159 results on '"Raghunath, Sushravya"'
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2. Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data
3. An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk
4. Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset
5. A deep neural network to enhance prediction of 1-year mortality using echocardiographic videos of the heart
6. Abstract 11473: An EHR-Based Machine Learning Model Predicts Myocardial Infarction Better Than an ECG-Based Machine Learning Model and the Pooled Cohort Equations
7. Abstract 11452: EHR-Based Machine Learning Model Predicts Drug-Induced QT Prolongation With Superior Performance Compared to Clinical Risk Predictors
8. Abstract 11000: Deep Learning Prediction of New-Onset Atrial Fibrillation Using Echocardiography Videos
9. Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality
10. A Machine Learning Approach to Management of Heart Failure Populations
11. Abstract 15393: Automatic Multi-structural Cardiac Segmentation of 2d Echocardiography With Convolutional Neural Networks
12. Abstract 13102: Prediction of Incident AF With Deep Learning Can Identify Patients at High Risk for AF-related Stroke
13. Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging?
14. Referenceless Stratification of Parenchymal Lung Abnormalities
15. Noninvasive Risk Stratification of Lung Adenocarcinoma using Quantitative Computed Tomography
16. Short-term Automated Quantification of Radiologic Changes in the Characterization of Idiopathic Pulmonary Fibrosis Versus Nonspecific Interstitial Pneumonia and Prediction of Long-term Survival
17. Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)—A Pilot Study
18. Session 2: AI in automated ECG analysisAI-based predictive and diagnostic electrocardiography
19. rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography
20. Active Relearning for Robust Supervised Training of Emphysema Patterns
21. Generalizability and Quality Control of Deep Learning-Based 2D Echocardiography Segmentation Models in a Large Clinical Dataset
22. Abstract 9756: An ECG-Based Machine Learning Model for Predicting New Onset Atrial Fibrillation is Superior to Age and Clinical Variables in Selecting a Population at High Stroke Risk
23. Abstract 9536: Prediction of Drug-Induced QTc Prolongation With an ECG Based Machine Learning Model
24. Abstract 9599: Rechommend: An Ecg-Based Machine-Learning Approach for Identifying Patients at High-Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography
25. Noninvasive Computed Tomography-based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial
26. rECHOmmend: an ECG-based machine-learning approach for identifying patients at high-risk of undiagnosed structural heart disease detectable by echocardiography
27. ONE YEAR PREDICTION OF MODERATE OR SEVERE AORTIC STENOSIS USING ECG- AND EHR-BASED MACHINE LEARNING MODELS
28. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation–Related Stroke
29. Correlation of Automated Quantitative Measures of Interstitial Lung Disease (ILD) Using CALIPER With Semiquantitative Visual Radiology Scores
30. Correlation of Quantitative Lung Tissue Characterization as Assessed by CALIPER With Pulmonary Function and 6-Minute Walk Test
31. Can Progression of Fibrosis as Assessed by Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) Predict Outcomes in Patients With Idiopathic Pulmonary Fibrosis?
32. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
33. Deep Neural Networks can Predict Incident Atrial Fibrillation from the 12-lead Electrocardiogram and may help Prevent Associated Strokes
34. Left ventricular and atrial segmentation of 2D echocardiography with convolutional neural networks
35. Deep neural networks can predict one-year mortality and incident atrial fibrillation from raw 12-lead electrocardiogram voltage data
36. Prevalence and Electronic Health Record-Based Phenotype of Loss-of-Function Genetic Variants in Arrhythmogenic Right Ventricular Cardiomyopathy-Associated Genes
37. CANARY Risk Management of Adenocarcinoma: The Future of Imaging?
38. Abstract 883: Elucidating cancer hallmark context from glioma MR imaging and RNA expression data
39. Abstract 882: Interpreting glioma MR imaging and somatic mutations in a cancer hallmark context
40. Automated Quantitative Computed Tomography Versus Visual Computed Tomography Scoring in Idiopathic Pulmonary Fibrosis
41. Pulmonary Nodule Characterization, Including Computer Analysis and Quantitative Features
42. Quantitative Stratification of Diffuse Parenchymal Lung Diseases
43. Quantitative Computed Tomography Imaging of Interstitial Lung Diseases
44. Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns
45. Quantitative image analytics for stratified pulmonary medicine
46. Automating the expert consensus paradigm for robust lung tissue classification
47. Active relearning for robust supervised classification of pulmonary emphysema
48. Effect of denoising on supervised lung parenchymal clusters
49. Noninvasive Characterization of Tissue Invasion by Pulmonary Nodules of the Lung Adenocarcinoma Spectrum Using CALIPER (Computer-Aided Lung Informatics for Pathology Evaluation and Rating) - A Pilot Study
50. Noninvasive CT-Based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial.
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