Design of an artificial neural network to predict mortality among COVID-19 patients
Tài liệu tham khảo
Abdoli, 2021, Covid-19-associated opportunistic infections: a snapshot on the current reports, Clin Exp Med, 23, 1
Arvind, 2021, Development of a machine learning algorithm to predict intubation among hospitalized patients with covid-19, J Crit Care, 62, 25, 10.1016/j.jcrc.2020.10.033
Chaudhry, 2021, Short durations of corticosteroids for hospitalised covid-19 patients are associated with a high readmission rate, J Infect, 82, 276, 10.1016/j.jinf.2021.03.002
2020, A machine learning-based predictive model for 30-day hospital readmission prediction for copd patients, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 11
de Siqueira Santos, 2022, Machine learning and network medicine approaches for drug repositioning for covid-19, Patterns, 3, 100396, 10.1016/j.patter.2021.100396
Ganguli, 2021, Machine learning application in covid-19 drug development, 229
Lv, 2021, Application of artificial intelligence and machine learning for covid-19 drug discovery and vaccine design, Brief Bioinformatics BRIEF BIOINFORM, 22, 1
Yazdani, 2021, Covid-19 and information communication technology: a conceptual model, J Adv Pharm Educ Res, 11, 83
Chaurasia, 2020, Application of machine learning time series analysis for prediction covid-19 pandemic, Biomed Eng Res, 38, 35, 10.1007/s42600-020-00105-4
Ma, 2020, Development of a support vector machine learning and smart phone internet of things-based architecture for real-time sleep apnea diagnosis, BMC Med Inf Decis Making, 20, 1
Karthikeyan, 2021, Machine learning based clinical decision support system for early covid-19 mortality prediction, Publ Health Forum, 9, 626697
Kaskovich, 2019, Matching patients with chronic obstructive pulmonary disease (copd) to personalized care: a novel machine learning tool to predict cause of 90-day readmission, Am J Respir Crit Care Med, 199, A7119
2016, Predicting chronic kidney failure disease using data mining techniques
Amirhajlou, 2019, Application of data mining techniques for predicting residents' performance on pre-board examinations: a case study, J Educ Health Promot, 8, 108
Xia, 2020, Data mining-based analysis of Chinese medicinal herb formulae in chronic kidney disease treatment, Evid Based Complement Alternat Med, 2020, 1
Zhang, 2021, Knowledge entity extraction and text mining in the era of big data, Inf Manag J, 5, 309
Hung, 2020, A machine learning approach to predicting readmission or mortality in patients hospitalized for stroke or transient ischemic attack, Appl Sci, 10, 6337, 10.3390/app10186337
Streun, 2020, A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules - proof of concept study using an artificial neural network for sample classification, Drug Test Anal, 12, 836, 10.1002/dta.2775
Yang, 2018, Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride, Nanoscale, 10, 19092, 10.1039/C8NR05703F
Yoo, 2016, Simple scoring system and artificial neural network for knee osteoarthritis risk prediction: a cross-sectional study, PLoS One, 11, 10.1371/journal.pone.0148724
Mollalo, 2020, Artificial neural network modeling of novel coronavirus (covid-19) incidence rates across the continental United States, Int J Environ Res, 17, 4204
Loey, 2020, Within the lack of chest covid-19 x-ray dataset: a novel detection model based on gan and deep transfer learning, Symmetry, 12, 651, 10.3390/sym12040651
Rong, 2019, Computer vision detection of foreign objects in walnuts using deep learning, Comput Electron Agric, 162, 1001, 10.1016/j.compag.2019.05.019
Shaffiee Haghshenas, 2020, Prioritizing and analyzing the role of climate and urban parameters in the confirmed cases of covid-19 based on artificial intelligence applications, Int J Environ Res, 17, 3730
Anjum, 2017, Life style characteristics: risk factor for breast cancer, EJPMR, 4, 629
Soltani Firouz, 2021, Dielectric spectroscopy coupled with artificial neural network for classification and quantification of sesame oil adulteration, Inf Process Agric, 9, 233
Khaire, 2019, Stability of feature selection algorithm: a review, J King Saud Univ - Comput Inf Sci., 34, 1060
Ibrahim, 2019, Improved salp swarm algorithm based on particle swarm optimization for feature selection, J Ambient Intell Humaniz Comput J AMB INTEL HUM COMP, 10, 3155, 10.1007/s12652-018-1031-9
Seijo-Pardo, 2017, Ensemble feature selection: homogeneous and heterogeneous approaches, Knowl Base Syst, 118, 124, 10.1016/j.knosys.2016.11.017
