Evidence map›Paper›PMID 36243102›Full record

ArticleIndian heart journal

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.

M Amruthlal, S Devika, Vignesh Krishnan, P A Ameer Suhail, Aravind K Menon, Alan Thomas, Manu Thomas, G Sanjay, L R Lakshmi Kanth, P Jeemon and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Indian heart journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Machine Learning for Warfarin Therapy: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2025
    Review
  4. Review
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

M AmruthlalDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India.
S DevikaDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India.
Vignesh KrishnanDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India.
P A Ameer SuhailDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India.
Aravind K MenonDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India.
Alan ThomasDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India.
Manu ThomasDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India.
G SanjaySree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India.
L R Lakshmi KanthSree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India.
P JeemonSree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India.
Jimmy JoseDepartment of Computer Science and Engineering, National Institute of Technology Calicut, India. Electronic address: jimmy@nitc.ac.in.
S HarikrishnanSree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India. Electronic address: drhari@sctimst.ac.in.
National Institute of Technology Calicut · INSree Chitra Thirunal Institute for Medical Sciences and Technology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients 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.

Indexed as

Mobile ApplicationsWarfarinAnticoagulantsArtificial IntelligenceFibrinolytic AgentsHeart ValvesHumansInternational Normalized RatioMachine LearningVitamin KAnticoagulantsFibrinolytic AgentsVitamin KWarfarinArtificial intelligenceAtrial fibrillationCardiac valve replacementInternational normalised ratio (PT-INR)Machine learningMechanical heart valveProthrombin timeSupport vector machine (SVM) regression algorithmVitamin K Antagonists (VKA)Warfarin

Identifiers

PMID36243102
PMCPMC9773288
OpenAlexW4304889621

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.