| 1 |
Kwok SWH, Wang G, Sohel F, Kashani KB, Zhu Y, Wang Z, Antpack E, Khandelwal K, Pagali SR, Nanda S, Abdalrhim AD, Sharma UM, Bhagra S, Dugani S, Takahashi PY, Murad MH, Yousufuddin M. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems. Respir Res 2023;24:79. [PMID: 36915107 DOI: 10.1186/s12931-023-02386-6] [Reference Citation Analysis]
|
| 2 |
Ershadi MM, Rise ZR. Fusing clinical and image data for detecting the severity level of hospitalized symptomatic COVID-19 patients using hierarchical model. Res Biomed Eng 2023. [DOI: 10.1007/s42600-023-00268-w] [Reference Citation Analysis]
|
| 3 |
Cowley HP, Robinette MS, Matelsky JK, Xenes D, Kashyap A, Ibrahim NF, Robinson ML, Zeger S, Garibaldi BT, Gray-Roncal W. Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients. Sci Rep 2023;13:2236. [PMID: 36755135 DOI: 10.1038/s41598-022-26294-9] [Reference Citation Analysis]
|
| 4 |
Jeyananthan P. Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients. Pathol Res Pract 2023;242:154311. [PMID: 36657221 DOI: 10.1016/j.prp.2023.154311] [Reference Citation Analysis]
|
| 5 |
Tiwari S, Chanak P, Singh SK. A Review of the Machine Learning Algorithms for Covid-19 Case Analysis. IEEE Trans Artif Intell 2023;4:44-59. [PMID: 36908643 DOI: 10.1109/TAI.2022.3142241] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
|
| 6 |
Kwekha-Rashid AS, Abduljabbar HN, Alhayani B. Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Appl Nanosci 2023;13:2013-25. [PMID: 34036034 DOI: 10.1007/s13204-021-01868-7] [Cited by in Crossref: 195] [Cited by in F6Publishing: 194] [Article Influence: 195.0] [Reference Citation Analysis]
|
| 7 |
Kazemi-arpanahi H, Nopour R, Shanbezadeh M. Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks. J Edu Health Promot 2023;12:16. [DOI: 10.4103/jehp.jehp_20_22] [Reference Citation Analysis]
|
| 8 |
Kamalov F, Cherukuri AK, Sulieman H, Thabtah F, Hossain A. Machine learning applications for COVID-19: a state-of-the-art review. Data Science for Genomics 2023. [DOI: 10.1016/b978-0-323-98352-5.00010-0] [Reference Citation Analysis]
|
| 9 |
Afrash MR, Shanbehzadeh M, Kazemi-Arpanahi H. Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms. J Biomed Phys Eng 2022;12:611-26. [PMID: 36569564 DOI: 10.31661/jbpe.v0i0.2105-1334] [Reference Citation Analysis]
|
| 10 |
Abdeltawab H, Khalifa F, Elnakieb Y, Elnakib A, Taher F, Alghamdi NS, Sandhu HS, El-baz A. Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning. Bioengineering 2022;9:536. [DOI: 10.3390/bioengineering9100536] [Reference Citation Analysis]
|
| 11 |
Uche-Anya E, Anyane-Yeboa A, Berzin TM, Ghassemi M, May FP. Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity. Gut 2022;71:1909-15. [PMID: 35688612 DOI: 10.1136/gutjnl-2021-326271] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
|
| 12 |
Nopour R, Shanbehzadeh M, Kazemi-Arpanahi H. Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study. Med J Islam Repub Iran 2022;36:30. [PMID: 35999913 DOI: 10.47176/mjiri.36.30] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 13 |
Bárcenas R, Fuentes-García R. Risk assessment in COVID-19 patients: A multiclass classification approach. Inform Med Unlocked 2022;32:101023. [PMID: 35873009 DOI: 10.1016/j.imu.2022.101023] [Reference Citation Analysis]
|
| 14 |
Mathew JK, Lakshmi SS. A Study On Diagnosis Of Diabetes Mellitus Based On Tongue Images With Various Methods. 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) 2022. [DOI: 10.1109/ic3sis54991.2022.9885616] [Reference Citation Analysis]
|
| 15 |
Bendavid I, Statlender L, Shvartser L, Teppler S, Azullay R, Sapir R, Singer P. A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19. Sci Rep 2022;12:10573. [PMID: 35732690 DOI: 10.1038/s41598-022-14758-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 16 |
