{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:12:38Z","timestamp":1776082358560,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2016\/14172-6"],"award-info":[{"award-number":["2016\/14172-6"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09312-3","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T20:01:34Z","timestamp":1702411294000},"page":"4387-4399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Combining artificial neural networks and hematological data to diagnose Covid-19 infection in Brazilian population"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3452-7033","authenticated-orcid":false,"given":"Tiago D.","family":"Martins","sequence":"first","affiliation":[]},{"given":"Sandra D.","family":"Martins","sequence":"additional","affiliation":[]},{"given":"Silmara","family":"Montalv\u00e3o","sequence":"additional","affiliation":[]},{"given":"Mohamad","family":"Al Bannoud","sequence":"additional","affiliation":[]},{"given":"Gabriel Y.","family":"Ottaiano","sequence":"additional","affiliation":[]},{"given":"Let\u00edcia Q.","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Stephany C.","family":"Huber","sequence":"additional","affiliation":[]},{"given":"Tassiana S. P.","family":"Diaz","sequence":"additional","affiliation":[]},{"given":"Carolina","family":"Wroclawski","sequence":"additional","affiliation":[]},{"given":"Cyrillo Cavalheiro","family":"Filho","sequence":"additional","affiliation":[]},{"given":"Rubens","family":"Maciel-Filho","sequence":"additional","affiliation":[]},{"given":"Joyce M.","family":"Annichino-Bizzacchi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"9312_CR1","unstructured":"OWID, Our World In Data. (2022) https:\/\/ourworldindata.org\/. Accessed: Sept 7th, 2022"},{"key":"9312_CR2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1515\/cclm-2020-1294","volume":"59","author":"F Cabitza","year":"2021","unstructured":"Cabitza F, Campagner A, Ferrari D, Resta CD, Ceriotti D, Sabetta E, Colombini A, Vecchi ED, Banfi G, Locatelli M, Carobene A (2021) Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests. Clin Chem Lab Med (CCLM) 59:421\u2013431","journal-title":"Clin Chem Lab Med (CCLM)"},{"key":"9312_CR3","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10916-020-01597-4","volume":"44","author":"D Brinati","year":"2020","unstructured":"Brinati D, Campagner A, Ferrari D, Locatelli M, Banfi G, Cabitza F (2020) Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study. J Med Syst 44:135","journal-title":"J Med Syst"},{"key":"9312_CR4","first-page":"E215","volume":"95","author":"BE Fan","year":"2020","unstructured":"Fan BE (2020) Hematologic parameters in patients with COVID-19 infection: a reply. Am J Hematol 95:E215\u2013E215","journal-title":"Am J Hematol"},{"key":"9312_CR5","doi-asserted-by":"crossref","first-page":"e114","DOI":"10.7861\/clinmed.2020-0373","volume":"20","author":"V Formica","year":"2020","unstructured":"Formica V, Minieri M, Bernardini S, Ciotti M, D\u2019Agostini C, Roselli M, Andreoni M, Morelli C, Parisi G, Federici M, Paganelli C, Legramante JM (2020) Complete blood count might help to identify subjects with high probability of testing positive to SARS-CoV-2. Clin Med 20:e114\u2013e119","journal-title":"Clin Med"},{"key":"9312_CR6","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.neucom.2016.08.039","volume":"216","author":"D Lin","year":"2016","unstructured":"Lin D, Vasilakos AV, Tang Y, Yao Y (2016) Neural networks for computer-aided diagnosis in medicine: a review. Neurocomputing 216:700\u2013708","journal-title":"Neurocomputing"},{"key":"9312_CR7","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.3390\/cancers11091235","volume":"11","author":"K Munir","year":"2019","unstructured":"Munir