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Subject Areas on Research
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A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.
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A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product.
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A Shared Vision for Machine Learning in Neuroscience.
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A contemporary review of machine learning in otolaryngology-head and neck surgery.
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A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.
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A machine learning-based prediction model of H3K27M mutations in brainstem gliomas using conventional MRI and clinical features.
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A multimodality test to guide the management of patients with a pancreatic cyst.
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A protein corona primer for physical chemists.
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A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.
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A systematic study of the class imbalance problem in convolutional neural networks.
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Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning.
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Active learning for computational chemogenomics.
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Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder.
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Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning.
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Advanced Modeling to Predict Pneumonia in Combat Trauma Patients.
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Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features.
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An investigation of machine learning methods in delta-radiomics feature analysis.
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An outcome model approach to transporting a randomized controlled trial results to a target population.
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Analyzing animal behavior via classifying each video frame using convolutional neural networks.
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Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.
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Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.
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Artificial Intelligence and Machine Learning in Cardiology.
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Artificial intelligence and the future of psychiatry: Insights from a global physician survey.
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Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.
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Association of Childhood Lead Exposure With MRI Measurements of Structural Brain Integrity in Midlife.
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Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients.
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Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment.
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Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells.
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Automated problem list generation and physicians perspective from a pilot study.
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Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging.
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Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.
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Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.
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Big data: More than big data sets.
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Biomarkers of cavernous angioma with symptomatic hemorrhage.
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Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability.
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Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure.
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Clinical risk factors and inflammatory biomarkers of post-traumatic acute kidney injury in combat patients.
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Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope.
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Computational advances in combating colloidal aggregation in drug discovery.
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Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.
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Contrasting Roles of Transcription Factors Spineless and EcR in the Highly Dynamic Chromatin Landscape of Butterfly Wing Metamorphosis.
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Deep Learning to Classify Radiology Free-Text Reports.
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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.
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Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.
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Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers.
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Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.
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Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.
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Development and validation of a machine learning, smartphone-based tonometer.
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Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.
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Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope.
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Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).
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Digital pathology and computational image analysis in nephropathology.
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Do no harm: a roadmap for responsible machine learning for health care.
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Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach.
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Enhancement of Risk Prediction With Machine Learning: Rise of the Machines.
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Expression of socially sensitive genes: The multi-ethnic study of atherosclerosis.
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Generative adversarial networks to predict treatment response for neovascular age-related macular degeneration: interesting, but is it useful?
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Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas.
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Guiding Clinical Decisions Through Predictive Risk Rules.
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Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies.
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Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection.
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Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis.
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Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach.
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Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial.
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Improved Detection of Invasive Pulmonary Aspergillosis Arising during Leukemia Treatment Using a Panel of Host Response Proteins and Fungal Antigens.
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Improving Acute GI Bleeding Management Through Artificial Intelligence: Unnatural Selection?
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In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms.
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Individual-Specific, Beat-to-beat Trending of Significant Human Blood Loss: The Compensatory Reserve.
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Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study.
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Interleukin 1 receptor (IL-1R1) activation exacerbates toxin-induced acute kidney injury.
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Investigating Predictors of Cognitive Decline Using Machine Learning.
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Kidney transplant in the COVID era: Cautious optimism and continued vigilance.
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Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches.
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MSIseq: Software for Assessing Microsatellite Instability from Catalogs of Somatic Mutations.
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Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.
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Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset.
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Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.
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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.
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Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.
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Machine Learning Uncovers Food- and Excipient-Drug Interactions.
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Machine Learning and Statistical Models to Predict Postpartum Hemorrhage.
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Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.
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Machine Learning in Medical Imaging: All Journeys Begin With a Single Step.
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Machine learning and modeling: Data, validation, communication challenges.
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Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.
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Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description.
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Machine learning for phenotyping opioid overdose events.
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Machine learning in 'big data': handle with care.
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Machine learning methods for credibility assessment of interviewees based on posturographic data.
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Machine learning predicts stem cell transplant response in severe scleroderma.
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Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.
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Machine-learning phenotypic classification of bicuspid aortopathy.
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Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.
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Mapping risk of ischemic heart disease using machine learning in a Brazilian state.
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Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports.
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Metabolic liver disease - what's in a name?
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Metabolomics Identifies a Biomarker Revealing In Vivo Loss of Functional β-Cell Mass Before Diabetes Onset.
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Microbiome composition and implications for ballast water classification using machine learning.
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Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery.
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Modeling false positive error making patterns in radiology trainees for improved mammography education.
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Monitoring urban black-odorous water by using hyperspectral data and machine learning.
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Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers.
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Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.
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Multiple ocular diseases detection based on joint sparse multi-task learning.
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Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
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Nonalcoholic fatty liver disease: another leap forward.
