Prediction of blood test values under different lifestyle scenarios using time-series electronic health record

被引:4
|
作者
Hasegawa, Takanori [1 ]
Yamaguchi, Rui [2 ]
Kakuta, Masanori [2 ]
Sawada, Kaori [3 ]
Kawatani, Kenichi [4 ]
Murashita, Koichi [4 ]
Nakaji, Shigeyuki [3 ]
Imoto, Seiya [1 ]
机构
[1] Univ Tokyo, Hlth Intelligence Ctr, Inst Med Sci, Minato Ku, Tokyo, Japan
[2] Univ Tokyo, Human Genome Ctr, Inst Med Sci, Minato Ku, Tokyo, Japan
[3] Hirosaki Univ, Grad Sch Med, Dept Social Med, Hirosaki, Aomori, Japan
[4] Hirosaki Univ, COI Res Initiat Org, Hirosaki, Aomori, Japan
来源
PLOS ONE | 2020年 / 15卷 / 03期
基金
日本学术振兴会; 日本科学技术振兴机构;
关键词
GENOME-WIDE ASSOCIATION; NETWORKS;
D O I
10.1371/journal.pone.0230172
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Owing to increasing medical expenses, researchers have attempted to detect clinical signs and preventive measures of diseases using electronic health record (EHR). In particular, time-series EHRs collected by periodic medical check-up enable us to clarify the relevance among check-up results and individual environmental factors such as lifestyle. However, usually such time-series data have many missing observations and some results are strongly correlated to each other. These problems make the analysis difficult and there exists strong demand to detect clinical findings beyond them. We focus on blood test values in medical check-up results and apply a time-series analysis methodology using a state space model. It can infer the internal medical states emerged in blood test values and handle missing observations. The estimated models enable us to predict one's blood test values under specified condition and predict the effect of intervention, such as changes of body composition and lifestyle. We use time-series data of EHRs periodically collected in the Hirosaki cohort study in Japan and elucidate the effect of 17 environmental factors to 38 blood test values in elderly people. Using the estimated model, we then simulate and compare time-transitions of participant's blood test values under several lifestyle scenarios. It visualizes the impact of lifestyle changes for the prevention of diseases. Finally, we exemplify that prediction errors under participant's actual lifestyle can be partially explained by genetic variations, and some of their effects have not been investigated by traditional association studies.
引用
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页数:19
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