K, Elahi H, Ayub A, Frezza F, Rizzi A (2019) Cancer diagnosis using deep learning: a bibliographic review. Cancers 11:1235","journal-title":"Cancers"},{"key":"9312_CR8","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3390\/mti2030047","volume":"2","author":"M Bakator","year":"2018","unstructured":"Bakator M, Radosav D (2018) Deep learning and medical diagnosis: a review of literature. Multimod Technol Interact 2:47","journal-title":"Multimod Technol Interact"},{"key":"9312_CR9","doi-asserted-by":"crossref","first-page":"104221","DOI":"10.1016\/j.ijmedinf.2020.104221","volume":"141","author":"TD Martins","year":"2020","unstructured":"Martins TD, Annichino-Bizzacchi JM, Romano AVC, Maciel Filho R (2020) Artificial neural networks for prediction of recurrent venous thromboembolism. Int J Med Inform 141:104221","journal-title":"Int J Med Inform"},{"key":"9312_CR10","volume":"39","author":"ZAA Alyasseri","year":"2022","unstructured":"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 (2022) Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Syst 39:e12759","journal-title":"Expert Syst"},{"key":"9312_CR11","doi-asserted-by":"crossref","first-page":"105244","DOI":"10.1016\/j.compbiomed.2022.105244","volume":"142","author":"MF Aslan","year":"2022","unstructured":"Aslan MF, Sabanci K, Durdu A, Unlersen MF (2022) COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization. Comput Biol Med 142:105244","journal-title":"Comput Biol Med"},{"key":"9312_CR12","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1007\/s11277-022-09463-x","volume":"124","author":"JT Thirukrishna","year":"2022","unstructured":"Thirukrishna JT, Krishna SRS, Shashank P, Srikanth S, Raghu V (2022) Survey on diagnosing CORONA VIRUS from radiography Chest X-ray images using convolutional neural networks. Wirel Pers Commun 124:2261\u20132270","journal-title":"Wirel Pers Commun"},{"key":"9312_CR13","doi-asserted-by":"crossref","first-page":"869","DOI":"10.3390\/diagnostics12040869","volume":"12","author":"C Mulrenan","year":"2022","unstructured":"Mulrenan C, Rhode K, Fischer BM (2022) A literature review on the use of artificial intelligence for the diagnosis of COVID-19 on CT and chest X-ray. Diagnostics 12:869","journal-title":"Diagnostics"},{"key":"9312_CR14","doi-asserted-by":"crossref","first-page":"4820","DOI":"10.3390\/s22134820","volume":"22","author":"MT Huyut","year":"2022","unstructured":"Huyut MT, Velichko A (2022) Diagnosis and prognosis of COVID-19 disease using routine blood values and LogNNet neural network. Sensors 22:4820","journal-title":"Sensors"},{"key":"9312_CR15","doi-asserted-by":"crossref","first-page":"103263","DOI":"10.1016\/j.bspc.2021.103263","volume":"72","author":"S Babaei Rikan","year":"2022","unstructured":"Babaei Rikan S, Sorayaie Azar A, Ghafari A, Bagherzadeh Mohasefi J, Pirnejad H (2022) COVID-19 diagnosis from routine blood tests using artificial intelligence techniques. Biomed Signal Process Control 72:103263","journal-title":"Biomed Signal Process Control"},{"key":"9312_CR16","doi-asserted-by":"crossref","first-page":"15919","DOI":"10.1109\/JIOT.2021.3050775","volume":"8","author":"KH Abdulkareem","year":"2021","unstructured":"Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, Khanna A (2021) Realizing an effective COVID-19 diagnosis system based on machine learning and IoT in smart hospital environment. IEEE Internet Things J 8:15919\u201315928","journal-title":"IEEE Internet Things J"},{"key":"9312_CR17","doi-asserted-by":"publisher","DOI":"10.15392\/bjrs.v10i3.2056","author":"B Takara","year":"2022","unstructured":"Takara B, Freitas F, Bacelar A, Lykawka R, Sanchez MSA (2022) Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs. Braz J Radiat