A health informatics transformation model based on intelligent cloud computing – exemplified by type 2 diabetes mellitus with related cardiovascular diseases. (July 2020) - Lin, Hsueh-Chun; Kuo, Yu-Chen; Liu, Meng-Yu
Highlights: This study established a health informatics transformation model (HITM) with the cloud-computing technique. The PL/SQL modules were created in the database for preprocessing and training the analytical data. The results performed run-time efficiency of the platform and the feature prediction reached an accuracy of 90%. The HITM can be customized for the actual clinical database with proper machine learning in cloud computing. The feedback of computing can be expected to be a reference for risk assessment in health promotion. Abstract: Background and Objective: Many studies regarding health analysis request structured datasets but the legacy resources provide scattered data. This study aims to establish a health informatics transformation model (HITM) based upon intelligent cloud computing with the self-developed analytics modules by open source technique. The model was exemplified by the open data of type 2 diabetes mellitus (DM2) with related cardiovascular diseases. Methods: The Apache-SPARK framework was employed to generate the infrastructure of the HITM, which enables the machine learning (ML) algorithms including random forest, multi-layer perceptron classifier, support vector machine, and naïve Bayes classifier as well as the regression analysis for intelligent cloud computing. The modeling applied the MIMIC-III open database as an example to design the health informatics data warehouse, which embeds the PL/SQL-based modules to extract the analytical data for the training processes. A coupling analysis flow can drive the ML modules to train the sample data and validate the results. Results: The four modes of cloud computation were compared to evaluate the feasibility of the cloud platform in accordance with its system performance for more than 11, 500 datasets. Then, the modeling adaptability was validated by simulating the featured datasets of obesity and cardiovascular-related diseases for patients with DM2 and its complications. The results showed that the run-time efficiency of the platform performed in around one minute and the prediction accuracy of the featured datasets reached 90%. Conclusions: This study helped contribute the modeling for efficient transformation of health informatics. The HITM can be customized for the actual clinical database, which provides big data for training, with the proper ML modules for a predictable process in the cloud platform. The feedback of intelligent computing can be referred to risk assessment in health promotion.