Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care
Kidwai-Khan F, Rentsch C, Pulk R, Alcorn C, Brandt C, Justice A. Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care. Frontiers In Big Data 2022, 5: 1059088. PMID: 36458283, PMCID: PMC9705957, DOI: 10.3389/fdata.2022.1059088.Peer-Reviewed Original ResearchK-Nearest NeighborSupport vector machinePreventable adverse eventsDecision support mechanismAdverse eventsCurrent medicationsExtreme gradient boostingPredictive modelingSoftware interfaceMachine learningVeterans AffairsData integrationF1 scoreLarge integrated healthcare systemNearest NeighborPatient's current medicationsVector machineOutpatient clinic visitsRandom forestDecision treeGradient boostingAUC scoreComplex treatment decisionsEHR dataIntegrated healthcare systemLong COVID burden and risk factors in 10 UK longitudinal studies and electronic health records
Thompson EJ, Williams DM, Walker AJ, Mitchell RE, Niedzwiedz CL, Yang TC, Huggins CF, Kwong ASF, Silverwood RJ, Di Gessa G, Bowyer RCE, Northstone K, Hou B, Green MJ, Dodgeon B, Doores KJ, Duncan EL, Williams FMK, Steptoe A, Porteous D, McEachan R, Tomlinson L, Goldacre B, Patalay P, Ploubidis G, Katikireddi S, Tilling K, Rentsch C, Timpson N, Chaturvedi N, Steves C. Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records. Nature Communications 2022, 13: 3528. PMID: 35764621, PMCID: PMC9240035, DOI: 10.1038/s41467-022-30836-0.Peer-Reviewed Original ResearchConceptsLong COVIDRisk factorsCardio-metabolic parametersOverweight/obesityElectronic healthcare recordsCOVID-19Community-based individualsSelf-reported COVID-19Electronic health recordsLongitudinal study sampleProlonged symptomsCOVID-19 casesFemale sexDiagnostic codesWhite ethnicityUK longitudinal studyMental healthHealth recordsStudy sampleEHR dataLongitudinal studySymptomsHealthcare recordsSpring 2021BurdenISPE‐Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID‐19: Opportunities and Trade‐Offs
Sarri G, Bennett D, Debray T, Deruaz‐Luyet A, Gabarró M, Largent JA, Li X, Liu W, Lund JL, Moga DC, Gokhale M, Rentsch CT, Wen X, Yanover C, Ye Y, Yun H, Zullo AR, Lin KJ. ISPE‐Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID‐19: Opportunities and Trade‐Offs. Clinical Pharmacology & Therapeutics 2022, 112: 990-999. PMID: 35170021, PMCID: PMC9087010, DOI: 10.1002/cpt.2560.Peer-Reviewed Original ResearchConceptsComparative effectiveness researchElectronic health recordsRoutine careCOVID-19Health recordsCoronavirus disease 2019 (COVID-19) pandemicLong-term treatment effectsEHR dataEffectiveness researchDisease 2019 pandemicRigorous study designsCOVID-19-related questionsAscertainment of outcomesVaccine effectivenessComplex patientsExposure statusHealthcare databasesTherapeutic interventionsOptimal managementHealthcare professionalsClinical researchStudy designTreatment effectsAppropriate statistical methodsInternational Society