Machine learning to assist clinical decision-making during the COVID-19 pandemic
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Background: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for “Emergency ML.” Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
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ML algorithms can be incorporated into several areas across the spectrum of care, either for disease management or in perioperative settings.
“ML algorithms can be incorporated into several areas across the spectrum of care, either for disease management or in perioperative settings (15).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [15]
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Richardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., Davidson, K. W., and the Northwell COVID-19 Research Consortium, Barnaby, D. P., Becker, L. B., Chelico, J. D., et al. (2020) · JAMA · doi:10.1001/jama.2020.6775 · Cited by 9,254 · see citing works
Self-citationLauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., Azman, A. S., Reich, N. G., & Lessler, J. (2020) · Annals of Internal Medicine · doi:10.7326/m20-0504 · Cited by 5,970 · see citing works
Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020) · Science · doi:10.1126/science.abb3221 · Cited by 3,633 · see citing works
CDC COVID-19 Response Team, CDC COVID-19 Response Team, Bialek, S. R., Boundy, E. O., Bowen, V. B., Chow, N. A., Cohn, A. C., Dowling, N. F., Ellington, S., Gierke, R., et al. (2020) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6912e2 · Cited by 2,278 · see citing works
Covid-19 — Navigating the Uncharted
Fauci, A. S., Lane, H. C., & Redfield, R. (2020) · New England Journal of Medicine · doi:10.1056/nejme2002387 · Cited by 1,983 · see citing works
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020) · Radiology · doi:10.1148/radiol.2020200905 · Cited by 1,927 · see citing works
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,668 · see citing works
Emergency department crowding: A systematic review of causes, consequences and solutions
Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018) · PLOS ONE · doi:10.1371/journal.pone.0203316 · Cited by 1,386 · see citing works
An interpretable mortality prediction model for COVID-19 patients
Li, Y., Zhang, H., Gonçalves, J. M., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jing, L., Zhang, M., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0180-7 · Cited by 1,304 · see citing works
Dorsey, E. R., & Topol, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmra1601705 · Cited by 1,211 · see citing works
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.04.010 · Cited by 1,209 · see citing works
Ahmed, S. F., Quadeer, A. A., & McKay, M. R. (2020) · Viruses · doi:10.3390/v12030254 · Cited by 1,116 · see citing works
A clinically applicable approach to continuous prediction of future acute kidney injury
Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Nature · doi:10.1038/s41586-019-1390-1 · Cited by 1,086 · see citing works
A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)Preprint
Wang, S., Kang, B., Ma, J., et al. (2020) · medRxiv · doi:10.1101/2020.02.14.20023028 · Cited by 1,035 · see citing works
Genotype and phenotype of COVID-19: Their roles in pathogenesis
Mousavizadeh, L., & Ghasemi, S. (2020) · Journal of Microbiology Immunology and Infection · doi:10.1016/j.jmii.2020.03.022 · Cited by 961 · see citing works
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 960 · see citing works
A Framework for Rationing Ventilators and Critical Care Beds During the COVID-19 Pandemic
White, D. B., & Lo, B. (2020) · JAMA · doi:10.1001/jama.2020.5046 · Cited by 826 · see citing works
Covid-19: a remote assessment in primary care
Greenhalgh, T., Koh, G. C. H., & Car, J. (2020) · BMJ · doi:10.1136/bmj.m1182 · Cited by 705 · see citing works
Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19
Yang, R., Li, X., Liu, H., Zhen, Y., Zhang, X., Xiong, Q., Luo, Y., Gao, C., & Zeng, W. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200047 · Cited by 684 · see citing works
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis
Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18685-1 · Cited by 591 · see citing works
Evaluating deep learning architectures for Speech Emotion Recognition
Fayek, H. M., Lech, M., & Cavedon, L. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.02.013 · Cited by 554 · see citing works
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system
