Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

[1]  D. Dey,et al.  Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events , 2022, The Journal of Nuclear Medicine.

[2]  D. Dey,et al.  Benefit of Early Revascularization Based on Inducible Ischemia and Left Ventricular Ejection Fraction. , 2022, Journal of the American College of Cardiology.

[3]  D. Dey,et al.  Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry , 2022, Comput. Biol. Medicine.

[4]  Yu-xiong Su,et al.  Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders , 2021, Cancers.

[5]  A. Philippakis,et al.  ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation , 2021, Circulation.

[6]  Deepak L. Bhatt,et al.  2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. , 2021, Circulation.

[7]  N. Sattar,et al.  Cardiovascular Outcomes Trials for Weight Loss Interventions: Another Tool for Cardiovascular Prevention? , 2021, Circulation.

[8]  D. Dey,et al.  Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. , 2021, Cardiovascular research.

[9]  Deepak L. Bhatt,et al.  Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. , 2020, The Lancet. Digital health.

[10]  D. Dey,et al.  Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia. , 2020, JACC. Cardiovascular imaging.

[11]  D. Dey,et al.  Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT. , 2020, European heart journal cardiovascular Imaging.

[12]  J. Knuuti 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC) , 2020, Russian Journal of Cardiology.

[13]  Artur Dubrawski,et al.  Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks , 2020, IEEE Journal of Biomedical and Health Informatics.

[14]  Damini Dey,et al.  5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects: Results From REFINE SPECT. , 2020, JACC. Cardiovascular imaging.

[15]  Marco Valgimigli,et al.  2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. , 2019, European heart journal.

[16]  E. V. van Beek,et al.  A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography , 2019, European heart journal.

[17]  Ida Scheel,et al.  Time-to-Event Prediction with Neural Networks and Cox Regression , 2019, J. Mach. Learn. Res..

[18]  I. Cha,et al.  Deep learning-based survival prediction of oral cancer patients , 2019, Scientific Reports.

[19]  Jackson T. Wright,et al.  2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. , 2018, Journal of the American Society of Hypertension : JASH.

[20]  Piotr J. Slomka,et al.  Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT) , 2018, Journal of Nuclear Cardiology.

[21]  Jackson T. Wright,et al.  2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. , 2018, Journal of the American College of Cardiology.

[22]  Changhee Lee,et al.  DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks , 2018, AAAI.

[23]  Uri Shaham,et al.  DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network , 2016, BMC Medical Research Methodology.

[24]  P. Muntner,et al.  Response to Letter to editor "2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults". , 2018, Journal of the American Society of Hypertension : JASH.

[25]  Jeffrey Dean,et al.  Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.

[26]  D. Brat,et al.  Predicting cancer outcomes from histology and genomics using convolutional networks , 2017, Proceedings of the National Academy of Sciences.

[27]  Joshua E. Lewis,et al.  Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models , 2017, Scientific Reports.

[28]  Sylvie Chevret,et al.  Summary measure of discrimination in survival models based on cumulative/dynamic time-dependent ROC curves , 2016, Statistical methods in medical research.

[29]  Jason P. Fine,et al.  Statistical Primer for Cardiovascular Research Introduction to the Analysis of Survival Data in the Presence of Competing Risks , 2022 .

[30]  J. Candell‐Riera,et al.  Warranty periods for normal myocardial perfusion stress SPECT , 2015, Journal of Nuclear Cardiology.

[31]  Gary S Collins,et al.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.

[32]  R. Thomson,et al.  Shared Decision Making: A Model for Clinical Practice , 2012, Journal of General Internal Medicine.

[33]  M. Pencina,et al.  On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data , 2011, Statistics in medicine.

[34]  A. Iskandrian,et al.  The effects of medications on myocardial perfusion. , 2008, Journal of the American College of Cardiology.

[35]  Kevin E. Kip,et al.  The problem with composite end points in cardiovascular studies: the story of major adverse cardiac events and percutaneous coronary intervention. , 2008, Journal of the American College of Cardiology.

[36]  D. Berman,et al.  Automated quantification of myocardial perfusion SPECT using simplified normal limits , 2004, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[37]  Hybrid Cardiac Imaging for Clinical Decision-Making: From Diagnosis to Prognosis , 2022 .

[38]  J. Saiz,et al.  Right‐sided non‐recurrent laryngeal nerve without any vascular anomaly: an anatomical trap , 2021, ANZ journal of surgery.

[39]  P. Kellman,et al.  The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence Based Approach Using Perfusion Mapping , 2020 .

[40]  W. Wijns The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC) , 2019 .

[41]  B. Gersh,et al.  The Problem With Composite End Points in Cardiovascular Studies: The Story of Major Adverse Cardiac Events and Percutaneous Coronary Intervention , 2009 .

[42]  D.,et al.  Regression Models and Life-Tables , 2022 .