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Pharmacovigilance using Clinical Text.

Lependu P, Iyer SV, Bauer-Mehren A, Harpaz R, Ghebremariam YT, Cooke JP, Shah NH - AMIA Jt Summits Transl Sci Proc (2013)

Bottom Line: The current state of the art in post-marketing drug surveillance utilizes voluntarily submitted reports of suspected adverse drug reactions.We present data mining methods that transform unstructured patient notes taken by doctors, nurses and other clinicians into a de-identified, temporally ordered, patient-feature matrix using standardized medical terminologies.We demonstrate how to use the resulting high-throughput data to monitor for adverse drug events based on the clinical notes in the EHR.

View Article: PubMed Central - PubMed

Affiliation: Stanford University, Stanford, CA.

ABSTRACT
The current state of the art in post-marketing drug surveillance utilizes voluntarily submitted reports of suspected adverse drug reactions. We present data mining methods that transform unstructured patient notes taken by doctors, nurses and other clinicians into a de-identified, temporally ordered, patient-feature matrix using standardized medical terminologies. We demonstrate how to use the resulting high-throughput data to monitor for adverse drug events based on the clinical notes in the EHR.

No MeSH data available.


Related in: MedlinePlus

Odds for myocardial infarction (MI) given proton pump inhibitor use: The x-axis is the OR. The plots also show the number of patients exposed (exp) and affected (aff) by each drug when testing for the strength of association with MI. Risks given PPIs are calculated using unadjusted methods (grey), adjusting for exposure propensity (PSM, blue), as well as by stratification on patients not taking clopidogrel (red). The size of the dot is proportional to the number of exposed patients and the lines on either side show the 95% confidence interval. We can see that adjusting using propensity score matching results in a slight reduction in risk estimates, but the overall risk trend remains. The risk estimate is higher in patients not taking clopidogrel. Details of each plot as well as comparative risks between each drug in each class, e.g., pantoprazole versus rabeprazole, can be found in Supplementary Material: PPI tables.
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f1-amia_tbi_2013_109: Odds for myocardial infarction (MI) given proton pump inhibitor use: The x-axis is the OR. The plots also show the number of patients exposed (exp) and affected (aff) by each drug when testing for the strength of association with MI. Risks given PPIs are calculated using unadjusted methods (grey), adjusting for exposure propensity (PSM, blue), as well as by stratification on patients not taking clopidogrel (red). The size of the dot is proportional to the number of exposed patients and the lines on either side show the 95% confidence interval. We can see that adjusting using propensity score matching results in a slight reduction in risk estimates, but the overall risk trend remains. The risk estimate is higher in patients not taking clopidogrel. Details of each plot as well as comparative risks between each drug in each class, e.g., pantoprazole versus rabeprazole, can be found in Supplementary Material: PPI tables.


Pharmacovigilance using Clinical Text.

Lependu P, Iyer SV, Bauer-Mehren A, Harpaz R, Ghebremariam YT, Cooke JP, Shah NH - AMIA Jt Summits Transl Sci Proc (2013)

Odds for myocardial infarction (MI) given proton pump inhibitor use: The x-axis is the OR. The plots also show the number of patients exposed (exp) and affected (aff) by each drug when testing for the strength of association with MI. Risks given PPIs are calculated using unadjusted methods (grey), adjusting for exposure propensity (PSM, blue), as well as by stratification on patients not taking clopidogrel (red). The size of the dot is proportional to the number of exposed patients and the lines on either side show the 95% confidence interval. We can see that adjusting using propensity score matching results in a slight reduction in risk estimates, but the overall risk trend remains. The risk estimate is higher in patients not taking clopidogrel. Details of each plot as well as comparative risks between each drug in each class, e.g., pantoprazole versus rabeprazole, can be found in Supplementary Material: PPI tables.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814501&req=5

f1-amia_tbi_2013_109: Odds for myocardial infarction (MI) given proton pump inhibitor use: The x-axis is the OR. The plots also show the number of patients exposed (exp) and affected (aff) by each drug when testing for the strength of association with MI. Risks given PPIs are calculated using unadjusted methods (grey), adjusting for exposure propensity (PSM, blue), as well as by stratification on patients not taking clopidogrel (red). The size of the dot is proportional to the number of exposed patients and the lines on either side show the 95% confidence interval. We can see that adjusting using propensity score matching results in a slight reduction in risk estimates, but the overall risk trend remains. The risk estimate is higher in patients not taking clopidogrel. Details of each plot as well as comparative risks between each drug in each class, e.g., pantoprazole versus rabeprazole, can be found in Supplementary Material: PPI tables.
Bottom Line: The current state of the art in post-marketing drug surveillance utilizes voluntarily submitted reports of suspected adverse drug reactions.We present data mining methods that transform unstructured patient notes taken by doctors, nurses and other clinicians into a de-identified, temporally ordered, patient-feature matrix using standardized medical terminologies.We demonstrate how to use the resulting high-throughput data to monitor for adverse drug events based on the clinical notes in the EHR.

View Article: PubMed Central - PubMed

Affiliation: Stanford University, Stanford, CA.

ABSTRACT
The current state of the art in post-marketing drug surveillance utilizes voluntarily submitted reports of suspected adverse drug reactions. We present data mining methods that transform unstructured patient notes taken by doctors, nurses and other clinicians into a de-identified, temporally ordered, patient-feature matrix using standardized medical terminologies. We demonstrate how to use the resulting high-throughput data to monitor for adverse drug events based on the clinical notes in the EHR.

No MeSH data available.


Related in: MedlinePlus