Development and validation of a mobile application based on a machine learning model to aid in predicting dosage of vitamin K antagonists among Indian patients post mechanical heart valve replacement

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dc.contributor.authorAmruthlal, M.en_US
dc.contributor.authorDevika, S.en_US
dc.contributor.authorKrishnan, Vigneshen_US
dc.contributor.authorSuhail, P.A. Ameeren_US
dc.contributor.authorMenon, Aravind K.en_US
dc.contributor.authorThomas, Alanen_US
dc.contributor.authorThomas, Manuen_US
dc.contributor.authorSanjay, G.en_US
dc.contributor.authorKanth, L.R. Lakshmien_US
dc.contributor.authorJeemon, P.en_US
dc.contributor.authorJose, Jimmyen_US
dc.contributor.authorHarikrishnan, S.en_US
dc.date.accessioned2023-07-21T11:36:56Z
dc.date.available2023-07-21T11:36:56Z
dc.date.issued2022-12
dc.description.abstractPatients who undergo heart valve replacements with mechanical valves need to take Vitamin K Antagonists (VKA) drugs (Warfarin, Nicoumalone) which has got a very narrow therapeutic range and needs very close monitoring using PT-INR. Accessibility to physicians to titrate drugs doses is a major problem in low-middle income countries (LMIC) like India. Our work was aimed at predicting the maintenance dosage of these drugs, using the de-identified medical data collected from patients attending an INR Clinic in South India. We used artificial intelligence (AI) - machine learning to develop the algorithm. A Support Vector Machine (SVM) regression model was built to predict the maintenance dosage of warfarin, who have stable INR values between 2.0 and 4.0. We developed a simple user friendly android mobile application for patients to use the algorithm to predict the doses. The algorithm generated drug doses in 1100 patients were compared to cardiologist prescribed doses and found to have an excellent correlation.en_US
dc.identifier.affiliationsDepartment of Computer Science and Engineering, National Institute of Technology Calicut, Indiaen_US
dc.identifier.affiliationsSree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Indiaen_US
dc.identifier.citationAmruthlal M., Devika S., Krishnan Vignesh, Suhail P.A. Ameer, Menon Aravind K., Thomas Alan, Thomas Manu, Sanjay G., Kanth L.R. Lakshmi, Jeemon P., Jose Jimmy, Harikrishnan S.. Development and validation of a mobile application based on a machine learning model to aid in predicting dosage of vitamin K antagonists among Indian patients post mechanical heart valve replacement. Indian Heart Journal. 2022 Dec; 74(6): 469-473en_US
dc.identifier.issn0019-4832
dc.identifier.issn2213-3763
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/220946
dc.languageenen_US
dc.publisherCardiological Society of Indiaen_US
dc.relation.issuenumber6en_US
dc.relation.volume74en_US
dc.source.urihttps://doi.org/10.1016/j.ihj.2022.10.002en_US
dc.subjectCardiac valve replacementen_US
dc.subjectMechanical heart valveen_US
dc.subjectAtrial fibrillationen_US
dc.subjectVitamin K Antagonists (VKA)en_US
dc.subjectProthrombin timeen_US
dc.subjectInternational normalised ratio (PT-INR)en_US
dc.subjectWarfarinen_US
dc.subjectSupport vector machine (SVM) regressionen_US
dc.subjectalgorithmen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.titleDevelopment and validation of a mobile application based on a machine learning model to aid in predicting dosage of vitamin K antagonists among Indian patients post mechanical heart valve replacementen_US
dc.typeJournal Articleen_US
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