Wang, B., Jin, S., Yan, Q., Xu, H., Luo, C. W., Wei, L., Zhao, W. Z., Hou, X., Ma, W., Xu, Z., et al. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106897 · Cited by 539 · see citing works
Emergency department triage prediction of clinical outcomes using machine learning models
Raita, Y., Goto, T., Faridi, M. K., Brown, D., Camargo, C. A., & Hasegawa, K. (2019) · Critical Care · doi:10.1186/s13054-019-2351-7 · Cited by 533 · see citing works
Gong, J., Ou, J., Qiu, X., Jie, Y., Chen, Y., Yuan, L., Cao, J., Tan, M., Xu, W., Zheng, F., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa443 · Cited by 513 · see citing works
Shi, Y., Yu, X., Zhao, H., Wang, H., Zhao, R.-H., & Sheng, J. (2020) · Critical Care · doi:10.1186/s13054-020-2833-7 · Cited by 510 · see citing works
Lung Infection Quantification of COVID-19 in CT Images with Deep LearningPreprint
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D. G., & Shi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04655 · Cited by 489 · see citing works
Jiang, X., Coffee, M. P., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., et al. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.010691 · Cited by 450 · see citing works
A machine learning-based model for survival prediction in patients with severe COVID-19 infectionPreprint
Li, Y., Zhang, H., Gonçalves, J. M., et al. (2020) · medRxiv · doi:10.1101/2020.02.27.20028027 · Cited by 426 · see citing works
Levin, S., Toerper, M., Hamrock, E., Hinson, J. S., Barnes, S., Gardner, H., Dugas, A., Linton, B., Kirsch, T., & Kelen, G. (2018) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2017.08.005 · Cited by 424 · see citing works
Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score
Guo, L., Wei, D., Zhang, X., Wu, Y., Li, Q. Y., Zhou, M., & Qu, J. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02752 · Cited by 412 · see citing works
Rao, A. S. R. S., & Vázquez, J. A. (2020) · Infection Control and Hospital Epidemiology · doi:10.1017/ice.2020.61 · Cited by 394 · see citing works
Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity
Zhan, A., Mohan, S., Tarolli, C. G., Schneider, R. B., Adams, J., Sharma, S., Elson, M. J., Spear, K. L., Glidden, A. M., Little, M. A., et al. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2018.0809 · Cited by 386 · see citing works
Shi, F., Xia, L., Shan, F., Song, B., Wu, D., Wei, Y., Yuan, H., Jiang, H., He, Y., Gao, Y., et al. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abe838 · Cited by 379 · see citing works
Frizzell, J. D., Liang, L., Schulte, P. J., Yancy, C. W., Heidenreich, P. A., Hernandez, A. F., Bhatt, D. L., Fonarow, G. C., & Laskey, W. K. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.3956 · Cited by 361 · see citing works
Santosh, K. C. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01562-1 · Cited by 341 · see citing works
Brekke, I. J., Puntervoll, L. H., Pedersen, P. B., Kellett, J. G., & Brabrand, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210875 · Cited by 314 · see citing works
Jin, S., Wang, B., Xu, H., et al. (2020) · medRxiv · doi:10.1101/2020.03.19.20039354 · Cited by 306 · see citing works
Golas, S. B., Shibahara, T., Agboola, S., Otaki, H., Sato, J., Nakae, T., Hisamitsu, T., Kojima, G., Felsted, J., Kakarmath, S., et al. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0620-z · Cited by 287 · see citing works
Estimation of risk factors for COVID-19 mortality - preliminary resultsPreprint
Caramelo, F. J., Ferreira, N. R., & Oliveiros, B. (2020) · medRxiv · doi:10.1101/2020.02.24.20027268 · Cited by 236 · see citing works
Eriksson, C. O., Stoner, R. C., Eden, K., Newgard, C. D., & Guise, J. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3936-3 · Cited by 209 · see citing works
COVID-19 diagnosis prediction in emergency care patients: a machine learning approachPreprint
Batista, A. F. D. M., Miraglia, J. L., Donato, T. H. R., & Filho, A. D. P. C. (2020) · medRxiv · doi:10.1101/2020.04.04.20052092 · Cited by 199 · see citing works
Development and Evaluation of an AI System for COVID-19 DiagnosisPreprint
Jin, C., Chen, W., Cao, Y., et al. (2020) · medRxiv · doi:10.1101/2020.03.20.20039834 · Cited by 194 · see citing works
COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patientsPreprint
Song, C., Xu, J., He, J., & Lu, Y. (2020) · medRxiv · doi:10.1101/2020.03.05.20031906 · Cited by 170 · see citing works
Kipnis, P., Turk, B. J., Wulf, D. A., LaGuardia, J. C., Liu, V. X., Churpek, M. M., Brufau, S. R., & Escobar, G. J. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.09.013 · Cited by 167 · see citing works