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Numerical Models and In Vitro Assays to Study Odorant Receptors.
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On Deep Learning for Medical Image Analysis.
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Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.
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Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm.
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Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.
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Prediction Models - Development, Evaluation, and Clinical Application.
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Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches.
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Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach.
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Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: Analysis using traditional and machine learning methods.
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Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.
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Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.
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Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach.
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Random forest modeling can predict infectious complications following trauma laparotomy.
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Random forest prediction of Alzheimer's disease using pairwise selection from time series data.
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Recent developments in pediatric retina.
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Reply: Titration of Guideline-Directed Medical Therapy Improves Patient-Centered Outcomes in Heart Failure With Reduced Ejection Fraction.
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Response to Letter.
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Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.
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Selection of Informative Examples in Chemogenomic Datasets.
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Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.
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Subject Matter Knowledge in the Age of Big Data and Machine Learning.
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Synthetic breast phantoms from patient based eigenbreasts.
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System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.
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TCR Repertoires of Thymic Conventional and Regulatory T Cells: Identification and Characterization of Both Unique and Shared TCR Sequences.
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Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences.
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The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5.
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The optimal number of lymph nodes to evaluate among patients undergoing surgery for gallbladder cancer: Correlating the number of nodes removed with survival in 6531 patients.
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Towards precision medicine: Accurate predictive modeling of infectious complications in combat casualties.
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Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.
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Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière's disease.
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Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting.
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Use of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma.
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Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study.
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Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy.
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Using Machine Learning to Identify Heterogeneous Effects in Randomized Clinical Trials-Moving Beyond the Forest Plot and Into the Forest.
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Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.
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Utilizing Machine Learning for Pre- and Postoperative Assessment of Patients Undergoing Resection for BCLC-0, A and B Hepatocellular Carcinoma: Implications for Resection Beyond the BCLC Guidelines.
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Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality.
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Vibrational Properties of Metastable Polymorph Structures by Machine Learning.
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What Counts as "Clinical Data" in Machine Learning Healthcare Applications?
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Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations.
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fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.
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Keywords of People
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Bradbury, Kyle,
Assistant Research Professor in the Department of Electrical and Computer Engineering,
Duke University Energy Initiative
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Bryan, Jordan,
Student,
Statistical Science
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Compton, Bobby,
Adjunct Assistant Professor in the Pratt School of Engineering,
Pratt School of Engineering
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Dunn, Timothy,
Assistant Professor in Neurosurgery,
Neurosurgery
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Farsiu, Sina,
Professor in the Department of Biomedical Engineering,
Electrical and Computer Engineering
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Gong, Neil,
Assistant Professor of Electrical and Computer Engineering,
Electrical and Computer Engineering
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Horstmeyer, Roarke,
Assistant Professor of Biomedical Engineering,
Electrical and Computer Engineering
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Huettel, Scott,
Professor in the Department of Psychology and Neuroscience,
Duke Science & Society
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Jelovsek, John E,
Associate Professor of Obstetrics and Gynecology,
Obstetrics and Gynecology, Urogynecology
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Jordan, Joseph Dedrick,
Professor of Neurology,
Neurosurgery
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Le, Cat,
Student,
Electrical and Computer Engineering
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Lee, Holden,
Phillip Griffiths Assistant Research Professor,
Mathematics
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Lo, Joseph Yuan-Chieh,
Professor in Radiology,
Electrical and Computer Engineering
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Ma, Li,
Associate Professor of Statistical Science,
Statistical Science
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Machanavajjhala, Ashwinkumar Venkatanaga,
Associate Professor of Computer Science,
Computer Science
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Mainsah, Boyla Octavie,
Assistant Research Professor in theDepartment of Electrical and Computer Engineering,
Electrical and Computer Engineering
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Mandal, Sayan,
Student,
Electrical and Computer Engineering
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Mehta, Nikhil,
Student,
Electrical and Computer Engineering
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Nave, Ricky,
Student,
Mathematics
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Pearson, John,
Assistant Professor of Biostatistics and Bioinformatics,
Center for Cognitive Neuroscience
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Pryer, Kathleen M.,
Professor of Biology,
Duke Science & Society
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Rubin, Geoffrey D,
George Barth Geller Distinguished Professorship for Research in Cardiovascular Diseases,
Duke Innovation & Entrepreneurship
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Tchapyjnikov, Dmitry,
Adjunct Assistant Professor in the Department of Pediatrics,
Pediatrics, Neurology
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Wiggins, Walter,
Assistant Professor of Radiology,
Radiology, Neuroradiology
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Xiao, Ran,
Assistant Professor in the School of Nursing,
School of Nursing
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Yanchenko, Anna,
Student,
Statistical Science