基于健康数据分析的半监督在线学习血糖预报建模算法研究

项目名称： 基于健康数据分析的半监督在线学习血糖预报建模算法研究

项目编号： No.61503208

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 纪俊

作者单位： 青岛大学

项目金额： 19万元

中文摘要： 糖尿病已成为危害人类健康的第三大疾病，血糖预测对糖尿病预防与患者治疗具有重要意义。随着信息化与网络化及可穿戴设备的发展，血糖相关的健康数据大量的通过多类数据源被收集存储，包括电子病历、体检记录、饮食、运动、作息等。而现有血糖预报算法只针对上述的一种或者少量几种数据进行建模，且没有使用大量存在的无标签样本辅助建模，无法充分利用全部数据。此外，传统的离线建模算法针对大量的健康数据建模速度过慢，严重影响了血糖预报算法的实用性。本研究针对健康数据的数据量大、数据源多样、稀疏度高等特点，基于集成学习理论，充分利用多数据源数据与大量无标签样本数据提升模型精度并加快建模速度提升现有血糖预报算法模型，设计与实现可在线更新、可利用不同数据源有效建模的半监督在线学习血糖预报算法模型，以满足现实应用中往往需要满足的低空间与计算复杂度、高准确率、模型可自适应在线更新等需求。

中文关键词： 在线学习；集成学习；半监督学习；核学习；深度学习

英文摘要： Diabetes has become the third human health harm, blood glucose prediction makes lots of sense for both diabetes prevention and medicare of diabetes patients. Lots of blood glucose related records have been saved from various data sources, including electronic medical records, health examination records, dietary records, sport and sleep records, etc. However, existing blood glucose predictive algorithms only leverage one or few kinds of aforementioned data sources and widely existed unlabelled samples are unused to improve the model precision. Additionally, owing to the large scale of healthcare data, traditional offline modelling algorithms are too time-consuming for practical use. In this study, by leveraging the high volume, high variety, high sparsity characteristics of healthcare data and based on the ensemble learning theory, a novel semi-supervised online learning blood glucose predictive model will be designed with high precision and online updatable virtue. In order to meet the practical requirements including low space and computation complexity, high accuracy and adaptive online updatable, etc., the model precision and modelling efficiency are improved by effectively use of data from different data sources and unlabelled samples respectively.

英文关键词： online learning;ensemble learning;semi-supervised learning;kernel learning;deep learning