2025
LITA: An Efficient LLM-Assisted Iterative Topic Augmentation Framework
Chang C, Tsai J, Tsai Y, Hwang S. LITA: An Efficient LLM-Assisted Iterative Topic Augmentation Framework. Lecture Notes In Computer Science 2025, 15870: 449-460. DOI: 10.1007/978-981-96-8170-9_35.Peer-Reviewed Original ResearchAugmentation frameworkClustering performance metricsText clusteringTopic qualityLanguage modelSeed wordsTopic modelsText corpusPerformance metricsBaseline modelIterative refinementAPIGuided approachLabor-intensiveFrameworkTraditional modelsThematic structureTextDatasetMetricsLDAIterationClustersCostModelA Generative Artificial Intelligence Copilot for Biomedical Nanoengineering
Wang Y, Song H, Teng Y, Huang G, Qian J, Wang H, Dong S, Ha J, Ma Y, Chang M, Jeong S, Deng W, Schrank B, Grippin A, Wu A, Edwards J, Zhang Y, Lin Y, Poon W, Wilhelm S, Bi Y, Teng L, Wang Z, Kim B, Jiang W. A Generative Artificial Intelligence Copilot for Biomedical Nanoengineering. ACS Nano 2025, 19: 19394-19407. PMID: 40367350, DOI: 10.1021/acsnano.5c03454.Peer-Reviewed Original ResearchConceptsArtificial intelligenceNatural language processing tasksExtract contextual informationLanguage processing tasksAutomatically extract knowledgeAI-based methodsGenerative artificial intelligenceInformation extractionLanguage modelAutomated learningContextual informationProcessing tasksIntelligent copilotBaseline modelAI toolsDesign tasksTaskQueryScientific queriesAutomaticallyCopilotIntelligenceScientific researchLLMLearningConditional Convolution of Clinical Data Embeddings for Multimodal Prostate Cancer Classification
Zhong J, Chen F, Chen L, Shung D, Onofrey J. Conditional Convolution of Clinical Data Embeddings for Multimodal Prostate Cancer Classification. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10981307.Peer-Reviewed Original ResearchConvolutional neural networkGleason scoreProstate cancerClinical dataMultiparametric magnetic resonance imagingPredicting Gleason scoreClinical informationCurrent deep learning approachesPatient clinical dataMagnetic resonance imagingDeep learning approachNon-invasive diagnosisAccurate risk predictionData embeddingCNN kernelsMRI scansConditional convolutionPublic datasetsResonance imagingNeural networkProstate cancer classificationData modalitiesLearning approachBaseline modelGS prediction accuracy
2024
Deep-Reticular Pseudodrusen-Net: A 3-Dimensional Deep Network for Detection of Reticular Pseudodrusen on OCT Scans
Elsawy A, Keenan T, Thavikulwat A, Lu A, Bellur S, Mukherjee S, Agron E, Chen Q, Chew E, Lu Z. Deep-Reticular Pseudodrusen-Net: A 3-Dimensional Deep Network for Detection of Reticular Pseudodrusen on OCT Scans. Ophthalmology Science 2024, 5: 100655. PMID: 39866344, PMCID: PMC11758204, DOI: 10.1016/j.xops.2024.100655.Peer-Reviewed Original ResearchSemi-supervised learningReticular pseudodrusenOCT scansRetina specialistsOptical coherence tomographyArea under ROC curveSpectral-domain optical coherence tomographyBaseline modelOptical coherence tomography scansAge-related macular degeneration studyDetect reticular pseudodrusenFundus autofluorescence imagingDeep learning networkDeep networksBaseline methodsPretrained modelsModel decision-makingReading centerLearning networkHigh-performance metricsOCT studiesTomography scanAREDS2En faceCoherence tomographyAugmenting biomedical named entity recognition with general-domain resources
Yin Y, Kim H, Xiao X, Wei C, Kang J, Lu Z, Xu H, Fang M, Chen Q. Augmenting biomedical named entity recognition with general-domain resources. Journal Of Biomedical Informatics 2024, 159: 104731. PMID: 39368529, DOI: 10.1016/j.jbi.2024.104731.Peer-Reviewed Original ResearchBioNER datasetsMulti-task learningNER datasetsEntity typesBiomedical datasetsBaseline modelGeneral domain datasetsBiomedical language modelNeural network-basedYield performance improvementsBioNER modelsEntity recognitionBiomedical corporaHuman annotatorsLabel ambiguityLanguage modelTransfer learningF1 scoreBioNERHuman effortNetwork-basedBiomedical resourcesPerformance improvementDatasetSuperior performance
2023
Improving model transferability for clinical note section classification models using continued pretraining
Zhou W, Yetisgen M, Afshar M, Gao Y, Savova G, Miller T. Improving model transferability for clinical note section classification models using continued pretraining. Journal Of The American Medical Informatics Association 2023, 31: 89-97. PMID: 37725927, PMCID: PMC10746297, DOI: 10.1093/jamia/ocad190.Peer-Reviewed Original ResearchConceptsClinical note sectionsIn-domainClassification modelNatural language processing tasksNeural network-based methodTemporal information extractionLanguage processing tasksDrop of accuracyBERT-based modelsNetwork-based methodsInformation extractionCross-domainModel transferabilityF1 scoreProcessing tasksSocial determinantsBaseline modelPretrainingClassificationImprove model transferabilityNotes sectionModel performanceAccuracyImproved modelDatasetIdentifying suicide documentation in clinical notes through zero‐shot learning
Workman T, Goulet J, Brandt C, Warren A, Eleazer J, Skanderson M, Lindemann L, Blosnich J, O'Leary J, Zeng‐Treitler Q. Identifying suicide documentation in clinical notes through zero‐shot learning. Health Science Reports 2023, 6: e1526. PMID: 37706016, PMCID: PMC10495736, DOI: 10.1002/hsr2.1526.Peer-Reviewed Original ResearchZero-shot learningDeep neural networksTraining dataNeural networkZero-shot learning modelData sparsity issueIdentical training dataTrue positive instancesClinical notesDeep learningDocument contentSparsity issueManual annotationTarget labelsLearning modelSemantic spaceTraining samplesPositive instancesWord featuresTraining casesBaseline modelAuxiliary informationTerms of areaLearningProbability thresholdVideo Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care
Emberger R, Boss J, Baumann D, Seric M, Huo S, Tuggener L, Keller E, Stadelmann T. Video Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care. 2023, 00: 85-88. DOI: 10.1109/sds57534.2023.00019.Peer-Reviewed Original ResearchVideo object detectionClinical decision support systemObject detection methodShelf object detectorDecision support systemPatient monitoringImage color channelsPrivacy restrictionsVideo framesIT infrastructureObject detectionObject detectorObject classesHardware constraintsTemporal consistencySupport systemColor channelsBaseline modelImproved detection rateDetection methodReliable classificationProprietary datasetUnwanted artifactsInfrastructureInformation content[Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism].