Xie, J., Hungerford, D., Chen, H., Abrams, S. T., Li, S., Wang, G., Wang, Y., Kang, H., Bonnett, L. J., Zheng, R., et al. (2020) · SSRN Electronic Journal · doi:10.1101/2020.03.28.20045997 · Cited by 157 · see citing works
Classification of Coronavirus ( COVID ‐19) from X‐ray and CT images using shrunken features
Öztürk, Ş., Özkaya, U., & Barstuğan, M. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22469 · Cited by 154 · see citing works
Porter, P., Abeyratne, U. R., Swarnkar, V. R., Tan, J., Ng, T.-W., Brisbane, J. M., Speldewinde, D., Choveaux, J., Sharan, R. V., Kosasih, K., et al. (2019) · Respiratory Research · doi:10.1186/s12931-019-1046-6 · Cited by 139 · see citing works
Building a COVID-19 vulnerability index
DeCaprio, D., Gartner, J., McCall, C. J., Burgess, T., Garcia, K. M., Kothari, S., & Sayed, S. (2020) · Journal of Medical Artificial Intelligence · doi:10.21037/jmai-20-47 · Cited by 134 · see citing works
Gong, J., Ou, J., Qiu, X., et al. (2020) · medRxiv · doi:10.1101/2020.03.17.20037515 · Cited by 129 · see citing works
Lu, J., Hu, S., Fan, R., Liu, Z., Yin, X., Wang, Q., Lv, Q., Cai, Z., Li, H., Hu, Y., et al. (2020) · medRxiv · doi:10.1101/2020.02.20.20025510 · Cited by 122 · see citing works
Exploring the Potential of Predictive Analytics and Big Data in Emergency Care
Janke, A. T., Overbeek, D. L., Kocher, K. E., & Levy, P. D. (2015) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2015.06.024 · Cited by 108 · see citing works
Brajer, N., Cozzi, B., Gao, M., Nichols, M., Revoir, M., Balu, S., Futoma, J., Bae, J., Setji, N. P., Hernandez, A. F., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.20733 · Cited by 107 · see citing works
Building a COVID-19 Vulnerability IndexPreprint
DeCapprio, D., Gartner, J., McCall, C. J., et al. (2020) · medRxiv · doi:10.1101/2020.03.16.20036723 · Cited by 100 · see citing works
Predicting COVID-19 Malignant Progression with AI TechniquesPreprint
Bai, X., Fang, C., Zhou, Y., Bai, S., Liu, Z., Xia, L., Chen, Q., Xu, Y., Xia, T., Gong, S., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3557984 · Cited by 97 · see citing works
Zampieri, F. G., Costa, E. L. V., Iwashyna, T. J., de Carvalho, C. R. R., Damiani, L. P., Taniguchi, L. U., Amato, M. B. P., & Cavalcanti, A. B. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.02.026 · Cited by 76 · see citing works
A predictive model to estimate survival of hospitalized COVID-19 patients from admission dataPreprint
Levy, T. J., Richardson, S. I., Coppa, K., Barnaby, D. P., McGinn, T. G., Becker, L. B., Davidson, K. W., Hirsch, J. S., Zanos, T. P., Cohen, S. L., et al. (2020) · medRxiv · doi:10.1101/2020.04.22.20075416 · Cited by 52 · see citing works
Self-citationDevelopment and utilization of an intelligent application for aiding COVID-19 diagnosisPreprint
Meng, Z., Wang, M., Song, H., Guo, S., Zhou, Y., Li, W., Zhou, Y., Li, M., Song, X., Zhou, Y., et al. (2020) · medRxiv · doi:10.1101/2020.03.18.20035816 · Cited by 48 · see citing works
Conducting clinical trials in outbreak settings: Points to consider
Lane, H. C., Marston, H. D., & Fauci, A. S. (2016) · Clinical Trials · doi:10.1177/1740774515618198 · Cited by 46 · see citing works
(2020) · Utrecht University Repository (Utrecht University) · Cited by 46 · see citing works
Classification of Coronavirus Images using Shrunken FeaturesPreprint
Öztürk, Ş., Özkaya, U., & Barstuğan, M. (2020) · medRxiv · doi:10.1101/2020.04.03.20048868 · Cited by 29 · see citing works
Moving Beyond Single-Parameter Early Warning Scores for Rapid Response System Activation*
Churpek, M. M., & Edelson, D. P. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000002105 · Cited by 17 · see citing works
Wang, T., Tschampel, T., Apostolova, E., & Velez, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.12127 · Cited by 4 · see citing works
Machine learning application to the reduction of ambulatory care sensitive admissions (ACSA)
Ngo, T., Georgescu, V., Gervet, C., Laurent, A., Libourel, T., & Mercier, G. (2019) · European Journal of Public Health · doi:10.1093/eurpub/ckz187.061 · Cited by 2 · see citing works
Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?Preprint
Ribers, M. A., & Ullrich, H. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3392196 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Evans M Armour S Hospital capacity crosses tipping point in U.S. coronavirus hot spots. In: the wall street journal 2020 New York Dow Jones & Company, Inc.
- Levy TJ, Richardson S, Coppa K, Barnaby DP, McGinn T, Becker LB, Davidson KW, Hirsch JS, Zanos T. Estimating survival of hospitalized COVID-19 patients from admission information. medRxiv. 2020.04.22.20075416.
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Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network
Marques, G., Agarwal, D., & Díez, I. D. L. T. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106691 · Cited by 401 · see citing works
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works
Involvement of Machine Learning Tools in Healthcare Decision Making
Jayatilake, S., & Ganegoda, G. U. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6679512 · Cited by 312 · see citing works
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210 · see citing works
Shamout, F. E., Shen, Y., Wu, N., Kaku, A., Park, J., Makino, T., Jastrzȩbski, S., Witowski, J. S., Wang, D., Zhang, B., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00453-0 · Cited by 153 · see citing works
Application of Artificial Intelligence in Shared Decision Making: Scoping Review
Rahimi, S. A., Cwintal, M., Huang, Y., Ghadiri, P., Grad, R. M., Poenaru, D., Gore, G. C., Zomahoun, H. T. V., Légaré, F., & Pluye, P. (2022) · JMIR Medical Informatics · doi:10.2196/36199 · Cited by 124 · see citing works
Bolourani, S., Brenner, M., Wang, P., McGinn, T. G., Hirsch, J. S., Barnaby, D. P., & Zanos, T. P. (2021) · Journal of Medical Internet Research · doi:10.2196/24246 · Cited by 111 · see citing works
Self-citationChen, J., Lim, C. P., Tan, K. H., Govindan, K., & Kumar, A. (2021) · Annals of Operations Research · doi:10.1007/s10479-021-04373-w · Cited by 104 · see citing works
Bioelectronic medicine: Preclinical insights and clinical advances
Pavlov, V. A., & Tracey, K. J. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.003 · Cited by 106 · see citing works
A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic
Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. K. M. N. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3062771 · Cited by 99 · see citing works
Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic
Alhasan, M., & Hasaneen, M. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101933 · Cited by 89 · see citing works
Shahid, O., Nasajpour, M., Pouriyeh, S., Parizi, R. M., Han, M., Valero, M., Li, F., Aledhari, M., & Sheng, Q. Z. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103751 · Cited by 75 · see citing works
Awake prone positioning and oxygen therapy in patients with COVID-19: the APRONOX study
Pérez‐Nieto, O. R., Martínez, D. E., Gutiérrez, M. A. G., Zamarrón-López, E. I., Mancilla-Galindo, J., Kammar‐García, A., Martínez-Camacho, M. Á., Deloya-Tomás, E., Díaz, J. S. S., Macías-García, L. A., et al. (2021) · European Respiratory Journal · doi:10.1183/13993003.00265-2021 · Cited by 74 · see citing works
Hatmal, M. M., Al-Hatamleh, M. A. I., Olaimat, A. N., Mohamud, R., Fawaz, M. A., Kateeb, E. T., Alkhairy, O. K., Tayyem, R., Lounis, M., Al‐Raeei, M., et al. (2022) · Vaccines · doi:10.3390/vaccines10030366 · Cited by 72 · see citing works
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Tiwari, S., Chanak, P., & Singh, S. K. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3142241 · Cited by 66 · see citing works
Saleem, F., ALGhamdi, A. S. A.-M., Alassafi, M. O., & AlGhamdi, S. A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19095099 · Cited by 57 · see citing works
Ge, J., Kim, W. R., Lai, J. C., & Kwong, A. J. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2022.03.003 · Cited by 57 · see citing works
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Nayak, J., Naik, B., Dinesh, P., Vakula, K., Rao, B. K., Ding, W., & Pelusi, D. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02102-7 · Cited by 51 · see citing works
Raymond, L., Castonguay, A., Doyon, O., & Paré, G. (2022) · Applied Nursing Research · doi:10.1016/j.apnr.2022.151604 · Cited by 46 · see citing works
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González‐González, M. A., Conde, S. V., Latorre, R., Thébault, S. C., Pratelli, M., Spitzer, N. C., Verkhratsky, A., Tremblay, M., Akçora, C. G., Hernandez-Reynoso, A. G., et al. (2024) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2024.1321872 · Cited by 44 · see citing works