Walczak, 2018, Artificial neural networks, 120
Li, 2017, Application of artificial neural networks for catalysis: a review, Catalyst, 7, 1, 10.3390/catal7100306
Stoffel, 2018, Artificial neural networks and intelligent finite elements in non-linear structural mechanics, Thin-Walled Struct, 131, 102, 10.1016/j.tws.2018.06.035
Damodharan, 2017, Controlling input device based on iris movement detection using artificial neural network, int j sci, 2, 634
Tuhta, 2020, Artificial neural network based system identification usage for steel sheds, Int J Eng Res, 7, 22
Zakaulla, 2020, Artificial neural network based prediction on tribological properties of polycarbonate composites reinforced with graphene and boron carbide particle, Mater Today Proc, 26, 296, 10.1016/j.matpr.2019.11.276
Wu, 2018, Development and application of artificial neural network, Wireless Pers Commun, 102, 1645, 10.1007/s11277-017-5224-x
Erzurum Cicek, 2021, Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting, Appl Soft Comput, 102, 107091, 10.1016/j.asoc.2021.107091
Yang, 2018, Artificial neural network (ann) based prediction and optimization of an organic rankine cycle (orc) for diesel engine waste heat recovery, Energy Convers Manag, 164, 15, 10.1016/j.enconman.2018.02.062
Muralitharan, 2018, Neural network based optimization approach for energy demand prediction in smart grid, Neurocomputing, 273, 199, 10.1016/j.neucom.2017.08.017
Tharsanee, 2021, Deep convolutional neural network–based image classification for covid-19 diagnosis, 117
Bairathi, 2019, Salp swarm algorithm (ssa) for training feed-forward neural networks, 521
Shahmoradi, 2019, Development of decision support system to predict neurofeedback response in adhd: an artificial neural network approach, Acta Inf Med, 27, 186, 10.5455/aim.2019.27.186-191
Manik, 2019, Classification of electrocardiogram signals using principal component analysis and levenberg marquardt backpropagation for detection ventricular tachyarrhythmia, Data Sci J, 2, 29
Aljouie, 2021, Early prediction of covid-19 ventilation requirement and mortality from routinely collected baseline chest radiographs, laboratory, and clinical data with machine learning, J Multidiscip Healthc, 14, 2017, 10.2147/JMDH.S322431
Assaf, 2020, Utilization of machine-learning models to accurately predict the risk for critical covid-19, Intern Emerg Med, 15, 1435, 10.1007/s11739-020-02475-0
Agieb, 2020, Machine learning models for the prediction the necessity of resorting to icu of covid-19 patients, Int J Adv Trends Comput Sci Eng, 9, 6980, 10.30534/ijatcse/2020/15952020
Ryan, 2020, Mortality prediction model for the triage of covid-19, pneumonia, and mechanically ventilated icu patients: a retrospective study, Ann Med Surg, 59, 207, 10.1016/j.amsu.2020.09.044
Zhao, 2020, Prediction model and risk scores of icu admission and mortality in covid-19, PLoS One, 15, 10.1371/journal.pone.0236618
Allenbach, 2020, Development of a multivariate prediction model of intensive care unit transfer or death: a French prospective cohort study of hospitalized covid-19 patients, PLoS One, 15, 10.1371/journal.pone.0240711
Pan, 2020, Prognostic assessment of covid-19 in the intensive care unit by machine learning methods: model development and validation, J Med Internet Res, 22, 10.2196/23128
Gao, 2020, Machine learning based early warning system enables accurate mortality risk prediction for covid-19, Nat Commun, 11, 1, 10.1038/s41467-020-18684-2
Hernandez-Suarez, 2021, Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States, Cardiovasc Revascularization Med, 22, 22, 10.1016/j.carrev.2020.06.017
Parchure, 2020, Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with covid-19, BMJ Support Palliat Care, 1
Vaid, 2021, Federated learning of electronic health records to improve mortality prediction in hospitalized patients with covid-19: machine learning approach, JMIR Med Inform, 9, 10.2196/24207
Yadaw, 2020, Clinical features of covid-19 mortality: development and validation of a clinical prediction model, The Lancet Digital Health, 2, e516, 10.1016/S2589-7500(20)30217-X
Yan, 2020, An interpretable mortality prediction model for covid-19 patients, Nat Mach Intell, 2, 283, 10.1038/s42256-020-0180-7
Dhamodharavadhani, 2020, Covid-19 mortality rate prediction for India using statistical neural network models, Publ Health Forum, 8, 441
Gao, 2020, Machine learning based early warning system enables accurate mortality risk prediction for covid-19, Nat Commun, 11, 1, 10.1038/s41467-020-18684-2
