2024
Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study
Yang R, Zeng Q, You K, Qiao Y, Huang L, Hsieh C, Rosand B, Goldwasser J, Dave A, Keenan T, Ke Y, Hong C, Liu N, Chew E, Radev D, Lu Z, Xu H, Chen Q, Li I. Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study. Journal Of Medical Internet Research 2024, 26: e60601. PMID: 39361955, PMCID: PMC11487205, DOI: 10.2196/60601.Peer-Reviewed Original ResearchConceptsNatural language processingNatural language processing toolkitQuestion-answering taskLanguage modelText generationText processingDomain-specific language modelsNatural language processing functionsMinimal programming expertiseText generation tasksMedical knowledge graphMachine translation tasksROUGE-L scoreDomain-specific challengesAll-in-one solutionROUGE-LText summarizationBLEU scoreKnowledge graphMachine translationUnstructured textQuestion-answeringHugging FaceProcessing toolkitLanguage processingExtracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition
Zuo X, Kumar A, Shen S, Li J, Cong G, Jin E, Chen Q, Warner J, Yang P, Xu H. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition. JCO Clinical Cancer Informatics 2024, 8: e2300166. PMID: 38885475, PMCID: PMC12032536, DOI: 10.1200/cci.23.00166.Peer-Reviewed Original ResearchConceptsNatural language processingDomain-specific language modelsNatural language processing systemsInformation extraction systemRule-based moduleNarrative clinical textsNLP tasksEntity recognitionText normalizationAssertion classificationLanguage modelInformation extractionClinical textElectronic health recordsLearning-basedClinical notesLanguage processingTest setSystem performanceHealth recordsResponse extractionTime-consumingAnticancer therapyInformationAssessment information
2023
Synthetic Histopathological Images Generation with Artificial Intelligence to Accelerate Research and Improve Clinical Outcomes in Hematology
Asti G, D'Amico S, Curti N, Carlini G, Sauta E, Derus N, Dall'Olio D, Sala C, Dall'Olio L, Lanino L, Maggioni G, Campagna A, Ubezio M, Russo A, Todisco G, Tentori C, Morandini P, Bicchieri M, Grondelli M, Zampini M, Savevski V, Santoro A, Kordasti S, Santini V, Kubasch A, Platzbecker U, Diez-Campelo M, Fenaux P, Zhao L, Zeidan A, Haferlach T, Castellani G, Della Porta M. Synthetic Histopathological Images Generation with Artificial Intelligence to Accelerate Research and Improve Clinical Outcomes in Hematology. Blood 2023, 142: 902. DOI: 10.1182/blood-2023-187521.Peer-Reviewed Original ResearchData augmentationArtificial intelligenceSynthetic imagesTextual inputGenerative modelDomain-specific language modelsAvailable textual dataSynthetic data augmentationEffective data augmentationFeatures of imagesReal-world datasetsSynthetic data generationReal-world imagesSpecific language modelHistological imagesUse casesImage featuresData sharingMultimodal dataImage generationTextual dataLayers of informationLanguage modelMultimodal informationClassification performance
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