The Clinical Information Systems Response to the COVID-19 Pandemic
Reeves, J. J., Pageler, N. M., Wick, E. C., Melton, G. B., Tan, Y.-H. G., Clay, B. J., & Longhurst, C. (2021) · Yearbook of Medical Informatics · doi:10.1055/s-0041-1726513 · Cited by 42 · see citing works
Karthik, R., Menaka, R., Hariharan, M., & Kathiresan, G. S. (2021) · IRBM · doi:10.1016/j.irbm.2021.07.002 · Cited by 30 · see citing works
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A method to quantify autonomic nervous system function in healthy, able-bodied individuals
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Self-citationThe Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery
Wong, L.-W., Mak, S.-H., Goh, B. H., & Lee, W.-L. (2022) · Diagnostics · doi:10.3390/diagnostics13010022 · Cited by 25 · see citing works
Zanville, N., Cohen, B., Gray, T. F., Phillips, J., Linder, L. A., Starkweather, A. R., Yeager, K. A., & Cooley, M. E. (2021) · Oncology nursing forum · doi:10.1188/21.onf.131-145 · Cited by 22 · see citing works
Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications
Alsamhi, S. H., Hawbani, A., Shvetsov, A. V., & Kumar, S. (2023) · Advances in Human-Computer Interaction · doi:10.1155/2023/9992393 · Cited by 25 · see citing works
Javed, H., Muqeet, H. A., Danesh, A., Rehman, A. U., Javed, T., & Bermak, A. (2024) · IEEE Access · doi:10.1109/access.2024.3443812 · Cited by 19 · see citing works
Machine learning and IoT in healthcare: Recent advancements, challenges & future direction
Zonayed, M., Tasnim, R., Jhara, S. S., Mimona, M. A., Hussein, M. R., Mobarak, M. H., & Salma, U. (2025) · Advances in Biomarker Sciences and Technology · doi:10.1016/j.abst.2025.08.006 · Cited by 24 · see citing works
Hašimbegović, E., Papp, L., Grahovac, M., Krajnc, D., Poschner, T., Hasan, W., Andreas, M., Gross, C., Strouhal, A., Delle‐Karth, G., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11111062 · Cited by 17 · see citing works
Venaik, A., Kumari, R., Venaik, U., & Nayyar, A. (2022) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.2022010107 · Cited by 16 · see citing works
Aljahdali, M. O., Molla, M. H. R., & Ahammad, F. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.101367 · Cited by 14 · see citing works
Syleouni, M., Karavasiloglou, N., Manduchi, L., Wanner, M., Korol, D., Ortelli, L., Bordoni, A., & Rohrmann, S. (2023) · International Journal of Cancer · doi:10.1002/ijc.34568 · Cited by 13 · see citing works
Predicting intensive care need for COVID-19 patients using deep learning on chest radiography
Li, H., Drukker, K., Hu, Q., Whitney, H. M., Fuhrman, J. D., & Giger, M. L. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.4.044504 · Cited by 12 · see citing works
Prediction of patient choice tendency in medical decision-making based on machine learning algorithm
Lyu, Y., Xu, Q., Yang, Z., & Liu, J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1087358 · Cited by 12 · see citing works
Data Classification Model for COVID-19 Pandemic
Folorunso, S. O., Awotunde, J. B., Adeboye, N. O., & Matiluko, O. E. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-77302-1_6 · Cited by 13 · see citing works
Chen, Z., Russo, N. W., Miller, M. M., Murphy, R. X., & Burmeister, D. B. (2021) · Journal of the American College of Emergency Physicians Open · doi:10.1002/emp2.12406 · Cited by 11 · see citing works
Clustering Proposal Support for the COVID-19 Making Decision Process in a Data Demanding Scenario
Orjuela-Cañón, Á. D., & Perdómo, O. (2021) · IEEE Latin America Transactions · doi:10.1109/tla.2021.9451250 · Cited by 9 · see citing works
Migriño, J. R., & Batangan, A. R. U. (2021) · Western Pacific surveillance response journal · doi:10.5365/wpsar.2021.12.3.831 · Cited by 9 · see citing works
Guerrier, C., McDonnell, C., Magoč, T., Fishe, J. N., & Harle, C. A. (2022) · MDM Policy & Practice · doi:10.1177/23814683221089844 · Cited by 8 · see citing works
Emerging technologies for the management of COVID19: A review
Zafar, N., & Ahamed, J. (2022) · Sustainable Operations and Computers · doi:10.1016/j.susoc.2022.05.002 · Cited by 8 · see citing works
