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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 3 of 3 Research Studies DisplayedMisra-Hebert AD, Milinovich A, Zajichek A
Natural language processing improves detection of nonsevere hypoglycemia in medical records versus coding alone in patients with type 2 diabetes but does not improve prediction of severe hypoglycemia events: an analysis using the electronic medical record
The purpose of this study was to determine if natural language processing (NLP) improves detection of non-severe hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH). The authors identified NSH events by diagnosis codes and NLP 2005 to 2017 and built an SH prediction model. Their findings showed that detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.
AHRQ-funded; HS024128.
Citation: Misra-Hebert AD, Milinovich A, Zajichek A .
Natural language processing improves detection of nonsevere hypoglycemia in medical records versus coding alone in patients with type 2 diabetes but does not improve prediction of severe hypoglycemia events: an analysis using the electronic medical record
Diabetes Care 2020 Aug;43(8):1937-40. doi: 10.2337/dc19-1791..
Keywords: Diabetes, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality
Chandakkar PS, Venkatesan R, Li B
MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images.
Computer-aided diagnosis has the potential of improving diabetic retinopathy (DR) screening or diagnosis. The researchers developed a feature space of a modified color correlogram appended with statistics of steerable Gaussian filter responses selected by fast radial symmetric transform points. Experiments with real DR images collected from five different datasets demonstrate that the proposed approach is able to outperform existing methods.
AHRQ-funded; HS019792.
Citation: Chandakkar PS, Venkatesan R, Li B .
MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images.
J Med Imaging 2017 Jul;4(3):034003. doi: 10.1117/1.jmi.4.3.034003.
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Keywords: Diabetes, Imaging, Eye Disease and Health, Health Information Technology (HIT), Diagnostic Safety and Quality
Lawrence JM, Black MH, Zhang JL
Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.
The researchers explored the utility of different algorithms for diabetes case identification by using electronic health records. They found that case identification accuracy was highest in 75% of bootstrapped samples for those who had 1 or more outpatient diabetes diagnoses or 1 or more insulin prescriptions and in 25% of samples for those who had 2 or more outpatient diabetes diagnoses and 1 or more antidiabetic medications.
AHRQ-funded; HS019859.
Citation: Lawrence JM, Black MH, Zhang JL .
Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.
Am J Epidemiol 2014 Jan;179(1):27-38. doi: 10.1093/aje/kwt230..
Keywords: Children/Adolescents, Diabetes, Chronic Conditions, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality