| AKI in hospitalized patients with COVID-19 L Chan, K Chaudhary, A Saha, K Chauhan, A Vaid, S Zhao, I Paranjpe, ... Journal of the American Society of Nephrology 32 (1), 151-160, 2021 | 803 | 2021 |
| Mobile Applications for Diabetes Self-Management: Status and Potential O El-Gayar, P Timsina, N Nawar, W Eid Journal of Diabetes Science and Technology 7 (1), 247-262, 2013 | 463 | 2013 |
| Anticoagulation, bleeding, mortality, and pathology in hospitalized patients with COVID-19 GN Nadkarni, A Lala, E Bagiella, HL Chang, PR Moreno, E Pujadas, ... Journal of the American College of Cardiology 76 (16), 1815-1826, 2020 | 423 | 2020 |
| A systematic review of IT for diabetes self-management: are we there yet? O El-Gayar, P Timsina, N Nawar, W Eid International journal of medical informatics 82 (8), 637-652, 2013 | 201 | 2013 |
| Using machine learning to predict ICU transfer in hospitalized COVID-19 patients FY Cheng, H Joshi, P Tandon, R Freeman, DL Reich, M Mazumdar, ... Journal of clinical medicine 9 (6), 1668, 2020 | 192 | 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 | 183 | 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 | 127 | 2020 |
| Anticoagulation, mortality, bleeding and pathology among patients hospitalized with COVID-19: a single health system study GN Nadkarni, A Lala, E Bagiella, HL Chang, P Moreno, E Pujadas, ... J Am Coll Cardiol 76 (16), 1815-1826, 2020 | 91 | 2020 |
| Mount Sinai COVID Informatics Center (MSCIC): AKI in hospitalized patients with COVID-19 L Chan, K Chaudhary, A Saha, K Chauhan, A Vaid, S Zhao, I Paranjpe, ... J Am Soc Nephrol 32 (1), 151-160, 2021 | 88 | 2021 |
| Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19 P Parchure, H Joshi, K Dharmarajan, R Freeman, DL Reich, M Mazumdar, ... BMJ supportive & palliative care 12 (e3), e424-e431, 2022 | 58 | 2022 |
| Opportunities for business intelligence and big data analytics in evidence based medicine O El-Gayar, P Timsina 2014 47th Hawaii International Conference on System Sciences, 749-757, 2014 | 57 | 2014 |
| MEWS++: enhancing the prediction of clinical deterioration in admitted patients through a machine learning model A Kia, P Timsina, HN Joshi, E Klang, RR Gupta, RM Freeman, DL Reich, ... Journal of clinical medicine 9 (2), 343, 2020 | 46 | 2020 |
| A comparative analysis of semi-supervised learning: the case of article selection for medical systematic reviews J Liu, P Timsina, O El-Gayar Information Systems Frontiers 20, 195-207, 2018 | 41 | 2018 |
| Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City I Paranjpe, AJ Russak, JK De Freitas, A Lala, R Miotto, A Vaid, ... BMJ open 10 (11), e040736, 2020 | 35 | 2020 |
| Advanced analytics for the automation of medical systematic reviews P Timsina, J Liu, O El-Gayar Information Systems Frontiers 18, 237-252, 2016 | 33 | 2016 |
| Machine learning to predict mortality and critical events in covid-19 positive new york city patients A Vaid, S Somani, AJ Russak, JK De Freitas, FF Chaudhry, I Paranjpe, ... medRxiv, 2020.04. 26.20073411, 2020 | 29 | 2020 |
| Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach E Klang, BR Kummer, NS Dangayach, A Zhong, MA Kia, P Timsina, ... Scientific reports 11 (1), 1381, 2021 | 24 | 2021 |
| Machine learning to predict mortality and critical events in COVID-19 positive New York city patients: a cohort study A Vaid, S Somani, AJ Russak, JK De Freitas, FF Chaudhry, I Paranjpe, ... J. Med. Internet Res 22 (11), 2020 | 23 | 2020 |
| Using semi-supervised learning for the creation of medical systematic review: An exploratory analysis P Timsina, J Liu, O El-Gayar, Y Shang 2016 49th Hawaii international conference on system sciences (HICSS), 1195-1203, 2016 | 19 | 2016 |
| Predictive approaches for acute dialysis requirement and death in COVID-19 A Vaid, L Chan, K Chaudhary, SK Jaladanki, I Paranjpe, A Russak, A Kia, ... Clinical Journal of the American Society of Nephrology 16 (8), 1158-1168, 2021 | 15 | 2021 |
Matthew A LevinIcahn School of Medicine at Mount SinaiVerified email at mlevin.net