Translating evidence into practice during the COVID-19 pandemic: pitfalls and mileages
Mangoni, A. A., & Erre, G. L. (2021) · Therapeutic Advances in Drug Safety · doi:10.1177/2042098621998876 · Cited by 8 · see citing works
Uddin, M. J., Gholap, A., Khan, T., Proma, A. Y., Masud, S., Choonara, Y., Gowri, S., Webster, T. J., & Faiyazuddin, M. (2024) · Journal of Biomedical Nanotechnology · doi:10.1166/jbn.2024.3933 · Cited by 6 · see citing works
Advice quality and source disclosure shape trust in AI-generated ethical advice
Meincke, L., Nave, G., & Terwiesch, C. (2026) · Scientific Reports · doi:10.1038/s41598-026-44258-1 · Cited by 8 · see citing works
Barreto, T. D. O., Veras, N. V. R., Cardoso, P. H., Fernandes, F. R. D. S., Medeiros, L. P. D. S., Bezerra, M. V., de Andrade, F. M. Q., Pinheiro, C. D. O., Sánchez-Gendriz, I., Caldeira-Silva, G. J. P., et al. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1290022 · Cited by 7 · see citing works
Miranda, I. D. S., Cardoso, G. S., Pahar, M., de Oliveira, G. G., & Niesler, T. (2021) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi50953.2021.9508548 · Cited by 7 · see citing works
Ayat, M., Qureshi, S. M., Malikah, M., & Kang, C. (2023) · Journal of Engineering Design and Technology · doi:10.1108/jedt-01-2023-0033 · Cited by 6 · see citing works
Innovative solutions for disease management
Carmina, D., Benfenati, V., Simonelli, C., Rotolo, A., Cardano, P., Grovale, N., Stefano, L. M. D. S., Santo, T. D., Zamboni, R., Palermo, V., et al. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00131-4 · Cited by 6 · see citing works
Biswas, R. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1007466 · Cited by 5 · see citing works
Medical Image Processing and Deep Learning to Diagnose COVID-19 with CT Images
Sebdani, A. M., & Mostafavi, A. (2021) · doi:10.1109/ipria53572.2021.9483563 · Cited by 5 · see citing works
Machine learning for early prediction of secondary cancer after radiotherapy
SADEGHI, H., Seif, F., & Khanmohammadi, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-21177-1 · Cited by 4 · see citing works
Clark, K. S. B., Rudell, E., Setiadi, D., Agrawal, T., & Oliver, B. J. (2024) · The Permanente Journal · doi:10.7812/tpp/23.157 · Cited by 4 · see citing works
Utilizing patient data: A tutorial on predicting second cancer with machine learning models
SADEGHI, H., Seif, F., Farahani, E. H., Khanmohammadi, S., & Nahidinezhad, S. (2024) · Cancer Medicine · doi:10.1002/cam4.70231 · Cited by 4 · see citing works
Evolution of International Psychiatry
Girardi, P., Bonanni, L., Kotzalidis, G. D., Fiaschè, F., & CASALE, A. D. (2020) · Psychiatry International · doi:10.3390/psychiatryint1010004 · Cited by 4 · see citing works
Exploring the Potential of Quantum Computing in AI, Medical Advancements, and Cyber Security
Palvadi, S. K. (2023) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-1479-1.ch004 · Cited by 4 · see citing works
Zhou, Y., Swoboda, T. K., Ye, Z., Barbaro, M., Blalock, J., Zheng, D., & Wang, H. (2023) · Journal of Clinical Medicine Research · doi:10.14740/jocmr4862 · Cited by 3 · see citing works
Gerevini, A., Maroldi, R., Olivato, M., Putelli, L., & Serina, I. (2023) · IEEE Access · doi:10.1109/access.2023.3296260 · Cited by 3 · see citing works
Paradigm Shift Towards Federated Learning for COVID-19 Detection: A Survey
Krithika, L. B. (2023) · Iraqi Journal of Science · doi:10.24996/ijs.2023.64.7.37 · Cited by 3 · see citing works
Application of Machine Learning for COVID-19 Data Analysis
Shinde-Pawar, M., Pujari, R., Mujawar, A., Shah, A. S., Gala, D. M., & Patil, B. V. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.03.053 · Cited by 3 · see citing works
Identification of High Death Risk Coronavirus Disease-19 Patients using Blood Tests
Hosseingholi, E. Z., Maddahi, S., Jabbari, S., & Molavi, G. (2022) · Advanced Biomedical Research · doi:10.4103/abr.abr_178_21 · Cited by 3 · see citing works
Qin, Q., Li, Q., Zhu, G., Yu, H., Peng, M., Wu, S., Xu, X., Gu, W., & Guo, X. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1430899 · Cited by 2 · see citing works
