arXiv 2004.114602020-04-19q-bio.QMcs.CYcs.LGstat.ML

开发机器学习模型和移动应用程序，以帮助预测印度患者中维生素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

National Institute of Technology Calicut（国家理工学院卡利卡特分校）；Sree Chitra Tirunal Institute for Medical Sciences and Technology（斯里·奇特拉·蒂鲁纳尔医学科学与技术研究所）

Amruthlal M, Devika S, Ameer Suhail P A, Aravind K Menon, Vignesh Krishnan, Alan Thomas, Manu Thomas, Sanjay G, Lakshmi Kanth L R, Jimmy Jose, Harikrishnan S

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摘要：

接受机械心脏瓣膜置换术或患有房颤等疾病的患者必须服用维生素K拮抗剂（VKA）药物以防止血液凝固。这些药物具有狭窄的治疗范围，并且由于危及生命的副作用而需要非常密切地监测。VKA药物的剂量由医生根据通过血液检查获得的凝血酶原时间-国际标准化比值（PT-INR）值确定和修订。我们的工作旨在预测华法林的维持剂量，目前最广泛推荐的抗凝药物，使用从喀拉拉邦109例患者中收集的去识别的医疗数据。建立支持向量机（SVM）回归模型，以预测华法林的维持剂量，用于接受医生治疗并达到稳定INR值2.0至4.0的患者。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。