Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams

doi:10.1186/s12911-021-01387-z

科研成果详情

题名	Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams
作者	Nguyen, Thanh 1; Zhang, Tongbin2,3; Fox, Geoffrey 4; Zeng, Sisi 2; Cao, Ni 2; Pan, Chuandi2,3; Chen, Jake Y.1
发表日期	2021-02-24
发表期刊	BMC MEDICAL INFORMATICS AND DECISION MAKING 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Clinotype Lab test result Electronic medical record Machine learning
其他关键词	ELECTRONIC HEALTH RECORDS ; MISSING DATA ; IDENTIFY ; PATHWAY ; CARE ; SVM
摘要	Background In this work, we aimed to demonstrate how to utilize the lab test results and other clinical information to support precision medicine research and clinical decisions on complex diseases, with the support of electronic medical record facilities. We defined clinotypes as clinical information that could be observed and measured objectively using biomedical instruments. From well-known 'omic' problem definitions, we defined problems using clinotype information, including stratifying patients-identifying interested sub cohorts for future studies, mining significant associations between clinotypes and specific phenotypes-diseases, and discovering potential linkages between clinotype and genomic information. We solved these problems by integrating public omic databases and applying advanced machine learning and visual analytic techniques on two-year health exam records from a large population of healthy southern Chinese individuals (size n = 91,354). When developing the solution, we carefully addressed the missing information, imbalance and non-uniformed data annotation issues. Results We organized the techniques and solutions to address the problems and issues above into CPA framework (Clinotype Prediction and Association-finding). At the data preprocessing step, we handled the missing value issue with predicted accuracy of 0.760. We curated 12,635 clinotype-gene associations. We found 147 Associations between 147 chronic diseases-phenotype and clinotypes, which improved the disease predictive performance to AUC (average) of 0.967. We mined 182 significant clinotype-clinotype associations among 69 clinotypes. Conclusions Our results showed strong potential connectivity between the omics information and the clinical lab test information. The results further emphasized the needs to utilize and integrate the clinical information, especially the lab test results, in future PheWas and omic studies. Furthermore, it showed that the clinotype information could initiate an alternative research direction and serve as an independent field of data to support the well-known 'phenome' and 'genome' researches.
资助项目	Wenzhou Department of Science and Technology Development (Wenzhou Municipal Science and Technology Bureau) [ZG2017020]; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [3UL1TR003096-02]; American Heart Association institutional data scienceAmerican Heart Association; National Cancer InstituteUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA223976]; University of Alabama at Birmingham
出版者	BMC
出版地	LONDON
ISSN	1472-6947
EISSN	1472-6947
卷号	21 期号:SUPPL 3 页码:51
DOI	10.1186/s12911-021-01387-z
页数	12
WOS类目	Medical Informatics
WOS研究方向	Medical Informatics
WOS记录号	WOS:000621460100001
收录类别	SSCI ; SCIE ; PUBMED ; SCOPUS
URL	查看原文
Pubmed记录号	33627109
PMC记录号	PMC7903607
Scopus记录号	2-s2.0-85101500712
通讯作者地址	[Chen, Jake Y.]Informatics Institute,School of Medicine,The University of Alabama at Birmingham,Birmingham,United States
scopus学科分类	Health Policy;Health Informatics;Computer Science Applications
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/2805
专题	第一临床医学院（信息与工程学院）、附属第一医院_计算机与信息管理系附属第一医院
通讯作者	Chen, Jake Y.
作者单位	1.Informatics Institute,School of Medicine,The University of Alabama at Birmingham,Birmingham,United States; 2.School of First Clinical Medical Sciences - School of Information and Engineering,Wenzhou Medical University,Zhejiang,China; 3.Department of Computer Technology and Information Management,The First Affiliated Hospital of Wenzhou Medical University,Zhejiang,China; 4.School of Informatics,Computing,and Engineering,Indiana University,Bloomington,United States
推荐引用方式 GB/T 7714	Nguyen, Thanh,Zhang, Tongbin,Fox, Geoffrey,et al. Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams[J]. BMC MEDICAL INFORMATICS AND DECISION MAKING,2021,21(SUPPL 3):51.
APA	Nguyen, Thanh., Zhang, Tongbin., Fox, Geoffrey., Zeng, Sisi., Cao, Ni., ... & Chen, Jake Y.. (2021). Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams. BMC MEDICAL INFORMATICS AND DECISION MAKING, 21(SUPPL 3), 51.
MLA	Nguyen, Thanh,et al."Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams".BMC MEDICAL INFORMATICS AND DECISION MAKING 21.SUPPL 3(2021):51.