2024
A Natural Language Processing Study to Assess Quality of End-of-Life Care for Children with Cancer (RP215)
Lindsay M, De Oliveira S, Ward D, Sciacca K, Lindvall C, Ananth P. A Natural Language Processing Study to Assess Quality of End-of-Life Care for Children with Cancer (RP215). Journal Of Pain And Symptom Management 2024, 67: e763. DOI: 10.1016/j.jpainsymman.2024.02.425.Peer-Reviewed Original ResearchEnd-of-life careGoals of care discussionsEnd-of-lifePalliative care consultationLocation of deathProportion of decedentsNatural language processingCode status limitationsCancer decedentsHospice discussionCare discussionsCare consultationDocumented goals of care discussionsEnd-of-life care qualityEvidence-based process measuresKeyword libraryLanguage processingElectronic health recordsManual chart abstractionMonths of lifeQuality measuresRule-based natural language processingCare qualityNatural language processing technologyNatural language processing studies(121) Utilizing ChatGPT for Urology Trainee Simulation of Peyronie’s Disease Counseling
Reddy S, Smani S, Honig S, Harnisch B, Rotker K. (121) Utilizing ChatGPT for Urology Trainee Simulation of Peyronie’s Disease Counseling. The Journal Of Sexual Medicine 2024, 21: qdae001.115. DOI: 10.1093/jsxmed/qdae001.115.Peer-Reviewed Original ResearchSexual medicine specialistsMedicine specialistsDisease counselingTrained emergency medicine residentEmergency medicine residentsCohen's kappaSimulation-based educationInter-rater reliabilityAssessment of trainee performanceDelivering bad newsTrainee performanceSimulated clinical interactionsYear medical studentsOpen-ended questionsMedical student educationHigher competence levelsMedicine residentsAssessment of traineesPeyronie's diseaseNatural language processing technologyPatient interactionsClinical scenariosInitiate counselingTransgender healthLanguage processing technology
2023
Artificial Intelligence to Improve Patient Understanding of Radiology Reports
Amin K, Khosla P, Doshi R, Chheang S, Forman H. Artificial Intelligence to Improve Patient Understanding of Radiology Reports. The Yale Journal Of Biology And Medicine 2023, 96: 407-417. PMID: 37780992, PMCID: PMC10524809, DOI: 10.59249/nkoy5498.Peer-Reviewed Original ResearchConceptsLarge language modelsArtificial intelligenceNatural language processing technologyLanguage processing technologyPhone numberAI technologyRadiology reportsRadiologist workflowLanguage modelQuick accessProcessing technologyIntelligenceDiagnostic imaging reportsNascent technologyFocused solutionsTechnologySignificant researchTechnical detailsCentury Cures ActResearch purposesAccurate communicationImaging reportsTarget audienceWorkflowCures Act
2022
Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature
Schutte D, Vasilakes J, Bompelli A, Zhou Y, Fiszman M, Xu H, Kilicoglu H, Bishop J, Adam T, Zhang R. Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature. Journal Of Biomedical Informatics 2022, 131: 104120. PMID: 35709900, PMCID: PMC9335448, DOI: 10.1016/j.jbi.2022.104120.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemComprehensive knowledge graphDomain terminologyKnowledge graphSemantic relationsNatural language processing technologyLanguage processing technologyNLP toolsDownstream tasksF1 scoreSemantic relationshipsDiscovery patternsPubMed abstractsLimited coverageBiomedical literatureProcessing technologyLanguage systemSemRepDietary supplement informationManual reviewNovel methodologyGraphNodesDomainTask
2019
Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP
Soysal E, Warner J, Wang J, Jiang M, Harvey K, Jain S, Dong X, Song H, Siddhanamatha H, Wang L, Dai Q, Chen Q, Du X, Tao C, Yang P, Denny J, Liu H, Xu H. Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP. Studies In Health Technology And Informatics 2019, 264: 1041-1045. PMID: 31438083, PMCID: PMC7359882, DOI: 10.3233/shti190383.Peer-Reviewed Original ResearchConceptsElectronic health recordsNLP solutionNatural language processing technologyInformation extraction moduleLanguage processing technologyInformation extraction tasksUser-friendly interfaceBest F-measureInformation extractionExtraction moduleExtraction taskCustomizable modulesNLP systemsF-measureAcademic useHealth recordsComparable performanceProcessing technologyVanderbilt University Medical CenterModuleDiverse typesInformationNLPSubstantial effortSystem
2015
Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.
Wu Y, Jiang M, Lei J, Xu H. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. 2015, 216: 624-8. PMID: 26262126, PMCID: PMC4624324.Peer-Reviewed Original ResearchConceptsDeep neural networksLarge unlabeled corpusNamed Entity RecognitionWord embeddingsUnlabeled corpusUnsupervised learningEntity recognitionNeural networkNatural language processing technologyNovel deep learning methodLanguage processing technologyDeep learning methodsUnsupervised feature learningFeature engineering approachImportant healthcare informationChinese clinical textTypes of entitiesFeature learningNER taskClinical textLearning methodsClinical documentsCRF modelHealthcare informationFree text
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