2024
Development of Clinical NLP Systems
Xu H, Demner Fushman D. Development of Clinical NLP Systems. Cognitive Informatics In Biomedicine And Healthcare 2024, 301-324. DOI: 10.1007/978-3-031-55865-8_11.Peer-Reviewed Original Research
2023
Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models
Li Z, Ameer I, Hu Y, Abdelhameed A, Tao C, Selek S, Xu H. Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models. 2023, 00: 481-483. DOI: 10.1109/ichi57859.2023.00074.Peer-Reviewed Original ResearchWeighted F1 scoreF1 scoreMachine learning modelsElectronic health recordsLearning modelsState-of-the-art modelsState-of-the-artBinary classification taskHealth recordsBinary classification modelStandard diagnosis codesClassification taskMulticlass classificationHealth informaticsClassification modelMental health informaticsTransformation modelPrediction algorithmPsychiatric notesInitial psychiatric evaluationSuicidal tendenciesMachineRandom forest modelSuicidal ideationPerformance
2019
Recognizing software names in biomedical literature using machine learning
Wei Q, Zhang Y, Amith M, Lin R, Lapeyrolerie J, Tao C, Xu H. Recognizing software names in biomedical literature using machine learning. Health Informatics Journal 2019, 26: 21-33. PMID: 31566474, PMCID: PMC7334865, DOI: 10.1177/1460458219869490.Peer-Reviewed Original ResearchConceptsSoftware namesF-measureNatural language processing methodsBiomedical literatureWord representation featuresLanguage processing methodsEntity recognition systemSoftware catalogSoftware repositoriesFeature engineeringBiomedical softwareRecognition systemSoftware toolsBiomedical domainRepresentation featuresMEDLINE abstractsWord embeddingsKnowledge featuresManual curationSoftwareMachineProcessing methodsBest systemRepositorySystemA study of deep learning approaches for medication and adverse drug event extraction from clinical text
Wei Q, Ji Z, Li Z, Du J, Wang J, Xu J, Xiang Y, Tiryaki F, Wu S, Zhang Y, Tao C, Xu H. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. Journal Of The American Medical Informatics Association 2019, 27: 13-21. PMID: 31135882, PMCID: PMC6913210, DOI: 10.1093/jamia/ocz063.Peer-Reviewed Original ResearchConceptsDeep learning-based approachDeep learning approachLearning-based approachTraditional machineLearning approachNational NLP Clinical ChallengesAdverse drug event extractionOutperform traditional machineDifferent ensemble approachesConditional Random FieldsSequence labeling approachMIMIC-III databaseEvent extractionMedical domainEntity recognitionClassification componentF1 scoreClinical textRelation extractionClinical documentsVector machineEnd evaluationEnsemble approachClinical corpusMachine
2017
A hybrid approach to automatic de-identification of psychiatric notes
Lee H, Wu Y, Zhang Y, Xu J, Xu H, Roberts K. A hybrid approach to automatic de-identification of psychiatric notes. Journal Of Biomedical Informatics 2017, 75: s19-s27. PMID: 28602904, PMCID: PMC5705430, DOI: 10.1016/j.jbi.2017.06.006.Peer-Reviewed Original ResearchConceptsPsychiatric notesCEGS N-GRIDNatural language processing systemsRule-based componentTask Track 1Language processing systemRule-based approachDe-identificationDomain adaptationRich featuresProcessing systemHybrid approachN gridTrack 1Clinical dataTest setSystem performanceMachineHealth informationHybrid systemSystemClinical applicationTaskInformationData
2015
Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods.
