2020
Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study
Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Wu S, Zheng W, Xu H, Zhi D, Zhang Y, Tao C. Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. Journal Of Medical Internet Research 2020, 22: e16981. PMID: 32735224, PMCID: PMC7428917, DOI: 10.2196/16981.Peer-Reviewed Original ResearchConceptsAttentive Neural NetworkAsthma exacerbationsRisk factorsNeural networkAdvanced deep learning modelsClinical variablesDeep learning modelsCerner Health Facts databaseLarge electronic health recordNeural network modelRetrospective cohort studyHealth Facts databasePotential risk factorsRisk factor analysisPersonalized risk factorsElectronic health recordsBaseline methodsLearning modelPersonalized risk scoreProgressive asthmaAsthma symptomsEsophageal refluxAdult patientsCohort studyTime-Sensitive
2019
Time-sensitive clinical concept embeddings learned from large electronic health records
Xiang Y, Xu J, Si Y, Li Z, Rasmy L, Zhou Y, Tiryaki F, Li F, Zhang Y, Wu Y, Jiang X, Zheng W, Zhi D, Tao C, Xu H. Time-sensitive clinical concept embeddings learned from large electronic health records. BMC Medical Informatics And Decision Making 2019, 19: 58. PMID: 30961579, PMCID: PMC6454598, DOI: 10.1186/s12911-019-0766-3.Peer-Reviewed Original ResearchConceptsConcept similarity measurePositive pointwise mutual informationConcept embeddingsSimilarity measurePredictive modeling tasksLarge electronic health recordTime-sensitive informationPointwise mutual informationImportant research areaDeep learningElectronic health recordsMedical domainLarge electronic health record databaseWord2vec embeddingsTemporal dependenciesLearning methodsFastText algorithmModeling tasksResultsOur experimentsExtrinsic evaluationIntrinsic evaluationMutual informationHealth recordsDistributional representationsEmbedding
2014
Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality
Xu H, Aldrich M, Chen Q, Liu H, Peterson N, Dai Q, Levy M, Shah A, Han X, Ruan X, Jiang M, Li Y, St Julien J, Warner J, Friedman C, Roden D, Denny J. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. Journal Of The American Medical Informatics Association 2014, 22: 179-191. PMID: 25053577, PMCID: PMC4433365, DOI: 10.1136/amiajnl-2014-002649.Peer-Reviewed Original ResearchConceptsType 2 diabetes patientsElectronic health recordsCancer patientsCancer mortalityDiabetes patientsEHR dataNon-diabetic cancer patientsCox proportional hazards modelDrug exposure informationOral hypoglycemic medicationsCharlson Comorbidity IndexNon-diabetic patientsUse of metforminCancer diagnosisHealth recordsSite-specific cancersBody mass indexProportional hazards modelVanderbilt University Medical CenterUniversity Medical CenterLarge electronic health recordHypoglycemic medicationsCause mortalityComorbidity indexInsulin use