Varada Khanna, MSPH
About
Research
Publications
2026
Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis
Li H, Khanna V, Apathy N, Holmgren A, Loza A, Melnick E. Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis. JAMIA Open 2026, 9: ooag018. PMID: 41767181, PMCID: PMC12936052, DOI: 10.1093/jamiaopen/ooag018.Peer-Reviewed Original ResearchThis study investigates electronic health record (EHR) use patterns among physicians, showing that timely inbox management and reduced after-hours work improve chart completion efficiency and patient visit volume.
2025
Author Correction: An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients
Chadaga K, Khanna V, Prabhu S, Sampathila N, Chadaga R, Umakanth S, Bhat D, Swathi K, Kamath R. Author Correction: An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients. Scientific Reports 2025, 15: 2841. PMID: 39843648, PMCID: PMC11754466, DOI: 10.1038/s41598-025-86494-x.Commentaries, Editorials and Letters
2024
An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients
Chadaga K, Khanna V, Prabhu S, Sampathila N, Chadaga R, Umakanth S, Bhat D, Swathi K, Kamath R. An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients. Scientific Reports 2024, 14: 24454. PMID: 39424647, PMCID: PMC11489819, DOI: 10.1038/s41598-024-75896-y.Peer-Reviewed Original ResearchConceptsBat algorithmSelf-adaptive bat algorithmBat Algorithm techniqueSupervised learning techniquesHybrid bat algorithmArtificial intelligence techniquesMachine Learning FrameworkLearning frameworkHyperparameter tuningClassification metricsIntelligence techniquesLearning techniquesFirefly algorithmMachine learningArtificial intelligenceBayesian optimizationSearch techniqueRandom searchAlgorithmic techniquesAlgorithmClassifierHyperparametersExplainabilityIntelligenceTechniqueExplainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers
Vivek Khanna V, Chadaga K, Sampathila N, Prabhu S, Chadaga P. R, Bhat D, K. S. S. Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers. Cogent Engineering 2024, 11: 2330266. DOI: 10.1080/23311916.2024.2330266.Peer-Reviewed Original ResearchClinical decision support systemsDecision support systemArtificial intelligenceMachine learningSynthetic minority oversampling techniqueAdvent of machine learningMachine learning architectureSupport systemMinority oversampling techniqueEdited Nearest NeighborModel-agnostic explanationsLearning architectureDiabetes detectionOversampling techniqueGestational diabetesEnsemble stackingFeature importanceSHapley Additive exPlanationsDiabetes mellitus predictionNearest neighborsData processingBest-performing modelsIntelligenceMedical fieldPrediction systemDemystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence
Chadaga K, Khanna V, Prabhu S, Sampathila N, Chadaga R, Palkar A. Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence. Journal Of Intelligent Systems 2024, 33: 20240077. DOI: 10.1515/jisys-2024-0077.Peer-Reviewed Original ResearchMagnetic resonance imagingMachine learningArtificial intelligenceSearch techniqueMultiple sclerosisSpinal cord magnetic resonance imagingSupervised machine learningHyperparameter tuning techniqueML classifiersOptimization search techniquesMutual informationBayesian optimizationCentral nervous systemMultiple sclerosis diagnosisOligoclonal bandsVaricella diseaseInitial symptomsClassifierResonance imagingNervous systemAbstract Multiple sclerosisDiagnosisIntelligence
2023
A decision support system for osteoporosis risk prediction using machine learning and explainable artificial intelligence
Khanna V, Chadaga K, Sampathila N, Chadaga R, Prabhu S, K S S, Jagdale A, Bhat D. A decision support system for osteoporosis risk prediction using machine learning and explainable artificial intelligence. Heliyon 2023, 9: e22456. PMID: 38144333, PMCID: PMC10746430, DOI: 10.1016/j.heliyon.2023.e22456.Peer-Reviewed Original ResearchMachine learningArtificial intelligenceForward feature selection algorithmFeature selection algorithmOpen-source datasetsDecision support systemMachine-learning frameworkHealthcare domainClassifier predictionsSelection algorithmSelection techniquesOsteoporosis risk predictionSupport systemPrecision outcomesIntelligenceDiagnostic decisionsMachineLearningClassifierAutomated screeningAlgorithmDatasetDecisionELI5A machine learning and explainable artificial intelligence triage-prediction system for COVID-19
Khanna V, Chadaga K, Sampathila N, Prabhu S, P. R. A machine learning and explainable artificial intelligence triage-prediction system for COVID-19. Decision Analytics Journal 2023, 7: 100246. PMCID: PMC10163946, DOI: 10.1016/j.dajour.2023.100246.Peer-Reviewed Original ResearchMachine learningState-of-the-artRandom forestLocal Interpretable Model-Agnostic ExplanationsConvolutional neural networkDeep learning algorithmsK-Nearest NeighborTree-based ensemble modelsSupport vector machineArtificial intelligence advancesModel-agnostic explanationsData-balancing techniquesArtificial intelligence toolsClassifier architectureHeterogeneous classifiersNaive BayesLearning algorithmsNeural networkIntelligence advancesK-nearestVector machineHealthcare diagnosisIntelligence toolsDecision treeLight GBMA Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
Khanna V, Chadaga K, Sampathila N, Prabhu S, Bhandage V, Hegde G. A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Applied System Innovation 2023, 6: 32. DOI: 10.3390/asi6020032.Peer-Reviewed Original ResearchDeep learningMachine learningArtificial intelligenceTree-based classifiersOpen-source datasetsMachine Learning FrameworkMachine learning toolsExplainable AIXAI techniquesLearning frameworkPolycystic ovary syndromeAI approachesEngineering domainOpen-sourceClassifierML modelsRandom forestXAIDetection of polycystic ovary syndromeLearning toolsLearningDeepDatasetArchitectureDecision-making
2022
Diagnosing COVID-19 using artificial intelligence: a comprehensive review
Khanna V, Chadaga K, Sampathila N, Prabhu S, Chadaga R, Umakanth S. Diagnosing COVID-19 using artificial intelligence: a comprehensive review. Network Modeling Analysis In Health Informatics And Bioinformatics 2022, 11: 25. DOI: 10.1007/s13721-022-00367-1.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsArtificial intelligenceState-of-the-art approachesState-of-the-artApplication of artificial intelligenceWorld Health OrganizationLife-threatening infectionsFalse-negative resultsReverse transcription polymerase chain reactionTranscription polymerase chain reactionSevere prognosisCT scanDetection of coronavirusPolymerase chain reactionClinical markers