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- W2899018101 abstract "Objectives: The explosive increase in medical literature has changed therapeutic strategies, but it is challenging for physicians to keep their knowledge. Data mining on large-scale scientific literature can refresh knowledge to better improve the quality of disease treatment. Methods: This paper reports on a reformulated version of a data mining method called MedRank, which is a network- based algorithm that ranks therapy for a target disease based on the MEDLINE literature database. MedRank algorithm input for this study was a clear definition of the disease model; the algorithm output was the accurate recommendation of antihypertensive drugs. Hypertension with diabetes mellitus was chosen as the in- put disease model. The ranking output of antihypertensive drugs are based on the Joint National Committee (JNC) guidelines, one through eight, and the publication dates, ≤1977, ≤1980, ≤1984, ≤1988, ≤1993, ≤1997, ≤2003, and ≤2013. The McNemar's test was used to evaluate the efficacy of MedRank based on specific JNC guidelines. Results: The ranking order of antihypertensive drugs changed with the date of the published literature, and the MedRank algorithm drug recommendations had excellent consistency with the JNC guidelines in 2013 (P = 1.00 from McNemar's test, Kappa = 0.78, P = 1.00). Moreover, the Kappa index increased over time. Sensitivity was better than specificity for MedRank; in addition, sensitivity was maintained at a high level, and specificity increased from 1997 to 2013. Conclusion: The use of MedRank in ranking medical literature on hypertension with diabetes mellitus in our study suggests possible application in clinical practice; it is a potential method for supporting antihypertensive drug-prescription decisions." @default.
- W2899018101 created "2018-11-09" @default.
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- W2899018101 date "2018-10-01" @default.
- W2899018101 modified "2023-10-16" @default.
- W2899018101 title "A0976 Applying a “big data” literature system to recommend antihypertensive drugs for hypertension patients with diabetes mellitus" @default.
- W2899018101 doi "https://doi.org/10.1097/01.hjh.0000548454.13484.63" @default.
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