2023
The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era
Wen A, He H, Fu S, Liu S, Miller K, Wang L, Roberts K, Bedrick S, Hersh W, Liu H. The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era. Npj Digital Medicine 2023, 6: 132. PMID: 37479735, PMCID: PMC10362064, DOI: 10.1038/s41746-023-00878-9.Peer-Reviewed Original ResearchAlgorithm development processEase of adoptionDigital health applicationsSoftware applicationsPhenotyping tasksExample implementationInterested usersReusable toolsManual abstractionSuch tasksDevelopment processHealth applicationsDigital eraFoundational requirementCost requirementsTaskImplementationSilico meansFrameworkPotential solutionsRequirementsUsersDatasetAbstractionApplications
2019
Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Brugnone N, Gonopolskiy A, Moyle MW, Kuchroo M, van Dijk D, Moon KR, Colon-Ramos D, Wolf G, Hirn MJ, Krishnaswamy S. Coarse Graining of Data via Inhomogeneous Diffusion Condensation. 2024 IEEE International Conference On Big Data (BigData) 2019, 00: 2624-2633. PMID: 32747879, PMCID: PMC7398322, DOI: 10.1109/bigdata47090.2019.9006013.Peer-Reviewed Original Research
2015
Reverse Engineering Measures of Clinical Care Quality: Sequential Pattern Mining
Chiu H, Meeker D. Reverse Engineering Measures of Clinical Care Quality: Sequential Pattern Mining. Lecture Notes In Computer Science 2015, 9162: 208-222. DOI: 10.1007/978-3-319-21843-4_17.Peer-Reviewed Original ResearchPattern miningTraditional classification algorithmsSequential pattern miningClassification problemClassification algorithmsData-driven measuresFacilitate discoveryMiningClinical dataTraining signalsCritical patternsFrameworkPatterns of treatmentClinical care qualityAlgorithmAbstractionClinical guidelinesClinical careBasis of outcomesSurvival analysisCare qualityAccuracyDataOutcomesQuality
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply