Machine learning

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  Subject Areas on Research

  • A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization. 
  • A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product. 
  • A Shared Vision for Machine Learning in Neuroscience. 
  • A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. 
  • A systematic study of the class imbalance problem in convolutional neural networks. 
  • Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features. 
  • Analyzing animal behavior via classifying each video frame using convolutional neural networks. 
  • Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients. 
  • Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment. 
  • Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells. 
  • Automated problem list generation and physicians perspective from a pilot study. 
  • Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging. 
  • Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field. 
  • Big data: More than big data sets. 
  • Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability. 
  • Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods. 
  • 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. 
  • Deep Learning to Classify Radiology Free-Text Reports. 
  • Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. 
  • Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. 
  • Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers. 
  • Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. 
  • 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. 
  • Development of New Diagnostic Techniques - Machine Learning. 
  • Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis. 
  • Improved Detection of Invasive Pulmonary Aspergillosis Arising during Leukemia Treatment Using a Panel of Host Response Proteins and Fungal Antigens. 
  • In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms. 
  • Individual-Specific, Beat-to-beat Trending of Significant Human Blood Loss: The Compensatory Reserve. 
  • Interleukin 1 receptor (IL-1R1) activation exacerbates toxin-induced acute kidney injury. 
  • MSIseq: Software for Assessing Microsatellite Instability from Catalogs of Somatic Mutations. 
  • Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. 
  • Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. 
  • Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. 
  • Machine learning and modeling: Data, validation, communication challenges. 
  • Machine learning methods for credibility assessment of interviewees based on posturographic data. 
  • Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry. 
  • Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports. 
  • Modeling false positive error making patterns in radiology trainees for improved mammography education. 
  • Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers. 
  • Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. 
  • Multiple ocular diseases detection based on joint sparse multi-task learning. 
  • 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. 
  • Numerical Models and In Vitro Assays to Study Odorant Receptors. 
  • On Deep Learning for Medical Image Analysis. 
  • Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield. 
  • Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches. 
  • Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy. 
  • Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach. 
  • Synthetic breast phantoms from patient based eigenbreasts. 
  • Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences. 
  • The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5. 
  • 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. 
  • Towards precision medicine: Accurate predictive modeling of infectious complications in combat casualties. 
  • Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases. 
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  Keywords of People

  • Bradbury, Kyle, Lecturing Fellow in the Duke University Energy Initiative, Duke University Energy Initiative
  • Bryan, Jordan, Student, Statistical Science
  • Coronado Sroka, Eduardo, Student, Statistical Science
  • Farsiu, Sina, Paul Ruffin Scarborough Associate Professor of Engineering, Electrical and Computer Engineering
  • Gong, Neil, Assistant Professor of Electrical and Computer Engineering, Electrical and Computer Engineering
  • Horstmeyer, Roarke W., Assistant Professor of Biomedical Engineering, Electrical and Computer Engineering
  • Huettel, Scott, Professor in the Department of Psychology and Neuroscience, Duke Science & Society
  • Jelovsek, John E, Associate Professor of Obstetrics and Gynecology, Duke Clinical Research Institute
  • Lo, Joseph Yuan-Chieh, Professor of Radiology, Electrical and Computer Engineering
  • Ma, Li, Associate Professor of Statistical Science, Statistical Science
  • Nave, Ricky, Student, Mathematics
  • Palanisamy, Dinesh, Student, Computer Science
  • Pearson, John Michael, Assistant Professor of Biostatistics and Bioinformatics, Center for Cognitive Neuroscience
  • Rubin, Geoffrey D, George Barth Geller Professorship for Research in Cardiovascular Diseases, Duke Innovation & Entrepreneurship
  • Tchapyjnikov, Dmitry, Assistant Professor of Pediatrics, Pediatrics, Neurology
  • Yanchenko, Anna, Student, Statistical Science