Comito C, Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artif Intell Med 2022;128:102286. [PMID: 35534142 DOI: 10.1016/j.artmed.2022.102286] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 10.0] [Reference Citation Analysis]
|
| 17 |
Zhang K, Karanth S, Patel B, Murphy R, Jiang X. A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients. J Biomed Inform 2022;130:104079. [PMID: 35489596 DOI: 10.1016/j.jbi.2022.104079] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 18 |
Fahad SD, Gharghan SK, Hussein RH. DIAGNOSIS OF COVID-19 BASED ON ARTIFICIAL INTELLIGENCE MODELS AND PHYSIOLOGICAL SENSORS: REVIEW. Biomed Eng Appl Basis Commun 2022;34. [DOI: 10.4015/s1016237222500065] [Reference Citation Analysis]
|
| 19 |
Rahmani K, Garikipati A, Barnes G, Hoffman J, Calvert J, Mao Q, Das R. Early prediction of central line associated bloodstream infection using machine learning. Am J Infect Control 2022;50:440-5. [PMID: 34428529 DOI: 10.1016/j.ajic.2021.08.017] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 20 |
Guadiana-alvarez JL, Hussain F, Morales-menendez R, Rojas-flores E, García-zendejas A, Escobar CA, Ramírez-mendoza RA, Wang J. Prognosis patients with COVID-19 using deep learning. BMC Med Inform Decis Mak 2022;22. [DOI: 10.1186/s12911-022-01820-x] [Reference Citation Analysis]
|
| 21 |
Swain S, Bhushan B, Dhiman G, Viriyasitavat W. Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey. Arch Comput Methods Eng 2022;:1-23. [PMID: 35342282 DOI: 10.1007/s11831-022-09733-8] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
|
| 22 |
Cho B, Geng E, Arvind V, Valliani AA, Tang JE, Schwartz J, Dominy C, Cho SK, Kim JS. Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques. JBJS Rev 2022;10. [PMID: 35302963 DOI: 10.2106/JBJS.RVW.21.00142] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 23 |
Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1] [Reference Citation Analysis]
|
| 24 |
Cho NJ, Park S, Lyu J, Lee H, Hong M, Lee EY, Gil HW. Prediction Model of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning by Intentional Ingestion: Prediction of Respiratory Failure in Pesticide Intoxication (PREP) Scores in Cohort Study. J Clin Med 2022;11:1048. [PMID: 35207319 DOI: 10.3390/jcm11041048] [Reference Citation Analysis]
|
| 25 |
Saadatmand S, Salimifard K, Mohammadi R, Marzban M, Naghibzadeh-Tahami A. Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning. Med Biol Eng Comput 2022. [PMID: 35147843 DOI: 10.1007/s11517-022-02519-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 26 |
Modelli de Andrade LG, de Sandes-Freitas TV, Requião-Moura LR, Viana LA, Cristelli MP, Garcia VD, Alcântara ALC, Esmeraldo RM, Abbud Filho M, Pacheco-Silva A, de Lima Carneiro ECR, Manfro RC, Costa KMAH, Simão DR, de Sousa MV, Santana VBBM, Noronha IL, Romão EA, Zanocco JA, Arimatea GGQ, De Boni Monteiro de Carvalho D, Tedesco-Silva H, Medina-Pestana J; COVID-19-KT Brazil. Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients. Am J Transplant 2022;22:610-25. [PMID: 34416075 DOI: 10.1111/ajt.16807] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 9.0] [Reference Citation Analysis]
|
| 27 |
Maharjan J, Ektefaie Y, Ryan L, Mataraso S, Barnes G, Shokouhi S, Green-saxena A, Calvert J, Mao Q, Das R. Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm. Front Neurol 2022;12:784250. [DOI: 10.3389/fneur.2021.784250] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 28 |
Varzaneh ZA, Orooji A, Erfannia L, Shanbehzadeh M. A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. Inform Med Unlocked 2022;28:100825. [PMID: 34977330 DOI: 10.1016/j.imu.2021.100825] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 29 |
