2020
Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning
Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2020, 46: 216-225. PMID: 32500237, PMCID: PMC7714704, DOI: 10.1007/s00261-020-02604-5.Peer-Reviewed Original ResearchConceptsDeep convolutional neural networkAverage false positive rateDice similarity coefficientU-NetDeep learning algorithmsConvolutional neural networkTest setMean Dice similarity coefficientRandom forest classifierDCNN methodDCNN approachDeep learningNet architectureLearning algorithmNeural networkLiver segmentationManual 3D segmentationForest classifierGround truthManual segmentationFalse positive rateCorresponding segmentationSegmentationMultiphasic contrast-enhanced MRIThresholding
2019
Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs
Ma J, Lee DKK, Perkins ME, Pisani MA, Pinker E. Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs. Critical Care Explorations 2019, 1: e0010. PMID: 32166256, PMCID: PMC7063876, DOI: 10.1097/cce.0000000000000010.Peer-Reviewed Original ResearchPrecision-recall curveTrajectory informationData trajectoriesStatistical learning techniquesRandom forest classifierRelevant shape featuresStatistical learning modelsLearning techniquesMachine learningElectronic health recordsTrajectory featuresLearning modelShape featuresForest classifierTime series dataManual extractionHealth recordsPredictive modelingSeries dataPredictive performanceInformationPatients' clinical dataDynamic predictionClassifierTime series
2017
Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
Raja K, Patrick M, Elder J, Tsoi L. Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Scientific Reports 2017, 7: 3690. PMID: 28623363, PMCID: PMC5473874, DOI: 10.1038/s41598-017-03914-3.Peer-Reviewed Original ResearchConceptsDrug-gene interactionsDDI corpusPrediction of adverse drug reactionsRandom forest classifierMachine learning workflowPrediction of drug-drug interactionsF-scoreDrug-drug interactionsAdverse drug reactionsClassification modelMolecular levelLearning workflowForest classifierAdverse Drug Reactions ClassificationDrug discoveryClassifierADR typesCutaneous diseasePrevent adverse drug reactionsDrug reactionsPace of drug discoveryClassificationPotential drug-drug interactions
2011
Detecting abbreviations in discharge summaries using machine learning methods.
Wu Y, Rosenbloom S, Denny J, Miller R, Mani S, Giuse D, Xu H. Detecting abbreviations in discharge summaries using machine learning methods. AMIA Annual Symposium Proceedings 2011, 2011: 1541-9. PMID: 22195219, PMCID: PMC3243185.Peer-Reviewed Original ResearchConceptsNatural language processingMachine learning methodsHighest F-measureF-measureClinical natural language processingLexical resourcesClinical abbreviationsTraining setPre-defined featuresRandom forest classifierDomain expertsML algorithmsML classifiersLanguage processingVoting schemeLearning methodsDischarge summariesForest classifierTest setClassifierCorpus-based methodSetResourcesAlgorithmAbbreviations
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