2024
Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records
Li Z, Lan L, Zhou Y, Li R, Chavin K, Xu H, Li L, Shih D, Zheng W. Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records. Journal Of Biomedical Informatics 2024, 152: 104626. PMID: 38521180, DOI: 10.1016/j.jbi.2024.104626.Peer-Reviewed Original ResearchDeep learning modelsElectronic health recordsHCC risk predictionHealth recordsTime-varying covariatesLearning modelsElectronic health record dataRisk predictionHealth record dataAccuracy of deep learning modelsDeep learning-based strategyCovariate imbalanceDisease prediction tasksLearning-based strategyDeep learning performanceDisease risk predictionEHR databaseClassification problemLength of follow-upTransfer learningFatty liver diseasePrediction taskCarcinoma riskModel trainingRecord data
2020
Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies
Rasmy L, Tiryaki F, Zhou Y, Xiang Y, Tao C, Xu H, Zhi D. Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies. Journal Of The American Medical Informatics Association 2020, 27: 1593-1599. PMID: 32930711, PMCID: PMC7647355, DOI: 10.1093/jamia/ocaa180.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemRecurrent neural networkNeural networkPrediction performanceLogistic regressionPredictive modelingDeep learningData aggregationElectronic health record dataMachine learningRisk predictionBetter prediction performanceDengue hemorrhagic feverHealth record dataEHR dataCancer predictionLarge vocabularyDifferent tasksPredictive modelHeart failureDiabetes patientsPancreatic cancerClinical dataHemorrhagic feverICD-9