An exploration in remote blood pressure management:Application of daily routine pattern based on mobile data in health management
作者机构：Antai College of Economics and ManagementShanghai Jiao Tong UniversityShanghai 200240China
出 版 物：《Fundamental Research》 (自然科学基础研究（英文版）)
年 卷 期：2022年第2卷第1期
页 面：154-165页
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081201[工学-计算机系统结构] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：the National Natural Science Foundation of China(Grants No.91646205 and 71421002) the Fundamental Research Funds for the Central Universities of China(Grant No.16JCCS08)
主 题：Daily routine Blood pressure prediction Telemedicine Machine learning
摘 要：Blood pressure(BP)is an important indicator of an individuaPs health status and is closely related to daily ***,a continuous daily measurement of BP is critical for hypertension *** assist continuous measurement,BP prediction based on non-physiological data(ubiquitous mobile phone data)was studied in the *** algorithm was proposed that predicts BP based on patients daily routine,which includes activities such as sleep,work,and *** aim of the research is to provide insight into the application of mobile data in telemonitoring and the continuous unobtrusive daily measurement of BP.A half-year data set from October 2017 of 320 individuals,including telecom data and BP measurement data,was *** hierarchical Bayesian topic models were used to extract individuals,location-driven daily routine patterns(topics)and calculate probabilities among these topics from their day-level mobile *** on the topic probability distribution and patients contextual data,their BP were predicted using different *** prediction model comparison shows that the long short-term memory(LSTM)method exceeds others when the data has a high ***,the Random Forest regression model outperforms the LSTM ***,the experimental results validate the effectiveness of the topics in BP prediction.
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