Li F, Xu Y, Zhang B, Cong F. [Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism]. Journal Of Biomedical Engineering 2023, 40: 27-34. PMID: 36854545, PMCID: PMC9989766, DOI: 10.7507/1001-5515.202204052.Peer-Reviewed Original ResearchConceptsMulti-scale convolutional layersArousal detectionSingle-channel EEG signalsSelf-attention mechanismTransfer learning methodConvolutional neural networkSleep staging taskEnd-to-endMulti-modal signalsSingle-channel electroencephalogramPrecision-recall curveConvolutional layersEvent detectionNeural networkAverage accuracyLearning methodsEEG signalsTask transferBaseline modelMulti-ModalStaging taskImprovement of model performancePortable sleep monitoringSingle modalityTime-consuming
2022
ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records
Wei Q, Zuo X, Anjum O, Hu Y, Denlinger R, Bernstam E, Citardi M, Xu H. ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records. 2022, 00: 2821-2827. DOI: 10.1109/bigdata55660.2022.10020569.Peer-Reviewed Original ResearchOptical character recognitionMulti-modal modelElectronic health recordsClinical documentsNatural language processing tasksInformation extraction technologyPre-trained modelsHealth recordsLanguage processing tasksInformation extractionImage informationF1 scoreCharacter recognitionLayout analysisProcessing tasksMulti-modal approachClinical corpusBaseline modelDocumentsOpen domainTaskExtraction technologyClinical operationsDifferent categoriesText
2021
TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
Gonzales R, Lamy J, Seemann F, Heiberg E, Onofrey J, Peters D. TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline. Lecture Notes In Computer Science 2021, 12906: 567-576. DOI: 10.1007/978-3-030-87231-1_55.Peer-Reviewed Original Research
2019
Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Manica M, Oskooei A, Born J, Subramanian V, Sáez-Rodríguez J, Martínez M. Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Molecular Pharmaceutics 2019, 16: 4797-4806. PMID: 31618586, DOI: 10.1021/acs.molpharmaceut.9b00520.Peer-Reviewed Original ResearchConceptsConvolutional encoderReceptor tyrosine kinasesProtein-protein interaction networkAttention-based encoderStructural similarity indexSelection of encodingDrug designDrug sensitivity predictionGene expression profilesIn silico predictionSensitivity predictionAttention weightsLeukemia cell linesSMILES sequencesInformative genesGene expression profiles of tumorsApoptotic processInteraction networkExpression profiles of tumorsBaseline modelIntracellular interactionsEncodingTyrosine kinaseDevelopment of personalized therapiesGenes
2018
Video Generation From Text
Li Y, Min M, Shen D, Carlson D, Carin L. Video Generation From Text. Proceedings Of The AAAI Conference On Artificial Intelligence 2018, 32 DOI: 10.1609/aaai.v32i1.12233.Peer-Reviewed Original ResearchGenerative adversarial networkVariational autoencoderGenerative modelConditional generative modelDeep learning modelsInception ScoreVideo generationSmooth videosInput textAdversarial networkImage generationImage filteringStatic featuresBaseline modelVideoLayout structureHybrid frameworkOnline videosExperimental resultsDynamic informationBackground colorGeneration procedureDynamic featuresTextAutoencoder
2011
Using statistical and machine learning to help institutions detect suspicious access to electronic health records
Boxwala A, Kim J, Grillo J, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal Of The American Medical Informatics Association 2011, 18: 498-505. PMID: 21672912, PMCID: PMC3128412, DOI: 10.1136/amiajnl-2011-000217.Peer-Reviewed Original ResearchConceptsSuspicious accessMachine-learning methodsPrivacy officersMachine learning techniquesVector machine modelAccess logsElectronic health recordsBaseline methodsAccess dataCross-validation setGold standard setSVM modelWhole data setMachine modelBaseline modelOrganizational dataHealth recordsData setsSVM
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