Literature DB >> 25070682

The electronic health record for translational research.

Luke V Rasmussen1.   

Abstract

With growing adoption and use, the electronic health record (EHR) represents a rich source of clinical data that also offers many benefits for secondary use in biomedical research. Such benefits include access to a more comprehensive medical history, cost reductions, and increased efficiency in conducting research, as well as opportunities to evaluate new and expanded populations for sufficient statistical power. Existing work utilizing EHR data has uncovered some complexities and considerations for their use but, more importantly, has also generated practical lessons and solutions. Given an understanding of EHR data use in cardiovascular research, expanded adoption of this data source offers great potential to further transform the research landscape.

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Year:  2014        PMID： 25070682      PMCID： PMC4147395          DOI： 10.1007/s12265-014-9579-z

Source DB:  PubMed          Journal:  J Cardiovasc Transl Res        ISSN： 1937-5387            Impact factor:   4.132


  30 in total

Review 1.  The clinician's perspective on electronic health records and how they can affect patient care.

Authors:  Stephen H Walsh
Journal:  BMJ       Date:  2004-05-15

2.  Predicting atrial fibrillation and flutter using electronic health records.

Authors:  Shreyas Karnik; Sin Lam Tan; Bess Berg; Ingrid Glurich; Jinfeng Zhang; Humberto J Vidaillet; C David Page; Rajesh Chowdhary
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

3.  Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

Authors:  Katherine M Newton; Peggy L Peissig; Abel Ngo Kho; Suzette J Bielinski; Richard L Berg; Vidhu Choudhary; Melissa Basford; Christopher G Chute; Iftikhar J Kullo; Rongling Li; Jennifer A Pacheco; Luke V Rasmussen; Leslie Spangler; Joshua C Denny
Journal:  J Am Med Inform Assoc       Date:  2013-03-26       Impact factor: 4.497

4.  Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001-2013.

Authors:  Chun-Ju Hsiao; Esther Hing
Journal:  NCHS Data Brief       Date:  2014-01

5.  Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes.

Authors:  Peter C Austin; Jack V Tu; Jennifer E Ho; Daniel Levy; Douglas S Lee
Journal:  J Clin Epidemiol       Date:  2013-02-04       Impact factor: 6.437

6.  Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.

Authors:  Jyotishman Pathak; Kent R Bailey; Calvin E Beebe; Steven Bethard; David C Carrell; Pei J Chen; Dmitriy Dligach; Cory M Endle; Lacey A Hart; Peter J Haug; Stanley M Huff; Vinod C Kaggal; Dingcheng Li; Hongfang Liu; Kyle Marchant; James Masanz; Timothy Miller; Thomas A Oniki; Martha Palmer; Kevin J Peterson; Susan Rea; Guergana K Savova; Craig R Stancl; Sunghwan Sohn; Harold R Solbrig; Dale B Suesse; Cui Tao; David P Taylor; Les Westberg; Stephen Wu; Ning Zhuo; Christopher G Chute
Journal:  J Am Med Inform Assoc       Date:  2013-11-04       Impact factor: 4.497

7.  Support vector machines classification for discriminating coronary heart disease patients from non-coronary heart disease.

Authors:  S Hongzong; W Tao; Y Xiaojun; L Huanxiang; H Zhide; L Mancang; F BoTao
Journal:  West Indian Med J       Date:  2007-10       Impact factor: 0.171

Review 8.  The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.

Authors:  Omri Gottesman; Helena Kuivaniemi; Gerard Tromp; W Andrew Faucett; Rongling Li; Teri A Manolio; Saskia C Sanderson; Joseph Kannry; Randi Zinberg; Melissa A Basford; Murray Brilliant; David J Carey; Rex L Chisholm; Christopher G Chute; John J Connolly; David Crosslin; Joshua C Denny; Carlos J Gallego; Jonathan L Haines; Hakon Hakonarson; John Harley; Gail P Jarvik; Isaac Kohane; Iftikhar J Kullo; Eric B Larson; Catherine McCarty; Marylyn D Ritchie; Dan M Roden; Maureen E Smith; Erwin P Böttinger; Marc S Williams
Journal:  Genet Med       Date:  2013-06-06       Impact factor: 8.822

