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    block this user Enrique Frias-Martinez Trusted member

    Senior Research Fellow

    Telefonica Research, Madrid, Spain

    Tracking medication information across medical records

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    A patients electronic medical record can consist of a large number of reports, especially for an elderly patient or for one affected by a chronic disease. It can thus be cumbersome for a physician to go through all of the reports to understand the patients complete medical history. This paper describes work in progress towards tracking medications and their dosages through the course of a patients medical history. 923 reports associated with 11 patients were obtained from a university hospital. Drug names were identified using a dictionary look-up approach. Dosages corresponding to these drugs were determined using regular expressions. The state of a drug (ON, OFF), which determines whether or not the drug was being taken, was identified using a support vector machine with features based on expert knowledge. Results were promising: prec. recall 87%. The output is a timeline display of the drugs which the patient has been taking.

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    Description

    Title : Tracking medication information across medical records
    Author(s) : Juan Eugenio Iglesias, Krupa Rocks, Neda Jahanshad, Enrique Frias-Martinez, Lewellyn P Andrada, Alex A T Bui
    Abstract : A patients electronic medical record can consist of a large number of reports, especially for an elderly patient or for one affected by a chronic disease. It can thus be cumbersome for a physician to go through all of the reports to understand the patients complete medical history. This paper describes work in progress towards tracking medications and their dosages through the course of a patients medical history. 923 reports associated with 11 patients were obtained from a university hospital. Drug names were identified using a dictionary look-up approach. Dosages corresponding to these drugs were determined using regular expressions. The state of a drug (ON, OFF), which determines whether or not the drug was being taken, was identified using a support vector machine with features based on expert knowledge. Results were promising: prec. recall 87%. The output is a timeline display of the drugs which the patient has been taking.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2009

    Affiliations Telefonica Research, Madrid, Spain
    Journal : AMIA Annual Symposium proceedings AMIA Symposium AMIA Symposium
    Volume : 2009
    Publisher : American Medical Informatics Association
    Pages : 266-270
    Url : http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2815368&tool=pmcentrez&rendertype=abstract

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