Assesment of smartphone-based system for patients with Borderline Personality Disorder.

• Main Authors: Tzu-yu Yeh, 葉子郁
• Other Authors: 黃有評
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/u5eac7

碩士 === 國立臺北科技大學 === 電機工程系 === 106 === Self-injury and suicide have always been the common civilized diseases in todays society. Marginal personality disorder is a chronic syndrome with high morbidity and high mortality, and it is also the main inducing factor of depression. At present, dialectical behavioral therapy is widely used for treatment. In the past, the traditional method was to ask patients to go to the doctor regularly and go home to fill out the paper log card and work order, but some patients are unwilling to work hard, causing patients to be lazy and not on time. Some patients also have a lucky attitude when writing paperwork sheets and log cards at home, and the different course time of the patients often causes the medical staff to know the patients problems and integrate them effectively. To improve this problem and provide immediate assistance to the patient in the event of an emergency, to reduce the occurrence of human tragedy, this study proposes to computerize the treatment of the dialectical behavior of the paper in the form of App and put it on the Google Play store. In order to facilitate users to download and use. This study combines the IoT architecture to integrate and analyze the collected data so that patients and medical staff can keep up-to-date information at any time. In addition, this study adds a scale of dialectical behavior treatment. Through the filling of the scale, it can clearly show the improvement of the patients treatment process and enhance the effect of dialectical behavior treatment. According to the tracking analysis, there were 30 in the APP group and 6 table indicators for 2 months. The results show the trend of clinical characteristics and clinical characteristics. It can be found that BDQ is a strong significance. In addition, this study makes predictions through neural networks, and its accuracy is also 80%.