Development of a Machine Learning Model and Mobile Application to Aid in Predicting Dosage of Vitamin K Antagonists Among Indian Patients
This work addresses the challenge of optimizing warfarin dosing for Indian patients to prevent life-threatening side effects, but it is incremental as it applies an existing method to a new dataset.
The researchers developed a Support Vector Machine regression model to predict the maintenance dosage of warfarin for Indian patients with stable INR values, using medical data from 109 patients in Kerala.
Patients who undergo mechanical heart valve replacements or have conditions like Atrial Fibrillation have to take Vitamin K Antagonists (VKA) drugs to prevent coagulation of blood. These drugs have narrow therapeutic range and need to be very closely monitored due to life threatening side effects. The dosage of VKA drug is determined and revised by a physician based on Prothrombin Time - International Normalised Ratio (PT-INR) value obtained through a blood test. Our work aimed at predicting the maintenance dosage of warfarin, the present most widely recommended anticoagulant drug, using the de-identified medical data collected from 109 patients from Kerala. A Support Vector Machine (SVM) Regression model was built to predict the maintenance dosage of warfarin, for patients who have been undergoing treatment from a physician and have reached stable INR values between 2.0 and 4.0.