Eterafi, M., Fouladi, N., Amanzadeh, M., Mohammadnia, A., & Safarzadeh, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-24248-5 · Cited by 2 · see citing works
Nejadshamsi, S., Chu, C. H., McGilton, K. S., Li, X., Ronquillo, C., & Rahimi, S. A. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf171 · Cited by 2 · see citing works
Performance of Different Models of Machine Learning in Predicting the COVID-19 Pandemic
Wang, Q. (2020) · International Conference on Public Health and Data Science (ICPHDS) · doi:10.1109/icphds51617.2020.00050 · Cited by 2 · see citing works
Biomedical Data Driven COVID-19 Prediction Using Machine Learning Approach
Ghosal, A., Gupta, N., Nandi, E., & Somolu, H. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-04597-4_6 · Cited by 2 · see citing works
Machine Learning in Healthcare
Kushwaha, S., Srivastava, R., Vats, H., & Khanna, P. (2022) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-6684-4045-2.ch003 · Cited by 2 · see citing works
Coronavirus Disease 2019 (COVID-19): Prediction Strategy Using Sequential Deep Learning Model
Surja, A. S., Iqbal, M. S., & Faruk, M. O. (2023) · European Journal of Medical and Health Sciences · doi:10.24018/ejmed.2023.5.2.1645 · Cited by 2 · see citing works
Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability
Debnath, S., Koppel, R., Saadi, N., Potak, D., Weinberger, B., & Zanos, T. P. (2023) · Digital Health · doi:10.1177/20552076231187594 · Cited by 2 · see citing works
Self-citationPredicting cancer mortality using machine learning methods: a global vs. Iran analysis
SADEGHI, H., & Seif, F. (2025) · BMC Cancer · doi:10.1186/s12885-025-14796-4 · Cited by 2 · see citing works
Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning
Moustakidis, S., Kokkotis, C., Tsaopoulos, D. E., Sfikakis, P. P., Tsiodras, S., Sypsa, V., Zaoutis, T. E., & Paraskevis, D. N. (2022) · Viruses · doi:10.3390/v14030625 · Cited by 2 · see citing works
Fuzzy based classification and COVID-19 Patient’s Data analysis
Shinde-Pawar, M., Shah, A. S., Nayak, M., Jadhav, S. A., Jadhav, A., Sahasrabuddhe, D., Memon, S. A., & Gaikwad, A. S. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.03.054 · Cited by 2 · see citing works
Predicting Hospitals Hygiene Rate during COVID-19 Pandemic
Qahtani, A. M., Bader, M., Alhakami, H., Abuayeid, S., & Baz, A. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111294 · Cited by 2 · see citing works
Potential and role of artificial intelligence in current medical healthcare
Hung, C.-M., Shi, H., Lee, P., Chang, C., Rau, K., Lee, H.-M., Tseng, C.-H., Pei, S., Tsai, K.-J., & Chiu, C. (2022) · WArtificial Intelligence in Cancer · doi:10.35713/aic.v3.i1.1 · Cited by 2 · see citing works
Nanobioengineering: A promising approach for early detection of COVID-19
Gill, A. A. S., Nate, Z., Chauhan, R., Faya, M. A., Karpoormath, R. V., & Omolo, C. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90280-9.00004-3 · Cited by 2 · see citing works
Machine Learning-Based Predictors for ICU Admission of COVID-19 Patients
Alhawas, N., & Kartal, S. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06371-8_38 · Cited by 1 · see citing works
Ramírez, E. L., Soria-Cruz, J., ZUÑIGA, I. C., & López-Veyna, J. I. (2025) · COVID · doi:10.3390/covid5100174 · Cited by 1 · see citing works
Venugopal, J., Panneerselvam, S., Patan, A., & Theivendren, P. (2026) · Clinical Therapeutics · doi:10.1016/j.clinthera.2026.03.002 · Cited by 1 · see citing works
ARTIFICIAL INTELLIGENCE IN TACKLING CORONAVIRUS AND FUTURE PANDEMICS
Quazi, S., Singh, S. K., Saha, R. P., Das, A., & Singh, M. K. (2024) · Journal of Experimental Biology and Agricultural Sciences · doi:10.18006/2024.12(1).124.137 · Cited by 1 · see citing works
Challenges of Machine Learning in Healthcare Industry
Vasundhara, S. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr23706105157 · Cited by 1 · see citing works
Application of Machine Learning in Hospital Resource Allocation - A Survey
Kumar, A. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.33276 · Cited by 1 · see citing works
Ramesh, M. R., & Vijayakumaran, C. (2022) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc53470.2022.9753836 · Cited by 1 · see citing works
