Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics. (November 2017) - Chung, Chi-Jung; Kuo, Yu-Chen; Hsieh, Yun-Yu; Li, Tsai-Chung; Lin, Cheng-Chieh; Liang, Wen-Miin; Liao, Li-Na; Li, Chia-Ing; Lin, Hsueh-Chun
Highlights: This study established a subject-enabled analytics model for measurement statistics of case studies with the public health data. The Web-based model comprises the HMDW, HRIStat, and PASIS domains built by freeware for health risk assessment. Ten statistics subjects of two case studies for epidemiology were practiced in the approach to evaluate the proposed model. The PASIS interface supports step-by-step and auto-computing process modes for preliminary evaluation and instant computation. The HRIStat modules compliant with R scripts are flexible and expandable for more advanced subjects in measurement statistics. Abstract: Purpose: This study applied open source technology to establish a subject-enabled analytics model that can enhance measurement statistics of case studies with the public health data in cloud computing. Methods: The infrastructure of the proposed model comprises three domains: 1) the health measurement data warehouse (HMDW) for the case study repository, 2) the self-developed modules of online health risk information statistics (HRIStat) for cloud computing, and 3) the prototype of a Web-based process automation system in statistics (PASIS) for the health risk assessment of case studies with subject-enabled evaluation. The system design employed freeware including Java applications, MySQL, and R packages to drive a health risk expert system (HRES). In the design, the HRIStat modules enforce the typical analytics methods for biomedical statistics, and the PASIS interfaces enable process automation of the HRES for cloud computing. The Web-based model supports both modes, step-by-step analysis and auto-computing process, respectively for preliminary evaluation and real time computation. Results: The proposed model was evaluated by computing prior researches in relation to the epidemiological measurement of diseases that were caused by either heavy metal exposures in the environment or clinical complications in hospital. The simulation validity was approved by the commercial statistics software. The model was installed in a stand-alone computer and in a cloud-server workstation to verify computing performance for a data amount of more than 230 K sets. Both setups reached efficiency of about 10 5 sets per second. Conclusions: The Web-based PASIS interface can be used for cloud computing, and the HRIStat module can be flexibly expanded with advanced subjects for measurement statistics. The analytics procedure of the HRES prototype is capable of providing assessment criteria prior to estimating the potential risk to public health.