Sci. https:\/\/doi.org\/10.15392\/bjrs.v10i3.2056","journal-title":"Braz J Radiat Sci"},{"key":"9312_CR18","doi-asserted-by":"crossref","first-page":"268","DOI":"10.36311\/jhgd.v32.13324","volume":"32","author":"GC Pinasco","year":"2022","unstructured":"Pinasco GC, de Mattos Farina EMJ, Barcellos Filho FN, Fiorotti WF, Ferreira MCM, de Souza Cruz SC, Colodette AL, Loureiro LR, Com\u00e9rio T, Farias DCS (2022) An interpretable machine learning model for COVID-19 screening. J Human Growth Dev 32:268\u2013274","journal-title":"J Human Growth Dev"},{"key":"9312_CR19","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.110120","volume":"140","author":"TB Alakus","year":"2020","unstructured":"Alakus TB, Turkoglu I (2020) Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solit Fract 140:110120","journal-title":"Chaos, Solit Fract"},{"key":"9312_CR20","doi-asserted-by":"crossref","first-page":"100449","DOI":"10.1016\/j.imu.2020.100449","volume":"21","author":"M AlJame","year":"2020","unstructured":"AlJame M, Ahmad I, Imtiaz A, Mohammed A (2020) Ensemble learning model for diagnosing COVID-19 from routine blood tests. Inform Med Unlocked 21:100449","journal-title":"Inform Med Unlocked"},{"key":"9312_CR21","unstructured":"Batista AFDM, Miraglia JL, Donato THR, and Chiavegatto Filho ADP (2020) COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. MedRxiv"},{"key":"9312_CR22","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.9482","volume":"8","author":"E Avila","year":"2020","unstructured":"Avila E, Kahmann A, Alho C, Dorn M (2020) Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios. PeerJ 8:e9482","journal-title":"PeerJ"},{"key":"9312_CR23","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s42600-020-00112-5","volume":"38","author":"VA Freitas Barbosa","year":"2022","unstructured":"Freitas Barbosa VA, Gomes JC, de Santana MA, Albuquerque JEDA, de Souza RG, de Souza RE, dos Santos WP (2022) Heg. IA: an intelligent system to support diagnosis of Covid-19 based on blood tests. Res Biomed Eng 38:99\u2013116","journal-title":"Res Biomed Eng"},{"key":"9312_CR24","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1137\/0111030","volume":"11","author":"DW Marquardt","year":"1963","unstructured":"Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431\u2013441","journal-title":"J Soc Ind Appl Math"},{"key":"9312_CR25","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1093\/comjnl\/7.2.155","volume":"7","author":"MJD Powell","year":"1964","unstructured":"Powell MJD (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7:155\u2013162","journal-title":"Comput J"},{"key":"9312_CR26","first-page":"39","volume-title":"Numerical methods for nonlinear optimization","author":"E Beale","year":"1972","unstructured":"Beale E (1972) A derivation of conjugate gradients. In: Lootsma FA (ed) Numerical methods for nonlinear optimization. Academic Press, London, pp 39\u201343"},{"key":"9312_CR27","first-page":"586","volume":"1","author":"M Riedmiller","year":"1992","unstructured":"Riedmiller M, Braun H (1992) RPROP-A fast adaptive learning algorithm. Proc ISCIS VII 1:586\u2013591","journal-title":"Proc ISCIS VII"},{"key":"9312_CR28","unstructured":"Chien-Cheng Y and Bin-Da L (2002) A backpropagation algorithm with adaptive learning rate and momentum coefficient. