ABSTRACT

The most common diagnoses after childhood sexual abuse are Post-Traumatic Stress Disorder and depression. The aim of this study is to design a decision support system to help psychiatry physicians in the treatment of childhood sexual abuse. Computer aided decision support system (CADSS) based on ANN, which predicts the development of PTSD and Major Depressive Disorder, using different parameters of the act of abuse and patients was designed. The data of 149 girls and 21 boys who were victims of sexual abuse were included in the study. In the designed CADDS, the gender of the victim, the type of sexual abuse, the age of exposure, the duration until reporting, the time of abuse, the proximity of the abuser to the victim, number of sexual abuse, whether the child is exposed to threats and violence during the abuse, the person who reported the event, and the intelligence level of the victim are used as input parameters. The average accuracy values for all three designed systems were calculated as 99.2%. It has been shown that the system designed by using these data can be used safely in the psychiatric assessment process, in order to differentiate psychiatric diagnoses in the early post-abuse period.

摘要

儿童性虐待后最常见的诊断是创伤后应激障碍和抑郁症。本研究的目的是设计一个决策支持系统，以帮助精神科医生治疗儿童性虐待。设计了基于人工神经网络的计算机辅助决策支持系统（CADSS），该系统使用虐待行为和患者的不同参数来预测创伤后应激障碍和重度抑郁症的发展。研究包括了 149 名女孩和 21 名男孩遭受性虐待的数据。在设计的 CADDS 中，受害者的性别、性虐待的类型、暴露的年龄、直到报告的持续时间、虐待的时间、施虐者与受害者的接近程度、性虐待的次数、孩子是否在虐待期间暴露于威胁和暴力，报告事件的人，和受害者的智力水平被用作输入参数。所有三个设计系统的平均准确度值计算为 99.2%。已经表明，使用这些数据设计的系统可以安全地用于精神病学评估过程，以便在虐待后的早期区分精神病学诊断。