Personal Dynamic Healthcare System Utilizing Mobile Phone and Web technologies

2004年开发了利用移动的电话和网络技术的个人动态医疗保健系统，以预防疾病和改善健康。在该系统中，通过移动的电话输入的日常健康和生活方式数据经由因特网传送到网络应用服务器。一个Web应用程序服务器提供数据存档和参考服务可视化健康和生活方式的数据以图形方式在移动的手机显示屏上，和一个数据挖掘服务通知用户的一些重要规则之间的健康数据和生活方式的数据。医疗保健数据挖掘背后的概念是提取个人有用的信息，例如有关生活方式和健康状况的规则和模式，嵌入在日常时间序列的个人健康和生活方式数据中。这些数据存储在一个开发的个人动态医疗保健系统，利用移动的电话和网络技术。自动医疗保健数据挖掘的志愿者用户的存储时间序列数据产生了一些有用的规则相关的生活方式与血压和身体脂肪指数。2006年医疗保健数据挖掘在这里提出的提取个人有用的信息，如规则和模式有关的生活方式和健康状况，通过使用移动的电话和网络技术，从存储在个人动态健康护理系统上的日常时间序列个人健康和生活方式数据。今年，研究的重点是医疗保健数据挖掘过程中的时间序列数据分析。获得了关于生活方式（大豆蛋白摄入、运动能量消耗、膳食摄入）与健康状况（体脂百分比、基础代谢、血压）之间关系的有趣结果。

2004Personal dynamic healthcare system utilizing mobile phone and web technologies was developed for preventing diseases and improving wellness. In this system, daily health and lifestyle data inputted through a mobile phone are transferred to a web application server via the Internet. A web application server provides data archiving and reference services visualizing health and lifestyle data graphically on a mobile phone display, and a data mining service notifying users some important rules between health data and lifestyle data.2005Automated healthcare-data-mining processes, which extract rules correlating health and lifestyle data, were studied. The concept behind healthcare data mining is extracting personally useful information, such as rules and patterns concerning lifestyles and health conditions, embedded in daily time-series personal health and lifestyle data. These data were stored on a developed personal dynamic healthcare system utilizing mobile phone and web technologies. An automated healthcare-data mining for the stored time-series data of volunteer users generated some useful rules correlating lifestyles with blood pressure and body-fat index.2006The healthcare-data-mining presented here extracts personally useful information, such as rules and patterns concerning lifestyles and health conditions, from daily time-series personal health and lifestyle data stored on a personal dynamic healthcare system by using mobile phone and web technologies. This year, studies focused on time-series data analyses in the healthcare-data-mining process. Interesting results concerning the relation between lifestyles (soybean protein ingestion, energy expenditure due to exercise, ingestion at meal) and health conditions (body-fat percentage, basal metabolism, blood pressure) were obtained.