ArticleBMC medical informatics and decision making2015
Non-redundant association rules between diseases and medications: an automated method for knowledge base construction.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications.PloS one · 2023Article
- A clinical text classification paradigm using weak supervision and deep representation.BMC medical informatics and decision making · 2019Article
- Association Rule Analysis for Validating Interrelationships of Combined Medication of Compound Kushen Injection in Treating Colon Carcinoma: A Hospital Information System-Based Real-World Study.Evidence-based complementary and alternative medicine : eCAM · 2018Article
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6 authors.
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Abstract
backgroundThe widespread use of electronic health records (EHRs) has generated massive clinical data storage. Association rules mining is a feasible technique to convert this large amount of data into usable knowledge for clinical decision making, research or billing. We present a data driven method to create a knowledge base linking medications to pathological conditions through their therapeutic indications from elements within the EHRs.
methodsAssociation rules were created from the data of patients hospitalised between May 2012 and May 2013 in the department of Cardiology at the University Hospital of Strasbourg. Medications were extracted from the medication list, and the pathological conditions were extracted from the discharge summaries using a natural language processing tool. Association rules were generated along with different interestingness measures: chi square, lift, conviction, dependency, novelty and satisfaction. All medication-disease pairs were compared to the Summary of Product Characteristics, which is the gold standard. A score based on the other interestingness measures was created to filter the best rules, and the indices were calculated for the different interestingness measures.
resultsAfter the evaluation against the gold standard, a list of accurate association rules was successfully retrieved. Dependency represents the best recall (0.76). Our score exhibited higher exactness (0.84) and precision (0.27) than all of the others interestingness measures. Further reductions in noise produced by this method must be performed to improve the classification precision.
conclusionsAssociation rules mining using the unstructured elements of the EHR is a feasible technique to identify clinically accurate associations between medications and pathological conditions.
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