2023
An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)
Liu S, Wen A, Wang L, He H, Fu S, Miller R, Williams A, Harris D, Kavuluru R, Liu M, Abu-el-Rub N, Schutte D, Zhang R, Rouhizadeh M, Osborne J, He Y, Topaloglu U, Hong S, Saltz J, Schaffter T, Pfaff E, Chute C, Duong T, Haendel M, Fuentes R, Szolovits P, Xu H, Liu H. An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C). Journal Of The American Medical Informatics Association 2023, 30: 2036-2040. PMID: 37555837, PMCID: PMC10654844, DOI: 10.1093/jamia/ocad134.Peer-Reviewed Original ResearchConceptsNatural language processingNLP modelsClinical natural language processingNatural language processing frameworkEHR-based clinical researchMulti-site settingSymptom extractionProcessing frameworkNLP frameworkLanguage processingNLP solutionMulti-site dataAlgorithm robustnessMethodology advancementsResearch communityTranslational research communityNational COVID Cohort CollaborativeCase demonstrationProcess heterogeneityFrameworkAnnotationCOVID cohortThe 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
2021
A Living, Interactive Systematic Review and Network Meta-analysis of First-line Treatment of Metastatic Renal Cell Carcinoma
Riaz I, He H, Ryu A, Siddiqi R, Naqvi S, Yao Y, Husnain M, Narasimhulu D, Mathew J, Sipra Q, Vandvik P, Joseph R, Liu H, Wang Z, Herasevich V, Singh P, Hussain S, Ho T, Bryce A, Pagliaro L, Murad M, Costello B. A Living, Interactive Systematic Review and Network Meta-analysis of First-line Treatment of Metastatic Renal Cell Carcinoma. European Urology 2021, 80: 712-723. PMID: 33824031, DOI: 10.1016/j.eururo.2021.03.016.Peer-Reviewed Original ResearchConceptsInteractive tableGraphical user interfaceUser interfaceUser-friendly formatAdvanced programmingDisplay resultsInteractive mannerInteractive reviewNetworkSearch strategyCurrent approachesSparse direct evidenceMetastatic renal cell carcinomaData extractionTableFirst-line treatmentProgrammingUntreated metastatic renal cell carcinomaFormatMultiple treatment optionsRenal cell carcinomaInherent biasesAdverse eventsComplete responseFrameworkA framework for living evidence synthesis in cancer: Living, interactive network meta-analysis for first-line treatment of metastatic renal cell carcinoma (mRCC).
Riaz I, He H, Ryu A, Siddiqi R, Naqvi S, Yao Y, Husnain M, Maheswari D, Sipra Q, Montori V, Joseph R, Liu H, Wang Z, Herasevich V, Singh P, Ho T, Bryce A, Pagliaro L, Murad M, Costello B. A framework for living evidence synthesis in cancer: Living, interactive network meta-analysis for first-line treatment of metastatic renal cell carcinoma (mRCC). Journal Of Clinical Oncology 2021, 39: 335-335. DOI: 10.1200/jco.2021.39.6_suppl.335.Peer-Reviewed Original ResearchInteractive tableArtificial intelligenceJavaScript programming languageFive-layered architectureMetastatic renal cell carcinomaFirst-line metastatic renal cell carcinomaLine Metastatic Renal Cell CarcinomaProgramming languageProgression-free survivalDifferent teamsComplete responseOverall survivalSystematic reviewSearch strategyDynamic featuresNetworkEvidence synthesisInteractive networkFirst-line treatmentEvidence mapRenal cell carcinomaFrameworkShared-decision makingOverall responseBaseline characteristics
2019
Using Face Recognition to Detect “Ghost Writer” Cheating in Examination
He H, Zheng Q, Li R, Dong B. Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. Lecture Notes In Computer Science 2019, 11462: 389-397. DOI: 10.1007/978-3-030-23712-7_54.Peer-Reviewed Original ResearchPublic cloud servicesFace recognition servicesFeature extraction moduleFace recognition frameworkOpen source projectsFace recognition techniquesAccuracy of detectionCloud servicesRecognition serviceRecognition frameworkExtraction moduleSource projectsPrototype systemRecognition techniquesFace recognitionLayer architecturePublic dataOnline distance educationDistance educationServicesStudent identificationFrameworkArchitectureSystemFairness