Inferring multimodal latent topics from electronic health records

Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However...

Full description

Saved in:
Bibliographic Details
Published in:Nature communications Vol. 11; no. 1; pp. 2536 - 17
Main Authors: Li, Yue, Nair, Pratheeksha, Lu, Xing Han, Wen, Zhi, Wang, Yuening, Dehaghi, Amir Ardalan Kalantari, Miao, Yan, Liu, Weiqi, Ordog, Tamas, Biernacka, Joanna M., Ryu, Euijung, Olson, Janet E., Frye, Mark A., Liu, Aihua, Guo, Liming, Marelli, Ariane, Ahuja, Yuri, Davila-Velderrain, Jose, Kellis, Manolis
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 21.05.2020
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/1305
639/705/1042
692/700
Bayes Theorem
Bayesian analysis
Bipolar disorder
Cardiovascular diseases
Coronary artery disease
Data integration
Databases, Factual
Diagnostic systems
Disease
Electronic health records
Electronic Health Records - classification
Electronic Health Records - statistics & numerical data
Electronic medical records
Heart diseases
Humanities and Social Sciences
Humans
Informatics
Laboratory tests
Machine Learning
Mathematical models
Medical Informatics - methods
Missing data
Modelling
Models, Statistical
multidisciplinary
Phenotype
Precision medicine
Science
Science (multidisciplinary)
ISSN:2041-1723, 2041-1723
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics. Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-16378-3