Literature DB >> 22549152

Mining electronic health records: towards better research applications and clinical care.

Peter B Jensen1, Lars J Jensen, Søren Brunak.   

Abstract

Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype-phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.

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Year:  2012        PMID: 22549152     DOI: 10.1038/nrg3208

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  99 in total

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Journal:  Nat Biotechnol       Date:  2011-04-24       Impact factor: 54.908

4.  Systems biology and new technologies enable predictive and preventative medicine.

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5.  The human disease network.

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Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-14       Impact factor: 11.205

Review 6.  Electronic patient records: moving from islands and bridges towards electronic health records for continuity of care.

Authors:  Petra Knaup; Oliver Bott; Christian Kohl; Christian Lovis; Sebastian Garde
Journal:  Yearb Med Inform       Date:  2007

7.  Personal genomes: when consent gets in the way.

Authors:  Patrick Taylor
Journal:  Nature       Date:  2008-11-06       Impact factor: 49.962

8.  Use abstracted patient-specific features to assist an information-theoretic measurement to assess similarity between medical cases.

Authors:  Hui Cao; Genevieve B Melton; Marianthi Markatou; George Hripcsak
Journal:  J Biomed Inform       Date:  2008-03-22       Impact factor: 6.317

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Authors:  B Malin; L Sweeney
Journal:  Proc AMIA Symp       Date:  2001

10.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.

Authors:  Joshua C Denny; Marylyn D Ritchie; Melissa A Basford; Jill M Pulley; Lisa Bastarache; Kristin Brown-Gentry; Deede Wang; Dan R Masys; Dan M Roden; Dana C Crawford
Journal:  Bioinformatics       Date:  2010-03-24       Impact factor: 6.937

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  372 in total

1.  Using Electronic Health Records To Generate Phenotypes For Research.

Authors:  Sarah A Pendergrass; Dana C Crawford
Journal:  Curr Protoc Hum Genet       Date:  2018-12-05

2.  Learning Effective Treatment Pathways for Type-2 Diabetes from a clinical data warehouse.

Authors:  Rohit Vashisht; Ken Jung; Nigam Shah
Journal:  AMIA Annu Symp Proc       Date:  2017-02-10

3.  Evaluation of Documentation Patterns of Trainees and Supervising Physicians Using Data Mining.

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Journal:  J Grad Med Educ       Date:  2014-09

4.  Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research.

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Journal:  Pharmacoeconomics       Date:  2016-02       Impact factor: 4.981

5.  Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach.

Authors:  Menno Mostert; Annelien L Bredenoord; Monique C I H Biesaart; Johannes J M van Delden
Journal:  Eur J Hum Genet       Date:  2015-11-11       Impact factor: 4.246

6.  Differential Privacy Preserving in Big Data Analytics for Connected Health.

Authors:  Chi Lin; Zihao Song; Houbing Song; Yanhong Zhou; Yi Wang; Guowei Wu
Journal:  J Med Syst       Date:  2016-02-12       Impact factor: 4.460

Review 7.  Unravelling the human genome-phenome relationship using phenome-wide association studies.

Authors:  William S Bush; Matthew T Oetjens; Dana C Crawford
Journal:  Nat Rev Genet       Date:  2016-02-15       Impact factor: 53.242

8.  Automated disease cohort selection using word embeddings from Electronic Health Records.

Authors:  Benjamin S Glicksberg; Riccardo Miotto; Kipp W Johnson; Khader Shameer; Li Li; Rong Chen; Joel T Dudley
Journal:  Pac Symp Biocomput       Date:  2018

9.  Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis.

Authors:  Finale Doshi-Velez; Yaorong Ge; Isaac Kohane
Journal:  Pediatrics       Date:  2013-12-09       Impact factor: 7.124

Review 10.  Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions.

Authors:  Anjali A Wagle; Nino Isakadze; Khurram Nasir; Seth Shay Martin
Journal:  Curr Atheroscler Rep       Date:  2021-03-10       Impact factor: 5.113

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