| The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment MA Haendel, CG Chute, TD Bennett, DA Eichmann, J Guinney, WA Kibbe, ... Journal of the American Medical Informatics Association 28 (3), 427-443, 2021 | 506 | 2021 |
| Clinical characterization and prediction of clinical severity of SARS-CoV-2 infection among US adults using data from the US National COVID Cohort Collaborative TD Bennett, RA Moffitt, JG Hajagos, B Amor, A Anand, MM Bissell, ... JAMA network open 4 (7), e2116901-e2116901, 2021 | 226 | 2021 |
| Analysis of discrepancies between pulse oximetry and arterial oxygen saturation measurements by race and ethnicity and association with organ dysfunction and mortality AKI Wong, M Charpignon, H Kim, C Josef, AAH De Hond, JJ Fojas, ... JAMA network open 4 (11), e2131674-e2131674, 2021 | 196 | 2021 |
| Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU R Kamaleswaran, O Akbilgic, MA Hallman, AN West, RL Davis, SH Shah Pediatric Critical Care Medicine 19 (10), e495-e503, 2018 | 127 | 2018 |
| Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program MD Brannock, RF Chew, AJ Preiss, EC Hadley, S Redfield, JA McMurry, ... Nature communications 14 (1), 2914, 2023 | 110 | 2023 |
| A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length R Kamaleswaran, R Mahajan, O Akbilgic Physiological measurement 39 (3), 035006, 2018 | 100 | 2018 |
| A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier F van Wyk, A Khojandi, A Mohammed, E Begoli, RL Davis, ... International journal of medical informatics 122, 55-62, 2019 | 73 | 2019 |
| Autotriage-an open source edge computing raspberry pi-based clinical screening system C Hegde, Z Jiang, PB Suresha, J Zelko, S Seyedi, MA Smith, DW Wright, ... medrxiv, 2020.04. 09.20059840, 2020 | 62 | 2020 |
| Machine learning identifies complicated sepsis course and subsequent mortality based on 20 genes in peripheral blood immune cells at 24 H post-ICU admission S Banerjee, A Mohammed, HR Wong, N Palaniyar, R Kamaleswaran Frontiers in immunology 12, 592303, 2021 | 59 | 2021 |
| Improving prediction performance using hierarchical analysis of real-time data: a sepsis case study F Van Wyk, A Khojandi, R Kamaleswaran IEEE journal of biomedical and health informatics 23 (3), 978-986, 2019 | 56 | 2019 |
| Ideal algorithms in healthcare: explainable, dynamic, precise, autonomous, fair, and reproducible TJ Loftus, PJ Tighe, T Ozrazgat-Baslanti, JP Davis, MM Ruppert, Y Ren, ... PLOS digital health 1 (1), e0000006, 2022 | 52 | 2022 |
| Cardiac rhythm classification from a short single lead ECG recording via random forest R Mahajan, R Kamaleswaran, JA Howe, O Akbilgic 2017 Computing in Cardiology (CinC), 1-4, 2017 | 50 | 2017 |
| PhysOnline: an open source machine learning pipeline for real-time analysis of streaming physiological waveform JR Sutton, R Mahajan, O Akbilgic, R Kamaleswaran IEEE journal of biomedical and health informatics 23 (1), 59-65, 2018 | 48 | 2018 |
| eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19 L Singhal, Y Garg, P Yang, A Tabaie, AI Wong, A Mohammed, L Chinthala, ... PloS one 16 (9), e0257056, 2021 | 41 | 2021 |
| Temporal differential expression of physiomarkers predicts sepsis in critically ill adults A Mohammed, F Van Wyk, LK Chinthala, A Khojandi, RL Davis, ... Shock 56 (1), 58-64, 2021 | 41 | 2021 |
| How much data should we collect? A case study in sepsis detection using deep learning F van Wyk, A Khojandi, R Kamaleswaran, O Akbilgic, S Nemati, RL Davis 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT …, 2017 | 34 | 2017 |
| Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients A Mohammed, Y Cui, VR Mas, R Kamaleswaran Scientific reports 9 (1), 11270, 2019 | 33 | 2019 |
| Electrocardiographic changes predate Parkinson’s disease onset O Akbilgic, R Kamaleswaran, A Mohammed, GW Ross, K Masaki, ... Scientific reports 10 (1), 11319, 2020 | 32 | 2020 |
| A wearable multimodal sensing system for tracking changes in pulmonary fluid status, lung sounds, and respiratory markers JA Sanchez-Perez, JA Berkebile, BN Nevius, GC Ozmen, CJ Nichols, ... Sensors 22 (3), 1130, 2022 | 30 | 2022 |
| Machine learning methods to predict acute respiratory failure and acute respiratory distress syndrome AKI Wong, PC Cheung, R Kamaleswaran, GS Martin, AL Holder Frontiers in big Data 3, 579774, 2020 | 24 | 2020 |
Akram MohammedUniversity of Tennessee Health Science Center (Bioinformatics)uthsc.edu üzerinde doğrulanmış e-posta adresine sahip