Kipp Johnson
Kipp Johnson
Northwestern University
Потвърден имейл адрес: nm.org - Начална страница
Artificial intelligence in cardiology
KW Johnson, J Torres Soto, BS Glicksberg, K Shameer, R Miotto, M Ali, ...
Journal of the American College of Cardiology 71 (23), 2668-2679, 2018
Prevalence and impact of myocardial injury in patients hospitalized with COVID-19 infection
A Lala, KW Johnson, JL Januzzi, AJ Russak, I Paranjpe, F Richter, S Zhao, ...
Journal of the American college of cardiology 76 (5), 533-546, 2020
Machine learning in cardiovascular medicine: are we there yet?
K Shameer, KW Johnson, BS Glicksberg, JT Dudley, PP Sengupta
Heart 104 (14), 1156-1164, 2018
Deep learning for cardiovascular medicine: a practical primer
C Krittanawong, KW Johnson, RS Rosenson, Z Wang, M Aydar, U Baber, ...
European heart journal 40 (25), 2058-2073, 2019
Pathology of peripheral artery disease in patients with critical limb ischemia
N Narula, AJ Dannenberg, JW Olin, DL Bhatt, KW Johnson, G Nadkarni, ...
Journal of the American College of Cardiology 72 (18), 2152-2163, 2018
Machine learning prediction in cardiovascular diseases: a meta-analysis
C Krittanawong, HUH Virk, S Bangalore, Z Wang, KW Johnson, R Pinotti, ...
Scientific reports 10 (1), 16057, 2020
Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai heart failure cohort
K Shameer, KW Johnson, A Yahi, R Miotto, LI Li, D Ricks, J Jebakaran, ...
Pacific Symposium on Biocomputing 2017, 276-287, 2017
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
S Raghunath, AE Ulloa Cerna, L Jing, DP VanMaanen, J Stough, ...
Nature medicine 26 (6), 886-891, 2020
Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation
A Vaid, S Somani, AJ Russak, JK De Freitas, FF Chaudhry, I Paranjpe, ...
Journal of medical Internet research 22 (11), e24018, 2020
Clinical characteristics of hospitalized Covid-19 patients in New York City
I Paranjpe, AJ Russak, JK De Freitas, A Lala, R Miotto, A Vaid, ...
MedRxiv, 2020.04. 19.20062117, 2020
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
S Raghunath, JM Pfeifer, AE Ulloa-Cerna, A Nemani, T Carbonati, L Jing, ...
Circulation 143 (13), 1287-1298, 2021
Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation …
PP Sengupta, S Shrestha, B Berthon, E Messas, E Donal, GH Tison, ...
Cardiovascular Imaging 13 (9), 2017-2035, 2020
Federated learning of electronic health records to improve mortality prediction in hospitalized patients with COVID-19: machine learning approach
A Vaid, SK Jaladanki, J Xu, S Teng, A Kumar, S Lee, S Somani, ...
JMIR medical informatics 9 (1), e24207, 2021
Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management
C Krittanawong, AJ Rogers, KW Johnson, Z Wang, MP Turakhia, ...
Nature Reviews Cardiology 18 (2), 75-91, 2021
Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning
K Shameer, BS Glicksberg, R Hodos, KW Johnson, MA Badgeley, ...
Briefings in bioinformatics 19 (4), 656-678, 2018
Enabling precision cardiology through multiscale biology and systems medicine
KW Johnson, K Shameer, BS Glicksberg, B Readhead, PP Sengupta, ...
Basic to Translational Science 2 (3), 311-327, 2017
Automated disease cohort selection using word embeddings from Electronic Health Records
BS Glicksberg, R Miotto, KW Johnson, K Shameer, L Li, R Chen, ...
PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018: Proceedings of the Pacific Symposium …, 2018
Integrating blockchain technology with artificial intelligence for cardiovascular medicine
C Krittanawong, AJ Rogers, M Aydar, E Choi, KW Johnson, Z Wang, ...
Nature Reviews Cardiology 17 (1), 1-3, 2020
Association of hemoglobin A1c levels with use of sulfonylureas, dipeptidyl peptidase 4 inhibitors, and thiazolidinediones in patients with type 2 diabetes treated with …
R Vashisht, K Jung, A Schuler, JM Banda, RW Park, S Jin, L Li, JT Dudley, ...
JAMA network open 1 (4), e181755-e181755, 2018
Intracoronary imaging, cholesterol efflux, and transcriptomes after intensive statin treatment: the YELLOW II study
AS Kini, Y Vengrenyuk, K Shameer, A Maehara, M Purushothaman, ...
Journal of the American College of Cardiology 69 (6), 628-640, 2017
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