Tortajada-Goitia, 2020, Survey on the situation of telepharmacy as applied to the outpatient care in hospital pharmacy departments in Spain during the covid-19 pandemic, Farm Hosp, 44, 135
Guo, 2021, Prediction of the confirmed cases and deaths of global covid-19 using artificial intelligence, Environ Sci Pollut Res, 28, 11672, 10.1007/s11356-020-11930-6
Gupta, 2021, Prediction of covid-19 confirmed, death, and cured cases in India using random forest model, Stat Anal Data Min, 4, 116, 10.26599/BDMA.2020.9020016
2020, Gpr and ann based prediction models for covid-19 death cases, 3
Mónica, 2021
Rasjid, 2021, A comparison: prediction of death and infected covid-19 cases in Indonesia using time series smoothing and lstm neural network, Procedia Comput Sci, 179, 982, 10.1016/j.procs.2021.01.102
Alle, 2022, Covid-19 risk stratification and mortality prediction in hospitalized indian patients: harnessing clinical data for public health benefits, PLoS One, 17, 10.1371/journal.pone.0264785
Du, 2021, The amputation and mortality of inpatients with diabetic foot ulceration in the covid-19 pandemic and postpandemic era: a machine learning study, Int Wound J, 1
Khan, 2021, Computational intelligence-based model for mortality rate prediction in covid-19 patients, Int J Environ Res Publ Health, 18, 6429, 10.3390/ijerph18126429
Ma, 2020, Development and validation of prognosis model of mortality risk in patients with covid-19, Epidemiol Infect, 148, e168, 10.1017/S0950268820001727
Snider, 2021, Identification of variable importance for predictions of mortality from covid-19 using ai models for ontario, Canada, Publ Health Forum, 9, 675766
Ma, 2020, Characteristic of 523 covid-19 in henan province and a death prediction model, Publ Health Forum, 8, 1
Asseri, 2020, Implementation and evaluation of telepharmacy during covid-19 pandemic in an academic medical city in the kingdom of Saudi Arabia: paving the way for telepharmacy, Na J Adv Res, 7, 218
Zakiyyah, 2020, Prediction of Covid-19 Infection in Indonesia Using Machine Learning Methods
Vaid, 2020
Zhao, 2020, Prediction model and risk scores of icu admission and mortality in covid-19, PLoS One, 15, 10.1371/journal.pone.0236618
Asteris, 2022, Genetic prediction of icu hospitalization and mortality in covid‐19 patients using artificial neural networks, Na J Adv Res, 26, 1445
Lin, 2021, An artificial neural network model to predict the mortality of covid-19 patients using routine blood samples at the time of hospital admission: development and validation study, Medicine, 100, 10.1097/MD.0000000000026532
Adib QAR, Tasmi ST, Bhuiyan M, Islam S, Raihan M, Sarker M, et al. Prediction model for mortality analysis of pregnant women affected with covid-19. arXiv preprint arXiv:211111477. 2021.
Naseem, 2021, Predicting mortality in sars-cov-2 (covid-19) positive patients in the inpatient setting using a novel deep neural network, Int J Med Inf, 154, 104556, 10.1016/j.ijmedinf.2021.104556
Li, 2020, Deep learning prediction of likelihood of icu admission and mortality in covid-19 patients using clinical variables, PeerJ, 8, 10.7717/peerj.10337
Ko, 2020, An artificial intelligence model to predict the mortality of covid-19 patients at hospital admission time using routine blood samples: development and validation of an ensemble model, J Med Internet Res, 22, 10.2196/25442
Alsuwaiket, 2020, Predicting the covid-19 spread, recoveries and mortalities rates in Saudi Arabia using ann, J Theor Appl Inf Technol, 98, 3643
Sankaranarayanan, 2021, Covid-19 mortality prediction from deep learning in a large multistate ehr and lis dataset: algorithm development and validation, J Med Internet Res, 23, 10.2196/30157
Schiaffino, 2021, Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features, J Personalized Med, 11, 501, 10.3390/jpm11060501
2020, Machine learning to predict icu admission, icu mortality and survivors' length of stay among covid-19 patients: toward optimal allocation of icu resources
Sampedro-Gómez, 2021, Prediction of in-hospital mortality and 30-day readmission in heart failure using machine learning, REC (Rev Esp Cardiol): CardioClinics., 28, 710
Shin, 2021, Machine learning vs. Conventional statistical models for predicting heart failure readmission and mortality, ESC Heart Failure, 8, 106, 10.1002/ehf2.13073
Mirsoleymani, 2021, Predictors of mortality among covid-19 patients with or without comorbid diabetes mellitus, Acta Med Iran, 59, 393