2025
Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures
Zaboski B, Bednarek L. Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures. Journal Of Clinical Medicine 2025, 14: 2442. PMID: 40217892, PMCID: PMC11989962, DOI: 10.3390/jcm14072442.Peer-Reviewed Original ResearchNeural networkDeep learningObsessive-compulsive disorderApplication of deep learning architecturesRecurrent neural networkConvolutional neural networkDeep learning architectureHigh-dimensional dataAdversarial networkLearning architectureMultimodal datasetData generationObsessive-compulsive disorder researchNeural predictorsPredictors of treatment responseComplex psychiatric conditionsNetworkTreatment responseArchitecturePsychiatric conditionsPrecision psychiatryImplementation of DLDatasetClassificationLearningImproving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media
Li Y, Viswaroopan D, He W, Li J, Zuo X, Xu H, Tao C. Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media. Journal Of Biomedical Informatics 2025, 163: 104789. PMID: 39923968, DOI: 10.1016/j.jbi.2025.104789.Peer-Reviewed Original ResearchConceptsTraditional deep learning modelsDeep learning modelsRecurrent neural networkLearning modelsEntity recognitionLanguage modelF1 scoreEnsemble of deep learningAdvances of natural language processingEffectiveness of ensemble methodsMicro-averaged F1Bidirectional Encoder RepresentationsExtensive labeled dataNatural language processingFine-tuned modelsBiomedical text miningFeature representationEncoder RepresentationsEvent extractionEntity typesText dataDeep learningSequential dataGPT-2Neural network
2023
Development of a Natural Language Processing Tool to Extract Acupuncture Point Location Terms
Li Y, Peng X, Li J, Peng S, Pei D, Tao C, Xu H, Hong N. Development of a Natural Language Processing Tool to Extract Acupuncture Point Location Terms. 2023, 00: 344-351. DOI: 10.1109/ichi57859.2023.00053.Peer-Reviewed Original ResearchAcupuncture point locationsNatural language processingRecurrent neural networkConditional random fieldWorld Health OrganizationWorld Health Organization standardsNatural language processing toolsEffect of acupuncture therapyLocation informationAcupuncture researchAcupuncture therapyAcupoint locationRecurrent neural network modelDictionary lookup methodNatural language processing modelsDeep learning techniquesAcupunctureLanguage processing toolsWestern Pacific RegionFree-text formatInternational anatomical terminologyHealth OrganizationF1 scoreInformatics applicationsNeural networkMulti-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
Yan W, Yu L, Liu D, Sui J, Calhoun V, Lin Z. Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG. Frontiers In Psychiatry 2023, 14: 1202049. PMID: 37441141, PMCID: PMC10333510, DOI: 10.3389/fpsyt.2023.1202049.Peer-Reviewed Original ResearchConvolutional recurrent neural networkRecurrent neural networkResting-state EEGNeural networkPsychiatric disordersDeep learning classification modelLow-dimensional subspaceTwo-class classificationDesigning individualized treatmentLearning classification modelsEEG backgroundClassification modelHealthy controlsDepressive disorderSpatiotemporal informationClinical observationsDisease severityAccurate classificationIndividualized treatmentBiomarkersDisorder classificationDisorder identificationDisordersClassificationNeuroimaging biomarkers
2022
Geometry of neural computation unifies working memory and planning
Ehrlich DB, Murray JD. Geometry of neural computation unifies working memory and planning. Proceedings Of The National Academy Of Sciences Of The United States Of America 2022, 119: e2115610119. PMID: 36067286, PMCID: PMC9478653, DOI: 10.1073/pnas.2115610119.Peer-Reviewed Original ResearchConceptsNeural dataPossible circuit mechanismReal-world tasksMemory taskUpcoming eventsSensory modelPrefrontal cortexCognitive functionRecurrent neural networkHuman behaviorNeurophysiological observationsMemoryCircuit mechanismsFalsifiable predictionsFuture behaviorTaskModular processRepresentational strategiesRepresentationNeural networkCortexBehaviorDistinct typesBrainFindingsTracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models
Hou J, Deng J, Li C, Wang Q. Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models. Journal Of Personalized Medicine 2022, 12: 742. PMID: 35629164, PMCID: PMC9147215, DOI: 10.3390/jpm12050742.Peer-Reviewed Original ResearchShort-term memory recurrent neural networkLong short-term memory recurrent neural networkTransfer learningRecurrent neural networkDeep learning modelsReduced training timeNeural networkLearning modelTraining timeDynamical system modelLearningShort-term predictionNormsMore cancer patientsComparable levelsPhysiological modelPatient-specific models
2020
PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks
Ehrlich DB, Stone JT, Brandfonbrener D, Atanasov A, Murray JD. PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks. ENeuro 2020, 8: eneuro.0427-20.2020. PMID: 33328247, PMCID: PMC7814477, DOI: 10.1523/eneuro.0427-20.2020.Peer-Reviewed Original ResearchConceptsRecurrent neural networkCognitive tasksCognitive neurosciencePython packageTraining of animalsTraining recurrent neural networksNetwork modelArtificial recurrent neural networkDeep learning softwareDeep-learning methodsRecurrent neural network modelNeural network modelNeural representationCognitive computationsNeuroscience researchNeural networkRNN modelCurriculum learningNeuroscienceCircuit mechanismsAdditional customizationConnectivity patternsTaskSoftware packageComputational modeling frameworkDemographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Dvornek NC, Li X, Zhuang J, Ventola P, Duncan JS. Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity. Lecture Notes In Computer Science 2020, 12436: 363-372. PMID: 34308438, PMCID: PMC8299434, DOI: 10.1007/978-3-030-59861-7_37.Peer-Reviewed Original ResearchRecurrent neural network modelRecurrent neural networkNeural network modelFunctional magnetic resonance imaging (fMRI) time series dataAttention mechanismArt resultsNeural networkCross-validation frameworkNetwork modelTime series dataIndividual demographic informationABIDE IImproved classificationNetwork differencesNetworkClassificationFunctional network differencesFrameworkIndividual demographic variablesInformationRepresentation 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-9Estimating Reproducible Functional Networks Associated with Task Dynamics Using Unsupervised LSTMS
Dvornek NC, Ventola P, Duncan JS. Estimating Reproducible Functional Networks Associated with Task Dynamics Using Unsupervised LSTMS. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2020, 00: 1-4. PMID: 34422224, PMCID: PMC8375550, DOI: 10.1109/isbi45749.2020.9098377.Peer-Reviewed Original ResearchLong short-term memoryFunctional networksBiological motion perception taskTask activitiesMotion perception taskShort-term memoryLSTM modelPerception taskNeural correlatesTask paradigmFMRI activityTerm memoryRecurrent neural networkTask dynamicsTarget taskFunctional magnetic resonance imaging (fMRI) time series dataTaskUnsupervised mannerIdentification of patients with carotid stenosis using natural language processing
Wu X, Zhao Y, Radev D, Malhotra A. Identification of patients with carotid stenosis using natural language processing. European Radiology 2020, 30: 4125-4133. PMID: 32103365, DOI: 10.1007/s00330-020-06721-z.Peer-Reviewed Original Research
2019
Deep learning in clinical natural language processing: a methodical review
Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, Soni S, Wang Q, Wei Q, Xiang Y, Zhao B, Xu H. Deep learning in clinical natural language processing: a methodical review. Journal Of The American Medical Informatics Association 2019, 27: 457-470. PMID: 31794016, PMCID: PMC7025365, DOI: 10.1093/jamia/ocz200.Peer-Reviewed Original ResearchConceptsNatural language processingClinical natural language processingDeep learningLanguage processingComputing Machinery Digital LibraryInformation extraction tasksMedical informatics communityComputational Linguistics anthologyRecurrent neural networkDigital librariesText classificationElectronic health recordsExtraction taskEntity recognitionWord2vec embeddingsNeural networkRelation extractionNLP communityNLP researchInformatics communitySpecific tasksHealth recordsNLP problemLearningClinical domainsJointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Dvornek NC, Li X, Zhuang J, Duncan JS. Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI. Lecture Notes In Computer Science 2019, 11861: 382-390. PMID: 32274470, PMCID: PMC7143657, DOI: 10.1007/978-3-030-32692-0_44.Peer-Reviewed Original ResearchRecurrent neural networkAutism Brain Imaging Data ExchangeFunctional magnetic resonance imaging (fMRI) dataLarge fMRI datasetShort-term memory structureGenerative modelLong short-term memory (LSTM) structureGenerative recurrent neural networkNeural networkLSTM nodesFMRI time series dataTime series dataMagnetic resonance imaging dataRNN-based modelsNetwork interpretabilityFMRI datasetsMemory structureClassification taskData exchangeDiscriminative modelModeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
Gigante S, van Dijk D, Moon K, Strzalkowski A, Wolf G, Krishnaswamy S. Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030978.Peer-Reviewed Original ResearchStochastic dynamic systemsDeep generative neural networksProbability distributionDynamic systemsMarkov modelGlobal dynamicsLocal snapshotGenerative neural networksKalman filterSnapshot dataNeural networkNeural network frameworkRecurrent neural networkSuch systemsNext stateModeling networkNetwork frameworkDynamicsShallow modelsLocal transitionsHypothetical trajectoryModelBiological systemsNatural sciencesLongitudinal measurementsPrediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning.
