Literature DB >> 21624499

Electronic health records: Implications for drug discovery.

Lixia Yao1, Yiye Zhang, Yong Li, Philippe Sanseau, Pankaj Agarwal.   

Abstract

Electronic health records (EHRs) have increased in popularity in many countries. Pushed by legal mandates, EHR systems have seen substantial progress recently, including increasing adoption of standards, improved medical vocabularies and enhancements in technical infrastructure for data sharing across healthcare providers. Although the progress is directly beneficial to patient care in a hospital or clinical setting, it can also aid drug discovery. In this article, we review three specific applications of EHRs in a drug discovery context: finding novel relationships between diseases, re-evaluating drug usage and discovering phenotype-genotype associations. We believe that in the near future EHR systems and related databases will impact significantly how we discover and develop safe and efficacious medicines.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21624499     DOI: 10.1016/j.drudis.2011.05.009

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  14 in total

1.  Defining a comprehensive verotype using electronic health records for personalized medicine.

Authors:  Mary Regina Boland; George Hripcsak; Yufeng Shen; Wendy K Chung; Chunhua Weng
Journal:  J Am Med Inform Assoc       Date:  2013-09-03       Impact factor: 4.497

2.  Leveraging Big Data to Transform Drug Discovery.

Authors:  Benjamin S Glicksberg; Li Li; Rong Chen; Joel Dudley; Bin Chen
Journal:  Methods Mol Biol       Date:  2019

3.  TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database.

Authors:  Lirong Wang; Chao Ma; Peter Wipf; Haibin Liu; Weiwei Su; Xiang-Qun Xie
Journal:  AAPS J       Date:  2013-01-05       Impact factor: 4.009

Review 4.  In silico methods for drug repurposing and pharmacology.

Authors:  Rachel A Hodos; Brian A Kidd; Khader Shameer; Ben P Readhead; Joel T Dudley
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2016-04-15

Review 5.  Mining electronic health records: towards better research applications and clinical care.

Authors:  Peter B Jensen; Lars J Jensen; Søren Brunak
Journal:  Nat Rev Genet       Date:  2012-05-02       Impact factor: 53.242

6.  Serendipity-A Machine-Learning Application for Mining Serendipitous Drug Usage From Social Media.

Authors:  Boshu Ru; Dingcheng Li; Yueqi Hu; Lixia Yao
Journal:  IEEE Trans Nanobioscience       Date:  2019-04-04       Impact factor: 2.935

7.  Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials.

Authors:  Riccardo Miotto; Chunhua Weng
Journal:  J Am Med Inform Assoc       Date:  2015-03-13       Impact factor: 4.497

8.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.

Authors:  Riccardo Miotto; Li Li; Brian A Kidd; Joel T Dudley
Journal:  Sci Rep       Date:  2016-05-17       Impact factor: 4.379

9.  Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.

Authors:  Imon Banerjee; Miji Sofela; Jaden Yang; Jonathan H Chen; Nigam H Shah; Robyn Ball; Alvin I Mushlin; Manisha Desai; Joseph Bledsoe; Timothy Amrhein; Daniel L Rubin; Roham Zamanian; Matthew P Lungren
Journal:  JAMA Netw Open       Date:  2019-08-02

10.  Synergistic drug combinations from electronic health records and gene expression.

Authors:  Yen S Low; Aaron C Daugherty; Elizabeth A Schroeder; William Chen; Tina Seto; Susan Weber; Michael Lim; Trevor Hastie; Maya Mathur; Manisha Desai; Carl Farrington; Andrew A Radin; Marina Sirota; Pragati Kenkare; Caroline A Thompson; Peter P Yu; Scarlett L Gomez; George W Sledge; Allison W Kurian; Nigam H Shah
Journal:  J Am Med Inform Assoc       Date:  2017-05-01       Impact factor: 4.497

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