Literature DB >> 23920919

Text mining electronic health records to identify hospital adverse events.

Lars Ulrik Gerdes1, Christian Hardahl.   

Abstract

Manual reviews of health records to identify possible adverse events are time consuming. We are developing a method based on natural language processing to quickly search electronic health records for common triggers and adverse events. Our results agree fairly well with those obtained using manual reviews, and we therefore believe that it is possible to develop automatic tools for monitoring aspects of patient safety.

Entities:  

Mesh：

Year:  2013        PMID： 23920919

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN： 0926-9630


  7 in total

Review 1.  Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.

Authors:  Yuan Luo; William K Thompson; Timothy M Herr; Zexian Zeng; Mark A Berendsen; Siddhartha R Jonnalagadda; Matthew B Carson; Justin Starren
Journal:  Drug Saf       Date:  2017-11       Impact factor: 5.606

2.  Comparison of a Voluntary Safety Reporting System to a Global Trigger Tool for Identifying Adverse Events in an Oncology Population.

Authors:  Lipika Samal; Srijesa Khasnabish; Cathy Foskett; Katherine Zigmont; Arild Faxvaag; Frank Chang; Marsha Clements; Sarah Collins Rossetti; Anuj K Dalal; Kathleen Leone; Stuart Lipsitz; Anthony Massaro; Ronen Rozenblum; Kumiko O Schnock; Catherine Yoon; David W Bates; Patricia C Dykes
Journal:  J Patient Saf       Date:  2022-07-21       Impact factor: 2.243

3.  Analysis of free text in electronic health records for identification of cancer patient trajectories.

Authors:  Kasper Jensen; Cristina Soguero-Ruiz; Karl Oyvind Mikalsen; Rolv-Ole Lindsetmo; Irene Kouskoumvekaki; Mark Girolami; Stein Olav Skrovseth; Knut Magne Augestad
Journal:  Sci Rep       Date:  2017-04-07       Impact factor: 4.379

4.  What Can We Learn about Fall Risk Factors from EHR Nursing Notes? A Text Mining Study.

Authors:  Ragnhildur I Bjarnadottir; Robert J Lucero
Journal:  EGEMS (Wash DC)       Date:  2018-09-20

5.  Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review.

Authors:  Sarah N Musy; Dietmar Ausserhofer; René Schwendimann; Hans Ulrich Rothen; Marie-Madlen Jeitziner; Anne Ws Rutjes; Michael Simon
Journal:  J Med Internet Res       Date:  2018-05-30       Impact factor: 5.428

6.  Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study.

Authors:  Jens Kjølseth Møller; Martin Sørensen; Christian Hardahl
Journal:  PLoS One       Date:  2021-03-31       Impact factor: 3.240

7.  A Systematic Review of Methods for Medical Record Analysis to Detect Adverse Events in Hospitalized Patients.

Authors:  Dorthe O Klein; Roger J M W Rennenberg; Richard P Koopmans; Martin H Prins
Journal:  J Patient Saf       Date:  2021-12-01       Impact factor: 2.243
  7 in total

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