DEVELOPMENT OF FUZZY INFERENCE SYSTEM FOR COVID-19 DATA ANALYSIS
Shinde-Pawar, M., Patil, J., Shah, A. S., & Rasal, P. R. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.04.093 · Cited by 1 · see citing works
A contribution spanning three time periods offered by artificial intelligence and synthetic biology
Sven, D. A., Bellman, F., & Nurdiyanto, H. (2023) · International Journal of Artificial Intelligence Research · doi:10.29099/ijair.v7i1.348 · Cited by 1 · see citing works
Miranda, I. D. S., Cardoso, G. S., Pahar, M., Oliveira, G., & Niesler, T. (2021) · doi:10.36227/techrxiv.13736698.v1 · Cited by 1 · see citing works
Machine Learning Techniques for the Identification and Diagnosis of COVID-19
Gasmi, A. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-68936-0_12
ClusteringProposal Supportfor theCOVID-19 Making Decision Process in a Data Demanding Scenario
Canon, Á. D. O., & Perdómo, O. (2021)
Reinforcement Learning Model for Pandemic Precautions in Healthcare Environment
Sekaran, R., Raveendran, A. P., Munnangi, A. K., & Manikandan, R. (2022) · Disruptive technologies and digital transformations for society 5.0 · doi:10.1007/978-981-16-3783-4_8
Miranda, I. D. S., Cardoso, G. S., Pahar, M., de Oliveira, G. H. H., & Niesler, T. (2021) · doi:10.36227/techrxiv.13736698
Munnangi, A. K., Sekaran, R., Raveendran, A. P., & Manikandan, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-98167-9_8
COVID-19 PANDEMIC: PRE AND POST COVID RELATIVE ANALYSIS OF RURAL PATIENTS’ HEALTH
Shinde-Pawar, M., Kadam, K., Patil, B., & Desai, P. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.s06.199
Lyu, Y., Xu, Q., Zhen, C., & Liu, J. (2022) · doi:10.2196/preprints.45089
Surja, A. S., Limon, M. T. I., Soeb, M. J. A., Arifin, M. S., Islam, M. M., Kayshar, M. S., Islam, A., Rahman, M. M., Malek, M. A., Mohsin, F. M., et al. (2022) · Journal of Ideas in Health · doi:10.47108/jidhealth.vol5.iss4.259
Severity Detection of COVID-19 Patient’s Using Machine Learning Techniques
Uphade, D. B., & Muley, A. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-34644-6_6
Nahajowski, M., Kędziora, M., & Jóźwiak, I. J. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-42430-4_40
Issues in Healthcare and the Role of Machine Learning in Healthcare
Chauhan, N., Kaur, N., Saini, K. S., & Kaur, M. (2024) · doi:10.1002/9781394175376.ch2
Ulrichsen, F. C., Sena, A. C., Pôrto, L. C., & Figueiredo, K. (2024) · medRxiv · doi:10.1101/2024.02.12.24302722
Building a Smarter Government Using Machine Learning Applications: Benefits and Challenges
Manga, E., Karanikolas, N. Ν., & Marinagi, C. C. (2024) · Contributions to economics · doi:10.1007/978-3-031-66669-8_4
Khotimchenko, M., Bundey, Y., Bhave, R., Baran, S. W., Chakravarty, K., & Varshney, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5065235
Efficient Disease Diagnosis Platform: Multi-Model Solution for Early Health Condition Detection
Beniwal, R., Kalra, S., & Nisha, K. (2025) · doi:10.1109/icsc64553.2025.10967730
- Authors
- Debnath, S., Barnaby, D. P., Coppa, K., Makhnevich, A., Kim, E. J., Chatterjee, S., Tóth, V., Levy, T. J., Paradis, M. D., Cohen, S. L., Hirsch, J. S., Zanos, T. P., the Northwell COVID-19 Research Consortium, Becker, L. B., Cookingham, J., Davidson, K. W., Dominello, A. J., Falzon, L., McGinn, T., Mogavero, J. N., & Osorio, G. A.
- Affiliations
- Northwell Health (US)
- Corresponding author
- Zanos, Theodoros P.
- ORCID
- Venue
- Bioelectronic Medicine
- Published
- 2020-07-10
- Volume
- 6
- Issue
- 1
- Pages
- 14-14
- ISSN
- 2332-8886 (ISSN-L)
- PMC
- PMC7347420
- OpenAlex
- W3042081405
- Cited by
- 104
- References
- 57
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 12 · 2023: 17 · 2022: 29 · 2021: 27 · 2020: 6
- FWCI
- 7.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Bioelectronic Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (R24AG064191)
- U.S. National Library of Medicine (R01LM012836)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- COVID-19 pandemic, machine learning, clinical decision-making, clinical prediction model, model validation, hospital resource allocation, health care administrators, care pathways
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