Kanwal S, Khan F, Alamri S, Dashtipur K, Gogate M. COVID‐opt‐aiNet : A clinical decision support system for COVID ‐19 detection. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22695] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 30 |
Wu D, Ren Y, He L, Johnson J. The identification of risk factors associated with COVID-19 in a large inpatient cohort using machine learning approaches. Digital Innovation for Healthcare in COVID-19 Pandemic: Strategies and Solutions 2022. [DOI: 10.1016/b978-0-12-821318-6.00017-7] [Reference Citation Analysis]
|
| 31 |
Raol P, Vala B, Pandya NK. A Survey for Determining Patterns in the Severity of COVID Patients Using Machine Learning Algorithm. Expert Clouds and Applications 2022. [DOI: 10.1007/978-981-19-2500-9_28] [Reference Citation Analysis]
|
| 32 |
Rahman T, Yesmin MN. Utilization of Machine Learning Techniques for Prediction of COVID-19 Epidemic. Proceedings of Third International Conference on Communication, Computing and Electronics Systems 2022. [DOI: 10.1007/978-981-16-8862-1_48] [Reference Citation Analysis]
|
| 33 |
Masoudi-Sobhanzadeh Y, Esmaeili H, Masoudi-Nejad A. A fuzzy logic-based computational method for the repurposing of drugs against COVID-19. Bioimpacts 2022;12:315-24. [PMID: 35975205 DOI: 10.34172/bi.2021.40] [Reference Citation Analysis]
|
| 34 |
Thaljaoui A, Khediri SE, Benmohamed E, Alabdulatif A, Alourani A. Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients. Med Biol Eng Comput 2022;60:3475-96. [PMID: 36205834 DOI: 10.1007/s11517-022-02677-y] [Reference Citation Analysis]
|
| 35 |
Almars AM, Gad I, Atlam E. Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life. Studies in Computational Intelligence 2022. [DOI: 10.1007/978-3-030-91103-4_7] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 36 |
Danker C, Murzabekov M, Forsberg D, Lidströmer N, Honoré A, Rautiainen S, Herlenius E. AI and Dynamic Prediction of Deterioration in Covid-19. Artificial Intelligence in Covid-19 2022. [DOI: 10.1007/978-3-031-08506-2_12] [Reference Citation Analysis]
|
| 37 |
Shickel B, Balch J, Aggas JR, Loftus TJ, Kotanen CN, Rashidi P, Guiseppi-elie A. Scoring for Hemorrhage Severity in Traumatic Injury. Biomarkers in Trauma, Injury and Critical Care 2022. [DOI: 10.1007/978-3-030-87302-8_58-1] [Reference Citation Analysis]
|
| 38 |
Boussen S, Cordier PY, Malet A, Simeone P, Cataldi S, Vaisse C, Roche X, Castelli A, Assal M, Pepin G, Cot K, Denis JB, Morales T, Velly L, Bruder N; GRAM+(Groupe de Recherche en Réanimation et Anesthésie de Marseille Pluridisciplinaire). Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning. Comput Biol Med 2021;142:105192. [PMID: 34998220 DOI: 10.1016/j.compbiomed.2021.105192] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 39 |
Imbalzano E, Orlando L, Sciacqua A, Nato G, Dentali F, Nassisi V, Russo V, Camporese G, Bagnato G, Cicero AFG, Dattilo G, Vatrano M, Versace AG, Squadrito G, Di Micco P. Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer. J Clin Med 2021;11:219. [PMID: 35011959 DOI: 10.3390/jcm11010219] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 40 |
Ponce D, de Andrade LGM, Granado RC, Ferreiro-Fuentes A, Lombardi R. Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach. Sci Rep 2021;11:24439. [PMID: 34952908 DOI: 10.1038/s41598-021-03894-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 41 |
Singh V, Kamaleswaran R, Chalfin D, Buño-Soto A, San Roman J, Rojas-Kenney E, Molinaro R, von Sengbusch S, Hodjat P, Comaniciu D, Kamen A. A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers. iScience 2021;24:103523. [PMID: 34870131 DOI: 10.1016/j.isci.2021.103523] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 42 |