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2:1218\u20131223"},{"key":"9312_CR29","doi-asserted-by":"crossref","unstructured":". Rakitianskaia A and Engelbrecht A (2015) Measuring saturation in neural networks. In: 2015 IEEE Symposium Series on Computational Intelligence, 1:1423\u20131430","DOI":"10.1109\/SSCI.2015.202"},{"key":"9312_CR30","unstructured":"Glorot X, and Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, 9:249\u2013256"},{"key":"9312_CR31","first-page":"449","volume-title":"Detecting feature interactions in agricultural trade data using a deep neural network","author":"J O\u2019Donoghue","year":"2017","unstructured":"O\u2019Donoghue J, Roantree M, McCarren A (2017) Detecting feature interactions in agricultural trade data using a deep neural network. Springer International Publishing, Cham, pp 449\u2013458"},{"key":"9312_CR32","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2020.e05684","volume":"6","author":"MA Barek","year":"2020","unstructured":"Barek MA, Aziz MA, Islam MS (2020) Impact of age, sex, comorbidities and clinical symptoms on the severity of COVID-19 cases: a meta-analysis with 55 studies and 10014 cases. Heliyon 6:e05684","journal-title":"Heliyon"},{"key":"9312_CR33","volume":"16","author":"M Sandoval","year":"2021","unstructured":"Sandoval M, Nguyen DT, Vahidy FS, Graviss EA (2021) Risk factors for severity of COVID-19 in hospital patients age 18\u201329 years. PLoS ONE 16:e0255544","journal-title":"PLoS ONE"},{"key":"9312_CR34","doi-asserted-by":"crossref","first-page":"e2146","DOI":"10.1002\/rmv.2146","volume":"31","author":"B Gallo Marin","year":"2021","unstructured":"Gallo Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, Salazar-Mather TP, Dumenco L, Savaria MC, Aung SN, Flanigan T, Michelow IC (2021) Predictors of COVID-19 severity: a literature review. Rev Med Virol 31:e2146","journal-title":"Rev Med Virol"},{"key":"9312_CR35","doi-asserted-by":"crossref","unstructured":"Pennington AF, Kompaniyets L, Summers AD, Danielson ML, Goodman AB, Chevinsky JR, Preston LE, Schieber LZ, Namulanda G, Courtney J, Strosnider HM, Boehmer TK, Mac Kenzie WR, Baggs J, and Gundlapalli AV (2020) Risk of clinical severity by age and race\/ethnicity among adults hospitalized for COVID-19\u2014United States, March\u2013September 2020. Open Forum Infectious Diseases, 8","DOI":"10.1093\/ofid\/ofaa638"},{"key":"9312_CR36","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.chest.2020.08.2065","volume":"159","author":"JL Rapp","year":"2021","unstructured":"Rapp JL, Lieberman-Cribbin W, Tuminello S, Taioli E (2021) Male sex, severe obesity, older age, and chronic kidney disease are associated with COVID-19 severity and mortality in New York City. Chest 159:112\u2013115","journal-title":"Chest"},{"key":"9312_CR37","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1093\/ajcp\/aqaa231","volume":"155","author":"O Pozdnyakova","year":"2020","unstructured":"Pozdnyakova O, Connell NT, Battinelli EM, Connors JM, Fell G, Kim AS (2020) Clinical significance of CBC and WBC morphology in the diagnosis and clinical course of COVID-19 infection. Am J Clin Pathol 155:364\u2013375","journal-title":"Am J Clin Pathol"},{"key":"9312_CR38","doi-asserted-by":"crossref","unstructured":"Puah SH, Young BE, Chia PY, Ho VK, Loh J, Gokhale RS, Tan SY, Sewa DW, Kalimuddin S, Tan CK, Pada SKMS, Cove ME, Chai LYA, Parthasarathy P, Ho BCH, Ng JJ, Ling LM, Abisheganaden JA, Lee VJM, Tan CH, Lin RTP, Leo YS, Lye DC, Yeo TW, Lim PL, Ang BSP, Lee CC, Lee LSU, Ng OT, Chan M, Marimuthu K, Vasoo S, Wong CS, Lee TH, Sadarangani SP, Lin RJ, Sadasiv MS, Ng DHL, Choy CY, Tan GSE, Tan YK, Ong SWX, Sutjipto S, Lee PH, Tay JY, Ying D, Khoo BY, Tay WC, Ng G, Mah YY, Tan W, Lew SJW, Fong RKC, Oh HML, Chien JMF, Shafi H, Cheong HY, Teo DCH, Tan TT, Tan BH, Low JGH, Wijaya L, Venkatachalam I, Chua YY, Cherng BPZ, Chan YFZ, Phua GC, Goh KJ, Soh JXJ, Zheng S, Lingegowda PB, Peh WM, Lee YL, Ho JY, Chia AYJ, Lin L, Ooi ST, Anantharajah TP, Somani J, Oon JEL, Yan GZ, and t Singapore novel coronavirus outbreak research (2021) Clinical features and predictors of severity in COVID-19 patients with critical illness in Singapore. Sci Rep 