Tang B, Chen Q, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H. Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods. AMIA Annual Symposium Proceedings 2015, 2015: 1184-93. PMID: 26958258, PMCID: PMC4765674.Peer-Reviewed Original ResearchClassification of Cancer Primary Sites Using Machine Learning and Somatic Mutations
Chen Y, Sun J, Huang L, Xu H, Zhao Z. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International 2015, 2015: 491502. PMID: 26539502, PMCID: PMC4619847, DOI: 10.1155/2015/491502.Peer-Reviewed Original ResearchConceptsMachine learningF-measureAvailable big dataSupport vector machineBig dataVector machineClassification experimentsAccurate classificationCancer classificationGene function informationMachineSomatic mutation informationClassificationMutation informationFunction informationLearningGene symbolsInformationGene featuresGreat opportunityPerformanceSomatic mutation dataMutation dataAccuracyPrediction
2013
Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing.
Moon S, Berster B, Xu H, Cohen T. Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing. AMIA Annual Symposium Proceedings 2013, 2013: 1007-16. PMID: 24551390, PMCID: PMC3900125.Peer-Reviewed Original ResearchConceptsWord sense disambiguationAverage accuracySense disambiguationWord sense disambiguation algorithmSupport vector machineHyperdimensional ComputingNaïve BayesCommon machineClinical documentsVector machineDisambiguation algorithmClinical abbreviationsMedical informationAccurate extractionAlgorithmDisambiguationMachineSuch approachesClinical notesPresent new approachVector transformationNew approachAmbiguous termsComputingAccuracyMachine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy A, Abramson V, Bhave S, Levy M, Xu H, Yankeelov T. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. Journal Of The American Medical Informatics Association 2013, 20: 688-695. PMID: 23616206, PMCID: PMC3721158, DOI: 10.1136/amiajnl-2012-001332.Peer-Reviewed Original ResearchConceptsNeoadjuvant chemotherapyFeature selectionCycles of NACPredictive model buildingTime most patientsBreast cancer patientsImportant clinical problemCourse of therapyMachine learningDynamic contrast-enhanced MRIContrast-enhanced MRIQuantitative dynamic contrast-enhanced MRIMost patientsTreatment regimenCancer patientsClinical variablesTherapeutic responseBreast cancerPredictive modeling approachClinical problemData show promiseLogistic regressionPatientsMachineDiffusion-weighted MRI data
2012
A study of transportability of an existing smoking status detection module across institutions.
Liu M, Shah A, Jiang M, Peterson N, Dai Q, Aldrich M, Chen Q, Bowton E, Liu H, Denny J, Xu H. A study of transportability of an existing smoking status detection module across institutions. AMIA Annual Symposium Proceedings 2012, 2012: 577-86. PMID: 23304330, PMCID: PMC3540509.Peer-Reviewed Original ResearchConceptsDetection moduleNatural language processing systemsKnowledge Extraction SystemEMR dataRule-based classifierClinical Text AnalysisHighest F-measureLanguage processing systemElectronic medical recordsF-measureLevels of classificationProcessing systemSpecific tasksText analysisClassifierDesirable performanceModuleModest effortExtraction systemCTAKESSmoking moduleMachineSystemTaskClassificationExtracting epidemiologic exposure and outcome terms from literature using machine learning approaches.
Lu Y, Xu H, Peterson N, Dai Q, Jiang M, Denny J, Liu M. Extracting epidemiologic exposure and outcome terms from literature using machine learning approaches. International Journal Of Data Mining And Bioinformatics 2012, 6: 447-59. PMID: 23155773, DOI: 10.1504/ijdmb.2012.049284.Peer-Reviewed Original Research
2010
Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine.
Doan S, Xu H. Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine. Proceedings - International Conference On Computational Linguistics 2010, 2010: 259-266. PMID: 26848286, PMCID: PMC4736747.Peer-Reviewed Original ResearchSupport vector machineHospital discharge summariesConditional Random FieldsDischarge summariesMedication namesRelated entitiesClinical textVector machineType of medicationNamed Entity Recognition (NER) taskEntity recognition taskRule-based systemBest F-scoreI2b2 NLP challengeTypes of featuresF-scoreI2b2 challengeNLP challengeNER systemSemantic featuresRecognition taskMachineData setsRandom fieldsBetter performance