9.  An algorithm that identifies coronary and heart failure events in the electronic health record.

Authors:  Thomas E Kottke; Courtney Jordan Baechler
Journal:  Prev Chronic Dis       Date:  2013       Impact factor: 2.830

10.  The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools.

Authors:  Andrew D Boyd; Jianrong John Li; Mike D Burton; Michael Jonen; Vincent Gardeux; Ikbel Achour; Roger Q Luo; Ilir Zenku; Neil Bahroos; Stephen B Brown; Terry Vanden Hoek; Yves A Lussier
Journal:  J Am Med Inform Assoc       Date:  2013-05-05       Impact factor: 4.497
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  11 in total

Review 1.  Review and Updates in Regenerative and Personalized Medicine, Preclinical Animal Models, and Clinical Care in Cardiovascular Medicine.

Authors:  Emanuele Barbato; Paul J Barton; Jozef Bartunek; Sally Huber; Borja Ibanez; Daniel P Judge; Enrique Lara-Pezzi; Craig M Stolen; Angela Taylor; Jennifer L Hall
Journal:  J Cardiovasc Transl Res       Date:  2015-10-09       Impact factor: 4.132

2.  A multi-site cognitive task analysis for biomedical query mediation.

Authors:  Gregory W Hruby; Luke V Rasmussen; David Hanauer; Vimla L Patel; James J Cimino; Chunhua Weng
Journal:  Int J Med Inform       Date:  2016-06-16       Impact factor: 4.046

3.  Extracting similar terms from multiple EMR-based semantic embeddings to support chart reviews.

Authors:  Cheng Ye; Daniel Fabbri
Journal:  J Biomed Inform       Date:  2018-05-22       Impact factor: 6.317

4.  Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology.

Authors:  Richard H Epstein; Yuel-Kai Jean; Roman Dudaryk; Robert E Freundlich; Jeremy P Walco; Dorothee A Mueller; Shawn E Banks
Journal:  Methods Inf Med       Date:  2021-10-05       Impact factor: 1.800

5.  Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association.

Authors:  Jennifer L Hall; John J Ryan; Bruce E Bray; Candice Brown; David Lanfear; L Kristin Newby; Mary V Relling; Neil J Risch; Dan M Roden; Stanley Y Shaw; James E Tcheng; Jessica Tenenbaum; Thomas N Wang; William S Weintraub
Journal:  Circ Cardiovasc Genet       Date:  2016-03-14

Review 6.  Linking Genes to Cardiovascular Diseases: Gene Action and Gene-Environment Interactions.

Authors:  Ares Pasipoularides
Journal:  J Cardiovasc Transl Res       Date:  2015-11-06       Impact factor: 4.132

7.  Leveraging medical context to recommend semantically similar terms for chart reviews.

Authors:  Cheng Ye; Bradley A Malin; Daniel Fabbri
Journal:  BMC Med Inform Decis Mak       Date:  2021-12-18       Impact factor: 2.796

8.  A Modular Architecture for Electronic Health Record-Driven Phenotyping.

Authors:  Luke V Rasmussen; Richard C Kiefer; Huan Mo; Peter Speltz; William K Thompson; Guoqian Jiang; Jennifer A Pacheco; Jie Xu; Qian Zhu; Joshua C Denny; Enid Montague; Jyotishman Pathak
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2015-03-25

9.  Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records.

Authors:  Šefket Šabanović; Majnarić Trtica Ljiljana; František Babič; Michal Vadovský; Ján Paralič; Aleksandar Včev; Andreas Holzinger
Journal:  BMC Med Inform Decis Mak       Date:  2018-04-02       Impact factor: 2.796

10.  HiGHmed - An Open Platform Approach to Enhance Care and Research across Institutional Boundaries.

Authors:  Birger Haarbrandt; Björn Schreiweis; Sabine Rey; Ulrich Sax; Simone Scheithauer; Otto Rienhoff; Petra Knaup-Gregori; Udo Bavendiek; Christoph Dieterich; Benedikt Brors; Inga Kraus; Caroline Marieken Thoms; Dirk Jäger; Volker Ellenrieder; Björn Bergh; Ramin Yahyapour; Roland Eils; HiGHmed Consortium; Michael Marschollek
Journal:  Methods Inf Med       Date:  2018-07-17       Impact factor: 2.176
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