2019
Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text
Xu J, Li Z, Wei Q, Wu Y, Xiang Y, Lee H, Zhang Y, Wu S, Xu H. Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text. BMC Medical Informatics And Decision Making 2019, 19: 236. PMID: 31801529, PMCID: PMC6894107, DOI: 10.1186/s12911-019-0937-2.Peer-Reviewed Original ResearchConceptsSequence labeling approachMedical conceptsEntity recognitionRelation classificationClinical textDetection taskBidirectional long short-term memory networkLong short-term memory networkShort-term memory networkConditional Random FieldsSequence labeling problemTraditional methodsNLP applicationsBi-LSTMNeural architectureLabeling problemLabeling approachMemory networkNovel solutionRandom fieldsHigh accuracyEfficient wayTaskAttributesClassificationIntegrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text
Li Z, Yang Z, Shen C, Xu J, Zhang Y, Xu H. Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BMC Medical Informatics And Decision Making 2019, 19: 22. PMID: 30700301, PMCID: PMC6354333, DOI: 10.1186/s12911-019-0736-9.Peer-Reviewed Original ResearchConceptsShortest dependency pathConvolutional neural networkNeural network architectureNatural language processingSentence sequenceRelation extractionClinical relation extractionTarget entityNetwork architectureClinical textNeural networkRepresentation moduleDependency pathsDeep learning-based approachNew neural network architectureBidirectional long short-term memory networkLong short-term memory networkDeep learning frameworkDeep neural networksShort-term memory networkLearning-based approachNovel neural approachRelation extraction datasetBi-LSTM networkSyntactic features
2017
Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks
Dvornek NC, Ventola P, Pelphrey KA, Duncan JS. Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks. Lecture Notes In Computer Science 2017, 10541: 362-370. PMID: 29104967, PMCID: PMC5669262, DOI: 10.1007/978-3-319-67389-9_42.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingAutism spectrum disorderLong short-term memoryAutism Brain Imaging Data Exchange IResting-state functional connectivity measuresShort-term memoryLong short-term memory networkResting-state functional magnetic resonance imagingShort-term memory networkFunctional connectivity measuresPotential functional networksTypical controlsSpectrum disorderASD biomarkersMemory networkRecurrent neural networkExchange IMulti-site dataFMRI dataFunctional networksLSTM modelClassification of individualsCross-validation frameworkConnectivity measuresObjective biomarkers
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply