חיפוש מתקדם
Israel Medical Association Journal
Solomon, I., Department of Ophthalmology, Rabin Medical Center, Petah Tiqva, Israel, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
Maharshak, N., Department of Internal Medicine T, Sourasky Tel Aviv Medical Center, Tel Aviv, Israel, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
Chechik, G., School of Mathematical Sciences, Sourasky Tel Aviv University, Ramat Aviv, Israel
Leibovici, L., Department of Internal Medicine H, Rabin Medical Center, Petah Tiqva, Israel, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
Lubetsky, A., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Halkin, H., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Ezra, D., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Ash, N., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Background: Oral anticoagulation with warfarin can lead to life-threatening events as a result of either over-anticoagulation or undertreatment. One of the main contributors to an undesirable warfarin effect is the need to adjust its daily dose for a specific patient. The dose is adjusted empirically based on the experience of the clinician, a method that is often imprecise. There is currently no other well-accepted method for predicting the maintenance dose of warfarin. Objective: To describe the application of an artificial neural network to the problem of warfarin maintenance dose prediction. Methods: We designed a neural network that predicts the maintenance dose of warfarin. Data on 148 patients attending a large anticoagulant clinic were collected by file review. Using correlational analysis of the patients' data we selected the best input variables. The network was trained by using the back-propagation algorithm on a subset of our data and the results were validated against the rest of the data. We used a multivariate linear regression to create a comparable model. Results: The neural network generated reasonable predictions of the maintenance dose (r = 0.823). The results of the linear regression model were similar (r = 0.800). Conclusion: Neural networks can be applied successfully for warfarin maintenance dose prediction. The results are promising, but further investigation is needed.
פותח על ידי קלירמאש פתרונות בע"מ -
הספר "אוצר וולקני"
אודות
תנאי שימוש
Applying an artificial neural network to warfarin maintenance dose prediction
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Solomon, I., Department of Ophthalmology, Rabin Medical Center, Petah Tiqva, Israel, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
Maharshak, N., Department of Internal Medicine T, Sourasky Tel Aviv Medical Center, Tel Aviv, Israel, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
Chechik, G., School of Mathematical Sciences, Sourasky Tel Aviv University, Ramat Aviv, Israel
Leibovici, L., Department of Internal Medicine H, Rabin Medical Center, Petah Tiqva, Israel, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
Lubetsky, A., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Halkin, H., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Ezra, D., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Ash, N., Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel, Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer 52621, Israel
Applying an artificial neural network to warfarin maintenance dose prediction
Background: Oral anticoagulation with warfarin can lead to life-threatening events as a result of either over-anticoagulation or undertreatment. One of the main contributors to an undesirable warfarin effect is the need to adjust its daily dose for a specific patient. The dose is adjusted empirically based on the experience of the clinician, a method that is often imprecise. There is currently no other well-accepted method for predicting the maintenance dose of warfarin. Objective: To describe the application of an artificial neural network to the problem of warfarin maintenance dose prediction. Methods: We designed a neural network that predicts the maintenance dose of warfarin. Data on 148 patients attending a large anticoagulant clinic were collected by file review. Using correlational analysis of the patients' data we selected the best input variables. The network was trained by using the back-propagation algorithm on a subset of our data and the results were validated against the rest of the data. We used a multivariate linear regression to create a comparable model. Results: The neural network generated reasonable predictions of the maintenance dose (r = 0.823). The results of the linear regression model were similar (r = 0.800). Conclusion: Neural networks can be applied successfully for warfarin maintenance dose prediction. The results are promising, but further investigation is needed.
Scientific Publication
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