Literature DB >> 19745236

Detection of adverse drug events: proposal of a data model.

Emmanuel Chazard1, Béatrice Merlin, Grégoire Ficheur, Jean-Charles Sarfati, Régis Beuscart.   

Abstract

Our main objective is to detect adverse drug events (ADEs) in former hospital stays. As ADEs are rare, that supposes to screen thousands of electronic health records (EHRs). For that purpose, we need to define a data model that has two main objectives: (1) being able to describe hospital stays from various hospitals (2) being tuned so as to prepare the data mining process: as ADEs are not flagged in the datasets, the data model must be optimized for ADE detection. The article presents the phases of the design and the data model that results from this work. It is compatible with many hospitals. It deals with diagnoses, drug prescriptions, lab results and administrative information. It allows for data mining and ADE detection in EHRs.

Mesh:

Year:  2009        PMID: 19745236

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


  4 in total

1.  Clinical evaluation of the ADE scorecards as a decision support tool for adverse drug event analysis and medication safety management.

Authors:  Werner O Hackl; Elske Ammenwerth; Romaric Marcilly; Emmanuel Chazard; Michel Luyckx; Pascale Leurs; Regis Beuscart
Journal:  Br J Clin Pharmacol       Date:  2013-09       Impact factor: 4.335

2.  Constructing Clinical Decision Support Systems for Adverse Drug Event Prevention: A Knowledge-based Approach.

Authors:  Vassilis Koutkias; Vassilis Kilintzis; George Stalidis; Katerina Lazou; Chrysa Collyda; Emmanuel Chazard; Peter McNair; Regis Beuscart; Nicos Maglaveras
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

3.  Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases.

Authors:  Grégoire Ficheur; Emmanuel Chazard; Jean-Baptiste Beuscart; Béatrice Merlin; Michel Luyckx; Régis Beuscart
Journal:  BMC Med Inform Decis Mak       Date:  2014-09-12       Impact factor: 2.796

4.  A Personalized and Learning Approach for Identifying Drugs with Adverse Events.

Authors:  Sug Kyun Shin; Ho Hur; Eun Kyung Cheon; Ock Hee Oh; Jeong Seon Lee; Woo Jin Ko; Beom Seok Kim; YoungOk Kwon
Journal:  Yonsei Med J       Date:  2017-11       Impact factor: 2.759

  4 in total

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