Personal Dynamic Healthcare System Utilizing Mobile Phone and Web technologies

2004年开发了利用移动电话和网络技术的个人动态保健系统，以预防疾病和改善健康。在该系统中，通过手机输入的日常健康和生活方式数据通过互联网传输到web应用服务器。一个web应用服务器提供数据归档和参考服务，将健康和生活方式数据以图形化的方式显示在手机显示屏上，一个数据挖掘服务通知用户健康数据和生活方式数据之间的一些重要规则。2005 .研究了从健康和生活方式数据中提取相关规则的自动医疗保健数据挖掘过程。医疗保健数据挖掘背后的概念是提取个人有用的信息，例如嵌入在日常时间序列个人健康和生活方式数据中的有关生活方式和健康状况的规则和模式。这些数据存储在一个发达的个人动态医疗保健系统利用移动电话和网络技术。对志愿者用户存储的时间序列数据进行自动医疗保健数据挖掘，生成了一些有用的规则，将生活方式与血压和身体脂肪指数联系起来。2006 .这里介绍的医疗保健数据挖掘利用移动电话和网络技术，从存储在个人动态医疗保健系统中的每日时间序列个人健康和生活方式数据中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。今年的研究重点是医疗数据挖掘过程中的时间序列数据分析。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（体脂率、基础代谢、血压）之间关系的有趣结果得到了。

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.