Maves RC, Richard SA, Lindholm DA, Epsi N, Larson DT, Conlon C, Everson K, Lis S, Blair PW, Chi S, Ganesan A, Pollett S, Burgess TH, Agan BK, Colombo RE, Colombo CJ; EPICC COVID-19 Cohort Study Group . Predictive Value of an Age-Based Modification of the National Early Warning System in Hospitalized Patients With COVID-19. Open Forum Infect Dis 2021;8:ofab421. [PMID: 34877361 DOI: 10.1093/ofid/ofab421] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 43 |
Yijing L, Wenyu Y, Kang Y, Shengyu Z, Xianliang H, Xingliang J, Cheng W, Zehui S, Mengxing L. Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring. Comput Methods Programs Biomed 2021;214:106568. [PMID: 34883382 DOI: 10.1016/j.cmpb.2021.106568] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 44 |
Richards DM, Tweardy MJ, Steinhubl SR, Chestek DW, Hoek TLV, Larimer KA, Wegerich SW. Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients. NPJ Digit Med 2021;4:155. [PMID: 34750499 DOI: 10.1038/s41746-021-00527-z] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
|
| 45 |
Kundu R, Singh PK, Mirjalili S, Sarkar R. COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble. Comput Biol Med 2021;138:104895. [PMID: 34649147 DOI: 10.1016/j.compbiomed.2021.104895] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 7.5] [Reference Citation Analysis]
|
| 46 |
Kocadagli O, Baygul A, Gokmen N, Incir S, Aktan C. Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach. Curr Res Transl Med 2021;70:103319. [PMID: 34768217 DOI: 10.1016/j.retram.2021.103319] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 47 |
Aminu M, Ahmad NA, Mohd Noor MH. Covid-19 detection via deep neural network and occlusion sensitivity maps. Alexandria Engineering Journal 2021;60:4829-55. [DOI: 10.1016/j.aej.2021.03.052] [Cited by in Crossref: 15] [Cited by in F6Publishing: 6] [Article Influence: 7.5] [Reference Citation Analysis]
|
| 48 |
Chang W, Ji X, Wang L, Liu H, Zhang Y, Chen B, Zhou S. A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining. Healthcare (Basel) 2021;9:1306. [PMID: 34682985 DOI: 10.3390/healthcare9101306] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
|
| 49 |
Verspoor K. The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System. Yearb Med Inform 2021;30:176-84. [PMID: 34479389 DOI: 10.1055/s-0041-1726503] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 50 |
Meng Z, Guo S, Zhou Y, Li M, Wang M, Ying B. Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19. Signal Transduct Target Ther 2021;6:316. [PMID: 34433805 DOI: 10.1038/s41392-021-00731-z] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 51 |
Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021;10:1048. [PMID: 34451513 DOI: 10.3390/pathogens10081048] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
|
| 52 |
Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021;10:1048. [PMID: 34451513 DOI: 10.3390/pathogens10081048] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
|
| 53 |
Alyasseri ZAA, Al-Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Syst 2021;:e12759. [PMID: 34511689 DOI: 10.1111/exsy.12759] [Cited by in Crossref: 36] [Cited by in F6Publishing: 42] [Article Influence: 18.0] [Reference Citation Analysis]
|
| 54 |
Colombo CJ, Colombo RE, Maves RC, Branche AR, Cohen SH, Elie MC, George SL, Jang HJ, Kalil AC, Lindholm DA, Mularski RA, Ortiz JR, Tapson V, Liang CJ. Performance Analysis of the National Early Warning Score and Modified Early Warning Score in the Adaptive COVID-19 Treatment Trial Cohort. Crit Care Explor 2021;3:e0474. [PMID: 34278310 DOI: 10.1097/CCE.0000000000000474] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 55 |
Jia G, Lam HK, Xu Y. Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method. Comput Biol Med 2021;134:104425. [PMID: 33971427 DOI: 10.1016/j.compbiomed.2021.104425] [Cited by in Crossref: 30] [Cited by in F6Publishing: 32] [Article Influence: 15.0] [Reference Citation Analysis]