11:7477","DOI":"10.1038\/s41598-021-81377-3"},{"key":"9312_CR39","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.amsu.2020.12.035","volume":"62","author":"S Taj","year":"2021","unstructured":"Taj S, Kashif A, Arzinda Fatima S, Imran S, Lone A, Ahmed Q (2021) Role of hematological parameters in the stratification of COVID-19 disease severity. Ann Med Surg 62:68\u201372","journal-title":"Ann Med Surg"},{"key":"9312_CR40","doi-asserted-by":"crossref","first-page":"e0240200","DOI":"10.1371\/journal.pone.0240200","volume":"16","author":"M Marcos","year":"2021","unstructured":"Marcos M, Belhassen-Garc\u00eda M, S\u00e1nchez-Puente A, Sampedro-Gomez J, Azibeiro R, Dorado-D\u00edaz P-I, Marcano-Mill\u00e1n E, Garc\u00eda-Vidal C, Moreiro-Barroso M-T, Cubino-B\u00f3veda N, P\u00e9rez-Garc\u00eda M-L, Rodr\u00edguez-Alonso B, Encinas-S\u00e1nchez D, Pe\u00f1a-Balbuena S, Sobejano-Fuertes E, In\u00e9s S, Carbonell C, L\u00f3pez-Parra M, Andrade-Meira F, L\u00f3pez-Bern\u00fas A, Lorenzo C, Carpio A, Polo-San-Ricardo D, S\u00e1nchez-Hern\u00e1ndez M-V, Borr\u00e1s R, Sagredo-Meneses V, Sanchez P-L, Soriano A, Mart\u00edn-Oterino J-\u00c1 (2021) Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients. PLoS ONE 16:e0240200","journal-title":"PLoS ONE"},{"key":"9312_CR41","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/00365513.2021.1894601","volume":"81","author":"A Alnor","year":"2021","unstructured":"Alnor A, Sandberg MB, Toftanes BE, Vinholt PJ (2021) Platelet parameters and leukocyte morphology is altered in COVID-19 patients compared to non-COVID-19 patients with similar symptomatology. Scand J Clin Lab Invest 81:213\u2013217","journal-title":"Scand J Clin Lab Invest"},{"key":"9312_CR42","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1089\/vim.2020.0062","volume":"34","author":"Y Liu","year":"2021","unstructured":"Liu Y, Liao W, Wan L, Xiang T, Zhang W (2021) Correlation between relative nasopharyngeal virus RNA load and lymphocyte count disease severity in patients with COVID-19. Viral Immunol 34:330\u2013335","journal-title":"Viral Immunol"},{"key":"9312_CR43","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1182\/blood.2020008762","volume":"137","author":"K Althaus","year":"2021","unstructured":"Althaus K, Marini I, Zlamal J, Pelzl L, Singh A, H\u00e4berle H, Mehrl\u00e4nder M, Hammer S, Schulze H, Bitzer M, Malek N, Rath D, B\u00f6sm\u00fcller H, Nieswandt B, Gawaz M, Bakchoul T, Rosenberger P (2021) Antibody-induced procoagulant platelets in severe COVID-19 infection. Blood 137:1061\u20131071","journal-title":"Blood"},{"key":"9312_CR44","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s00592-021-01701-1","volume":"58","author":"H Kaminska","year":"2021","unstructured":"Kaminska H, Szarpak L, Kosior D, Wieczorek W, Szarpak A, Al-Jeabory M, Gawel W, Gasecka A, Jaguszewski MJ, Jarosz-Chobot P (2021) Impact of diabetes mellitus on in-hospital mortality in adult patients with COVID-19: a systematic review and meta-analysis. Acta Diabetol 58:1101\u20131110","journal-title":"Acta Diabetol"},{"key":"9312_CR45","doi-asserted-by":"crossref","first-page":"108347","DOI":"10.1016\/j.diabres.2020.108347","volume":"166","author":"A Abdi","year":"2020","unstructured":"Abdi A, Jalilian M, Sarbarzeh PA, Vlaisavljevic Z (2020) Diabetes and COVID-19: a systematic review on the current evidences. Diabetes Res Clin Pract 166:108347","journal-title":"Diabetes Res Clin Pract"},{"key":"9312_CR46","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.dsx.2021.02.009","volume":"15","author":"M Nassar","year":"2021","unstructured":"Nassar M, Nso N, Baraka B, Alfishawy M, Mohamed M, Nyabera A, Sachmechi I (2021) The association between COVID-19 and type 1 diabetes mellitus: a systematic review. Diabetes Metab Syndr 