能评估的效率，系统稳定性更好，能够满足当前心理健康智能评估的实际要求。