|
| 56 |
Parbate ND, Palande AP, Wagh SK, Kamble A, Isave S. ICU Admission Prediction Using Machine Learning for Covid-19 Patients. 2021 International Conference on Communication information and Computing Technology (ICCICT) 2021. [DOI: 10.1109/iccict50803.2021.9510117] [Reference Citation Analysis]
|
| 57 |
Cavallaro M, Moiz H, Keeling MJ, McCarthy ND. Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values. PLoS Comput Biol 2021;17:e1009121. [PMID: 34161326 DOI: 10.1371/journal.pcbi.1009121] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 58 |
Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. J Am Med Inform Assoc 2021;28:2050-67. [PMID: 34151987 DOI: 10.1093/jamia/ocab098] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
|
| 59 |
Colpo MP, Alves BC, Pereira KS, Brandão AFZ, de Aguiar MS, Primo TT. Attribute selection based on genetic and classification algorithms in the prediction of hospitalization need of COVID-19 patients. XVII Brazilian Symposium on Information Systems 2021. [DOI: 10.1145/3466933.3466935] [Reference Citation Analysis]
|
| 60 |
Saria S, Schulam P, Yeh BJ, Burke D, Mooney SD, Fong CT, Sunshine JE, Long DR, O'Reilly-Shah VN. Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients. Crit Care Explor 2021;3:e0441. [PMID: 34104894 DOI: 10.1097/CCE.0000000000000441] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 61 |
Carmichael H, Coquet J, Sun R, Sang S, Groat D, Asch SM, Bledsoe J, Peltan ID, Jacobs JR, Hernandez-Boussard T. Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study. J Biomed Inform 2021;119:103802. [PMID: 33965640 DOI: 10.1016/j.jbi.2021.103802] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
|
| 62 |
Subudhi S, Verma A, Patel AB, Hardin CC, Khandekar MJ, Lee H, McEvoy D, Stylianopoulos T, Munn LL, Dutta S, Jain RK. Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19. NPJ Digit Med 2021;4:87. [PMID: 34021235 DOI: 10.1038/s41746-021-00456-x] [Cited by in Crossref: 40] [Cited by in F6Publishing: 42] [Article Influence: 20.0] [Reference Citation Analysis]
|
| 63 |
Haj Ismail A, Dawi E, Jwaid T, Mahmoud ST, Abdelkader A. Simulation of the evolution of the Covid-19 pandemic in the United Arab Emirates using the sir epidemical model. Arab Journal of Basic and Applied Sciences 2021;28:128-34. [DOI: 10.1080/25765299.2021.1890900] [Reference Citation Analysis]
|
| 64 |
Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021;19:2833-50. [PMID: 34025952 DOI: 10.1016/j.csbj.2021.05.010] [Cited by in Crossref: 16] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
|
| 65 |
Shiri I, Sorouri M, Geramifar P, Nazari M, Abdollahi M, Salimi Y, Khosravi B, Askari D, Aghaghazvini L, Hajianfar G, Kasaeian A, Abdollahi H, Arabi H, Rahmim A, Radmard AR, Zaidi H. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput Biol Med 2021;132:104304. [PMID: 33691201 DOI: 10.1016/j.compbiomed.2021.104304] [Cited by in Crossref: 69] [Cited by in F6Publishing: 67] [Article Influence: 34.5] [Reference Citation Analysis]
|
| 66 |
Mauer E, Lee J, Choi J, Zhang H, Hoffman KL, Easthausen IJ, Rajan M, Weiner MG, Kaushal R, Safford MM, Steel PAD, Banerjee S. A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories. J Biomed Inform 2021;118:103794. [PMID: 33933654 DOI: 10.1016/j.jbi.2021.103794] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 67 |
Chee ML, Ong MEH, Siddiqui FJ, Zhang Z, Lim SL, Ho AFW, Liu N. Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review. Int J Environ Res Public Health 2021;18. [PMID: 33947006 DOI: 10.3390/ijerph18094749] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
|
| 68 |
Patrício A, Costa RS, Henriques R. Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study. J Med Internet Res 2021;23:e26075. [PMID: 33835931 DOI: 10.2196/26075] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 69 |
