论文标题
开发机器学习模型和移动应用，以帮助预测印度患者维生素K拮抗剂的剂量
Development of a Machine Learning Model and Mobile Application to Aid in Predicting Dosage of Vitamin K Antagonists Among Indian Patients
论文作者
论文摘要
进行机械心脏瓣膜替代或具有房颤等状况的患者必须服用维生素K拮抗剂（VKA）药物以防止血液凝结。这些药物具有狭窄的治疗范围，由于威胁生命的副作用，需要密切监测。 VKA药物的剂量由医师基于凝血酶原时间 - 国际标准化比率（PT -INR）值确定和修订。我们的工作旨在使用从喀拉拉邦的109名患者收集的去鉴定的医疗数据来预测目前最广泛建议的抗凝药物的华法林的维持剂量。建立了一个支持向量机（SVM）回归模型，以预测华法林的维持剂量，该剂量是针对医生接受治疗并达到2.0至4.0之间稳定INR值的患者。
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.
