Matches in SemOpenAlex for { <https://semopenalex.org/work/W2997473456> ?p ?o ?g. }
Showing items 1 to 64 of
64
with 100 items per page.
- W2997473456 abstract "Introduction: Artificial intelligence (AI) research within medicine is growing rapidly. AI is poised to transform medical practice. AI has been studied in several areas of healthcare and medical practice, including diagnosing, treating and caring of patients. Warfarin is one of the most commonly prescribed oral anticoagulant. Among all anticoagulants, warfarin has long been listed among the top ten drugs causing adverse drug events. Due to narrow therapeutic range and significant side effects, warfarin dosage determination becomes a challenging task in clinical practice. The purpose of this study was to determine exact dose of warfarin needed for patients with artificial heart valve using artificial neural networks (ANN).Development: To achieved the best model, some multi-layer perceptron ANNs were constructed with different structures. The dataset used included 846 patients who had been referred to the PT clinic in Tehran heart center in the second six months of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated and used for prediction system developments. In this paper the implementation of ANNs and proposed system in MatLab environment are described.Application: The effectiveness of ANNs were evaluated in terms of classification performance using 10fold cross-validation procedure and the results showed that the best model is a network that has 7 neurons in its hidden layer with an average absolute error of 0.1, disturbance rate of 0.33 and regression of 0.87. Conclusion: The achieved results reveal that ANN-based system is a suitable tool for warfarin dose prediction in Iranian patients with an artificial heartvalve. However, no system can be guaranteed to achieve 100% accuracy, but using such methods can reduce medical errors and thereby improve health care and patient safety." @default.
- W2997473456 created "2020-01-10" @default.
- W2997473456 creator A5009488392 @default.
- W2997473456 creator A5012917940 @default.
- W2997473456 creator A5017026669 @default.
- W2997473456 date "2019-12-24" @default.
- W2997473456 modified "2023-09-24" @default.
- W2997473456 title "Developing an Intelligent System for Prediction of Optimal Dose of Warfarin in Iranian Adult Patients with Artificial Heart Valve" @default.
- W2997473456 doi "https://doi.org/10.30699/fhi.v8i1.213" @default.
- W2997473456 hasPublicationYear "2019" @default.
- W2997473456 type Work @default.
- W2997473456 sameAs 2997473456 @default.
- W2997473456 citedByCount "0" @default.
- W2997473456 crossrefType "journal-article" @default.
- W2997473456 hasAuthorship W2997473456A5009488392 @default.
- W2997473456 hasAuthorship W2997473456A5012917940 @default.
- W2997473456 hasAuthorship W2997473456A5017026669 @default.
- W2997473456 hasBestOaLocation W29974734561 @default.
- W2997473456 hasConcept C105795698 @default.
- W2997473456 hasConcept C119857082 @default.
- W2997473456 hasConcept C139945424 @default.
- W2997473456 hasConcept C154945302 @default.
- W2997473456 hasConcept C164705383 @default.
- W2997473456 hasConcept C179717631 @default.
- W2997473456 hasConcept C188154048 @default.
- W2997473456 hasConcept C2776301958 @default.
- W2997473456 hasConcept C2779161974 @default.
- W2997473456 hasConcept C33923547 @default.
- W2997473456 hasConcept C41008148 @default.
- W2997473456 hasConcept C50644808 @default.
- W2997473456 hasConcept C60908668 @default.
- W2997473456 hasConcept C71924100 @default.
- W2997473456 hasConceptScore W2997473456C105795698 @default.
- W2997473456 hasConceptScore W2997473456C119857082 @default.
- W2997473456 hasConceptScore W2997473456C139945424 @default.
- W2997473456 hasConceptScore W2997473456C154945302 @default.
- W2997473456 hasConceptScore W2997473456C164705383 @default.
- W2997473456 hasConceptScore W2997473456C179717631 @default.
- W2997473456 hasConceptScore W2997473456C188154048 @default.
- W2997473456 hasConceptScore W2997473456C2776301958 @default.
- W2997473456 hasConceptScore W2997473456C2779161974 @default.
- W2997473456 hasConceptScore W2997473456C33923547 @default.
- W2997473456 hasConceptScore W2997473456C41008148 @default.
- W2997473456 hasConceptScore W2997473456C50644808 @default.
- W2997473456 hasConceptScore W2997473456C60908668 @default.
- W2997473456 hasConceptScore W2997473456C71924100 @default.
- W2997473456 hasLocation W29974734561 @default.
- W2997473456 hasLocation W29974734562 @default.
- W2997473456 hasOpenAccess W2997473456 @default.
- W2997473456 hasPrimaryLocation W29974734561 @default.
- W2997473456 hasRelatedWork W1489969923 @default.
- W2997473456 hasRelatedWork W2091943352 @default.
- W2997473456 hasRelatedWork W2749461815 @default.
- W2997473456 hasRelatedWork W2890929759 @default.
- W2997473456 hasRelatedWork W3185179407 @default.
- W2997473456 hasRelatedWork W4206558754 @default.
- W2997473456 hasRelatedWork W4220975826 @default.
- W2997473456 hasRelatedWork W4226023263 @default.
- W2997473456 hasRelatedWork W4231994957 @default.
- W2997473456 hasRelatedWork W4280611221 @default.
- W2997473456 isParatext "false" @default.
- W2997473456 isRetracted "false" @default.
- W2997473456 magId "2997473456" @default.
- W2997473456 workType "article" @default.