Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filt...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xia Ning, Ziwei Fan, Evan Burgun, Zhiyun Ren, Titus Schleyer
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c27e5388c81c44f0ba26e455f71508ad
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c27e5388c81c44f0ba26e455f71508ad
record_format	dspace
spelling	oai:doaj.org-article:c27e5388c81c44f0ba26e455f71508ad2021-12-02T20:18:40ZImproving information retrieval from electronic health records using dynamic and multi-collaborative filtering.1932-620310.1371/journal.pone.0255467https://doaj.org/article/c27e5388c81c44f0ba26e455f71508ad2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255467https://doaj.org/toc/1932-6203Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records to physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.Xia NingZiwei FanEvan BurgunZhiyun RenTitus SchleyerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255467 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xia Ning Ziwei Fan Evan Burgun Zhiyun Ren Titus Schleyer Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
description	Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records to physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.
format	article
author	Xia Ning Ziwei Fan Evan Burgun Zhiyun Ren Titus Schleyer
author_facet	Xia Ning Ziwei Fan Evan Burgun Zhiyun Ren Titus Schleyer
author_sort	Xia Ning
title	Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
title_short	Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
title_full	Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
title_fullStr	Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
title_full_unstemmed	Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
title_sort	improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c27e5388c81c44f0ba26e455f71508ad
work_keys_str_mv	AT xianing improvinginformationretrievalfromelectronichealthrecordsusingdynamicandmulticollaborativefiltering AT ziweifan improvinginformationretrievalfromelectronichealthrecordsusingdynamicandmulticollaborativefiltering AT evanburgun improvinginformationretrievalfromelectronichealthrecordsusingdynamicandmulticollaborativefiltering AT zhiyunren improvinginformationretrievalfromelectronichealthrecordsusingdynamicandmulticollaborativefiltering AT titusschleyer improvinginformationretrievalfromelectronichealthrecordsusingdynamicandmulticollaborativefiltering
_version_	1718374249251471360

Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.

Ejemplares similares