基于智能云计算的健康信息学转化模型——以 2 型糖尿病及其相关心血管疾病为例。

A health informatics transformation model based on intelligent cloud computing - exemplified by type 2 diabetes mellitus with related cardiovascular diseases.

机构信息

Department of Health Services Administration, China Medical University, 91 Hsueh-Shih Road, Taichung 40402, Taiwan, ROC.

Institute of Information System and Applications, National Tsing Hua University, Hsinchu, Taiwan.

出版信息

Comput Methods Programs Biomed. 2020 Jul;191:105409. doi: 10.1016/j.cmpb.2020.105409. Epub 2020 Feb 25.

DOI:10.1016/j.cmpb.2020.105409

PMID:32143073

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.

摘要

背景与目的

许多关于健康分析的研究都需要结构化的数据集，但传统资源提供的是分散的数据。本研究旨在建立一个基于智能云计算的健康信息学转换模型（HITM），并利用开源技术开发的分析模块。该模型以 2 型糖尿病（DM2）相关心血管疾病的开放数据为例进行说明。

方法

采用 Apache-SPARK 框架构建 HITM 的基础设施，支持机器学习（ML）算法，包括随机森林、多层感知机分类器、支持向量机和朴素贝叶斯分类器以及智能云计算的回归分析。该模型应用 MIMIC-III 开放数据库作为示例，设计健康信息学数据仓库，其中嵌入基于 PL/SQL 的模块，用于提取分析数据进行训练过程。耦合分析流程可驱动 ML 模块训练样本数据并验证结果。