Deep representation learning of electronic health records to unlock patient stratification at scale

Abstract Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framew...

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Autores principales:	Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee, Sarah Cherng, Giulia Landi, Matteo Danieletto, Joel T. Dudley, Cesare Furlanello, Riccardo Miotto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a3c812cf99b140a5b237ea6ab049acf1
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spelling	oai:doaj.org-article:a3c812cf99b140a5b237ea6ab049acf12021-12-02T15:32:56ZDeep representation learning of electronic health records to unlock patient stratification at scale10.1038/s41746-020-0301-z2398-6352https://doaj.org/article/a3c812cf99b140a5b237ea6ab049acf12020-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0301-zhttps://doaj.org/toc/2398-6352Abstract Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson’s disease, and Alzheimer’s disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.Isotta LandiBenjamin S. GlicksbergHao-Chih LeeSarah CherngGiulia LandiMatteo DanielettoJoel T. DudleyCesare FurlanelloRiccardo MiottoNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Isotta Landi Benjamin S. Glicksberg Hao-Chih Lee Sarah Cherng Giulia Landi Matteo Danieletto Joel T. Dudley Cesare Furlanello Riccardo Miotto Deep representation learning of electronic health records to unlock patient stratification at scale
description	Abstract Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson’s disease, and Alzheimer’s disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.
format	article
author	Isotta Landi Benjamin S. Glicksberg Hao-Chih Lee Sarah Cherng Giulia Landi Matteo Danieletto Joel T. Dudley Cesare Furlanello Riccardo Miotto
author_facet	Isotta Landi Benjamin S. Glicksberg Hao-Chih Lee Sarah Cherng Giulia Landi Matteo Danieletto Joel T. Dudley Cesare Furlanello Riccardo Miotto
author_sort	Isotta Landi
title	Deep representation learning of electronic health records to unlock patient stratification at scale
title_short	Deep representation learning of electronic health records to unlock patient stratification at scale
title_full	Deep representation learning of electronic health records to unlock patient stratification at scale
title_fullStr	Deep representation learning of electronic health records to unlock patient stratification at scale
title_full_unstemmed	Deep representation learning of electronic health records to unlock patient stratification at scale
title_sort	deep representation learning of electronic health records to unlock patient stratification at scale
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a3c812cf99b140a5b237ea6ab049acf1
work_keys_str_mv	AT isottalandi deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT benjaminsglicksberg deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT haochihlee deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT sarahcherng deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT giulialandi deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT matteodanieletto deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT joeltdudley deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT cesarefurlanello deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale AT riccardomiotto deeprepresentationlearningofelectronichealthrecordstounlockpatientstratificationatscale
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Deep representation learning of electronic health records to unlock patient stratification at scale

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