Schreyer KE, Isenberg DL, Satz WA, Lucas NV, Rosenbaum J, Zandrow G, Gentile NT. Rate of Decompensation of Normoxic Emergency Department Patients with SARS-CoV-2. West J Emerg Med 2021;22:580-6. [PMID: 34125030 DOI: 10.5811/westjem.2020.12.49206] [Reference Citation Analysis]
|
| 70 |
Chee ML, Ong MEH, Siddiqui FJ, Zhang Z, Lim SL, Ho AFW, Liu N. Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.. [DOI: 10.1101/2021.02.15.21251727] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 71 |
Turki T, Taguchi YH. Discriminating the single-cell gene regulatory networks of human pancreatic islets: A novel deep learning application. Comput Biol Med 2021;132:104257. [PMID: 33740535 DOI: 10.1016/j.compbiomed.2021.104257] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
|
| 72 |
Jimenez-Solem E, Petersen TS, Hansen C, Hansen C, Lioma C, Igel C, Boomsma W, Krause O, Lorenzen S, Selvan R, Petersen J, Nyeland ME, Ankarfeldt MZ, Virenfeldt GM, Winther-Jensen M, Linneberg A, Ghazi MM, Detlefsen N, Lauritzen AD, Smith AG, de Bruijne M, Ibragimov B, Petersen J, Lillholm M, Middleton J, Mogensen SH, Thorsen-Meyer HC, Perner A, Helleberg M, Kaas-Hansen BS, Bonde M, Bonde A, Pai A, Nielsen M, Sillesen M. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Sci Rep 2021;11:3246. [PMID: 33547335 DOI: 10.1038/s41598-021-81844-x] [Cited by in Crossref: 34] [Cited by in F6Publishing: 39] [Article Influence: 17.0] [Reference Citation Analysis]
|
| 73 |
Gong K, Wu D, Arru CD, Homayounieh F, Neumark N, Guan J, Buch V, Kim K, Bizzo BC, Ren H, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Carriero A, Saba L, Masjedi M, Talari H, Babaei R, Mobin HK, Ebrahimian S, Guo N, Digumarthy SR, Dayan I, Kalra MK, Li Q. A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records. Eur J Radiol 2021;139:109583. [PMID: 33846041 DOI: 10.1016/j.ejrad.2021.109583] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
|
| 74 |
Ponce D, Andrade LGM, Granado RC, Ferrero A, Lombardi R, Group LAIAC. Development of a Prediction Score for In-Hospital Mortality in COVID-19 Patients with Acute Kidney Injury: A Machine Learning Approach. SSRN Journal. [DOI: 10.2139/ssrn.3800070] [Reference Citation Analysis]
|
| 75 |
Pati A, Parhi M, Pattanayak BK. COVID-19 Pandemic Analysis and Prediction Using Machine Learning Approaches in India. Lecture Notes in Networks and Systems 2021. [DOI: 10.1007/978-981-16-0695-3_30] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 76 |
Nayak J, Naik B, Dinesh P, Vakula K, Rao BK, Ding W, Pelusi D. Intelligent system for COVID-19 prognosis: a state-of-the-art survey. Appl Intell (Dordr) 2021;51:2908-38. [PMID: 34764577 DOI: 10.1007/s10489-020-02102-7] [Cited by in Crossref: 26] [Cited by in F6Publishing: 26] [Article Influence: 13.0] [Reference Citation Analysis]
|
| 77 |
Li Y, Shang K, Bian W, He L, Fan Y, Ren T, Zhang J. Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification. Sci Rep 2020;10:22083. [PMID: 33328512 DOI: 10.1038/s41598-020-79097-1] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 3.3] [Reference Citation Analysis]
|
| 78 |
Cavallaro M, Moiz H, Keeling MJ, Mccarthy ND. Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values.. [DOI: 10.1101/2020.12.03.20242941] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
| 79 |
Patrício A, Costa RS, Henriques R. Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study (Preprint).. [DOI: 10.2196/preprints.26075] [Reference Citation Analysis]
|
| 80 |
Tsvetkov VV, Tokin II, Lioznov DA, Venev EV, Kulikov AN. Predicting the duration of inpatient treatment for COVID-19 patients. Medicinskij sovet 2020. [DOI: 10.21518/2079-701x-2020-17-82-90] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