Robinson E, Kulkarni P, Pradhan J, Gartrell R, Yang C, Rizk E, Acs B, Rohr B, Phelps R, Ferringer T, Horst B, Rimm D, Wang J, Saenger Y. Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning. Journal Of Clinical Oncology 2019, 37: 9577-9577. DOI: 10.1200/jco.2019.37.15_suppl.9577.Peer-Reviewed Original ResearchDeep neural net architectureOpen source softwareRecurrent neural networkNeural net architectureDigital pathology toolsDeep learningSource softwareNet architectureFeature informationNeural networkNetwork parametersTIFF filesAdjuvant immunotherapyMelanoma recurrenceCohort 2Cohort 1Cell classificationStage IMultivariable Cox proportional hazards modelsDNNCox proportional hazards modelColumbia University Medical CenterNuclear segmentationEvidence of diseaseIndependent prognostic factor
2018
Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition.
Wu Y, Yang X, Bian J, Guo Y, Xu H, Hogan W. Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition. AMIA Annual Symposium Proceedings 2018, 2018: 1110-1117. PMID: 30815153, PMCID: PMC6371322.Peer-Reviewed Original ResearchConceptsRecurrent neural networkWord embeddingsOne-hot vectorsWord representationsLow-frequency wordsOnly word embeddingsClinical Named Entity RecognitionClinical NER tasksWord embedding methodsConditional Random FieldsStatistical language modelNamed Entity RecognitionUnlabeled corpusLanguage modelLanguage systemNER taskDecent representationFactual medical knowledgeImportant wordsDeep learning modelsEntity recognitionClinical corpusNamed Entity Recognition SystemArt performanceFeature representationRecurrent Neural Networks in Mobile Sampling and Intervention
Koppe G, Guloksuz S, Reininghaus U, Durstewitz D. Recurrent Neural Networks in Mobile Sampling and Intervention. Schizophrenia Bulletin 2018, 45: 272-276. PMID: 30496527, PMCID: PMC6403085, DOI: 10.1093/schbul/sby171.Peer-Reviewed Original ResearchConceptsRecurrent neural networkEcological momentary interventionsEveryday life contextPsychological processesMomentary interventionsData modalitiesDifferent data modalitiesSocial outcomesSocioenvironmental factorsFuture researchMultiple data modalitiesNeural networkTreatment of psychosisOnline feedbackIndividual trajectoriesPsychosisDaily lifeContext-specific interventionsInterventionEmotionsCognitionExperienceStatistical machineContextFitness trackersLearning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets
Dvornek NC, Yang D, Ventola P, Duncan JS. Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets. Lecture Notes In Computer Science 2018, 11072: 329-337. PMID: 30873514, PMCID: PMC6411297, DOI: 10.1007/978-3-030-00931-1_38.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNeural networkTask fMRI datasetsMedical image analysis problemsSuch deep networksImage analysis problemsTask fMRI scanTypical control subjectsDeep networkDeep learningTraining lossSmall datasetsLarge datasetsNumber of approachesAutism spectrum disorderAnalysis problemDatasetNetworkTraining runsImage analysisGeneralizable modelNon-imaging variablesSpectrum disorderFMRI analysisModel performanceExtracting psychiatric stressors for suicide from social media using deep learning
Du J, Zhang Y, Luo J, Jia Y, Wei Q, Tao C, Xu H. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics And Decision Making 2018, 18: 43. PMID: 30066665, PMCID: PMC6069295, DOI: 10.1186/s12911-018-0632-8.Peer-Reviewed Original ResearchConceptsConvolutional neural networkRecurrent neural networkDeep learningConditional Random FieldsSupport vector machineSuicide-related tweetsClinical textNeural networkPsychiatric stressorsExtra TreesBinary classifierTransfer learning strategiesEntity recognition taskSocial mediaExact matchTraditional machineAnnotation costLearning strategiesRecognition problemSharing flowInexact matchVector machineTwitter dataRecognition taskTwitterA study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set
Rasmy L, Wu Y, Wang N, Geng X, Zheng W, Wang F, Wu H, Xu H, Zhi D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. Journal Of Biomedical Informatics 2018, 84: 11-16. PMID: 29908902, PMCID: PMC6076336, DOI: 10.1016/j.jbi.2018.06.011.Peer-Reviewed Original ResearchConceptsRecurrent neural networkOnset riskCapability of RNNCerner Health FactsHeterogeneous EHR dataHeart failure patientsData setsElectronic health record dataDeep learning modelsDifferent patient populationsNeural network-based predictive modelDifferent patient groupsHealth record dataEHR data setsPredictive modelingSmall data setsFailure patientsPatient groupPatient populationReduction of AUCNeural networkRNN modelRETAIN modelHealth FactsHospital
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