15:447\u2013454","journal-title":"Diabetes Metab Syndr"},{"issue":"2021","key":"9312_CR47","first-page":"100849","volume":"41","author":"M Naveed","year":"2019","unstructured":"Naveed M, Naeem M, ur Rahman M, Gul Hilal M, Kakakhel MA, Ali G, Hassan A (2019) Review of potential risk groups for coronavirus disease 2019 (COVID-19). New Microb New Infect 41(2021):100849","journal-title":"New Microb New Infect"},{"key":"9312_CR48","doi-asserted-by":"crossref","first-page":"416","DOI":"10.36660\/abc.20200885","volume":"117","author":"LG Santos","year":"2021","unstructured":"Santos LG, Baggio JADO, Leal TC, Costa FA, Fernandes TRMDO, Silva RVD, Armstrong A, Carmo RF, Souza CDFD (2021) Prevalence of systemic arterial hypertension and diabetes mellitus in individuals with COVID-19: a retrospective study of deaths in Pernambuco, Brazil. Arq Bras Cardiol 117:416\u2013422","journal-title":"Arq Bras Cardiol"},{"key":"9312_CR49","doi-asserted-by":"crossref","first-page":"e0248580","DOI":"10.1371\/journal.pone.0248580","volume":"16","author":"FSH Souza","year":"2021","unstructured":"Souza FSH, Hojo-Souza NS, Batista BDDO, da Silva CM, Guidoni DL (2021) On the analysis of mortality risk factors for hospitalized COVID-19 patients: a data-driven study using the major Brazilian database. PLoS ONE 16:e0248580","journal-title":"PLoS ONE"},{"key":"9312_CR50","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s00592-020-01636-z","volume":"58","author":"H Shah","year":"2021","unstructured":"Shah H, Khan MSH, Dhurandhar NV, Hegde V (2021) The triumvirate: why hypertension, obesity, and diabetes are risk factors for adverse effects in patients with COVID-19. Acta Diabetol 58:831\u2013843","journal-title":"Acta Diabetol"},{"key":"9312_CR51","doi-asserted-by":"crossref","unstructured":"Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, and a.t.N.C.-R. Consortium (2020) Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA, 323: 2052\u20132059","DOI":"10.1001\/jama.2020.6775"},{"key":"9312_CR52","doi-asserted-by":"crossref","unstructured":"Mubarik S, Liu X, Eshak ES, Liu K, Liu Q, Wang F, Shi F, Wen H, Bai J, Yu C and Cao J (2021) The association of hypertension with the severity of and mortality from the COVID-19 in the early stage of the epidemic in Wuhan, China: a multicenter retrospective cohort study. Front Med 8","DOI":"10.3389\/fmed.2021.623608"},{"key":"9312_CR53","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a JE-Dl, Rasc\u00f3n-Pacheco RA, Ascencio-Montiel IDJ, Gonz\u00e1lez-Figueroa E, Fern\u00e1ndez-G\u00e1rate JE, Medina-G\u00f3mez OS, Borja-Bustamante P, Santill\u00e1n-Oropeza JA, and Borja-Aburto VH (2021) Hypertension, diabetes and obesity, major risk factors for death in patients with COVID-19 in Mexico. Archiv Med Res 52:443\u2013449","DOI":"10.1016\/j.arcmed.2020.12.002"},{"key":"9312_CR54","volume":"21","author":"T-H Chang","year":"2020","unstructured":"Chang T-H, Chou C-C, Chang L-Y (2020) Effect of obesity and body mass index on coronavirus disease 2019 severity: a systematic review and meta-analysis. Obes Rev 21:e13089","journal-title":"Obes Rev"},{"key":"9312_CR55","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1108\/NFS-11-2020-0421","volume":"51","author":"H Mohseni","year":"2021","unstructured":"Mohseni H, Amini S, Abiri B, Kalantar M (2021) Do body mass index (BMI) and history of nutritional supplementation play a role in the severity of COVID-19? A retrospective study. Nutr Food Sci 51:1017\u20131027","journal-title":"Nutr Food Sci"},{"key":"9312_CR56","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.orcp.2020.07.005","volume":"14","author":"JVV Siqueira","year":"2020","unstructured":"Siqueira JVV, Almeida LG, Zica BO, Brum IB, Barcel\u00f3 A, de Siqueira Galil AG (2020) Impact of obesity on hospitalizations and mortality, due to COVID-19: a systematic review. Obes Res Clin Pract 14:398\u2013403","journal-title":"Obes Res Clin Pract"},{"key":"9312_CR57","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/S2213-8587(21)00089-9","volume":"9","author":"M Gao","year":"2021","unstructured":"Gao M, Piernas C, Astbury NM, Hippisley-Cox J, O\u2019Rahilly S, Aveyard P, Jebb SA (2021) Associations between body-mass index and COVID-19 severity in 6\u00b79 million people in England: a prospective, community-based, cohort study. Lancet Diabetes Endocrinol 9:350\u2013359","journal-title":"Lancet Diabetes Endocrinol"},{"key":"9312_CR58","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1002\/jmv.26336","volume":"93","author":"J Zhu","year":"2021","unstructured":"Zhu J, Pang J, Ji P, Zhong Z, Li H, Li B, Zhang J, Lu J (2021) Coagulation dysfunction is associated with severity of COVID-19: a meta-analysis. J Med Virol 93:962\u2013972","journal-title":"J Med Virol"},{"key":"9312_CR59","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s40560-020-00466-z","volume":"8","author":"Y Yao","year":"2020","unstructured":"Yao Y, Cao J, Wang Q, Shi Q, Liu K, Luo Z, Chen X, Chen S, Yu K, Huang Z, Hu B (2020) D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case control study. J Intensive Care 8:49","journal-title":"J Intensive Care"},{"key":"9312_CR60","doi-asserted-by":"crossref","first-page":"e24","DOI":"10.1111\/bjh.16797","volume":"190","author":"Y Li","year":"2020","unstructured":"Li Y, Zhao K, Wei H, Chen W, Wang W, Jia L, Liu Q, Zhang J, Shan T, Peng Z, Liu Y, Yan X (2020) Dynamic relationship between D-dimer and COVID-19 severity. Br J Haematol 190:e24\u2013e27","journal-title":"Br J Haematol"},{"key":"9312_CR61","doi-asserted-by":"crossref","first-page":"112411","DOI":"10.1016\/j.fluid.2019.112411","volume":"506","author":"EB Melo","year":"2020","unstructured":"Melo EB, Oliveira ET, Martins TD (2020) A neural network correlation for molar density and specific heat of water: predictions at pressures up to 100 MPa. Fluid Phase Equilib 506:112411","journal-title":"Fluid Phase Equilib"},{"key":"9312_CR62","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.ajem.2020.09.018","volume":"39","author":"B Gungor","year":"2021","unstructured":"Gungor B, Atici A, Baycan OF, Alici G, Ozturk F, Tugrul S, Asoglu R, Cevik E, Sahin I, Barman HA (2021) Elevated D-dimer levels on admission are associated with severity and increased risk of mortality in COVID-19: a systematic review and meta-analysis. Am J Emerg Med 39:173\u2013179","journal-title":"Am J Emerg Med"},{"key":"9312_CR63","first-page":"8","volume":"13","author":"A Pirsalehi","year":"2021","unstructured":"Pirsalehi A, Salari S, Baghestani A, Sanadgol G, Shirini D, Baerz MM, Abdi S, Akbari ME, Bashash D (2021) Differential alteration trend of white blood cells (WBCs) and monocytes count in severe and non-severe COVID-19 patients within a 7-day follow-up. Iran J Microbiol 13:8\u201316","journal-title":"Iran J Microbiol"},{"key":"9312_CR64","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0254073","volume":"16","author":"M Kilercik","year":"2021","unstructured":"Kilercik M, Demirelce \u00d6, Serdar MA, Mikailova P, Serteser M (2021) A new haematocytometric index: predicting severity and mortality risk value in COVID-19 patients. PLoS ONE 16:e0254073","journal-title":"PLoS ONE"},{"key":"9312_CR65","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1089\/vim.2020.0166","volume":"34","author":"S Kazancioglu","year":"2021","unstructured":"Kazancioglu S, Yilmaz FM, Bastug A, Sakall\u0131 A, Ozbay BO, Buyuktarakci C, Bodur H, Yilmaz G (2021) Lymphocyte subset alteration and monocyte CD4 expression reduction in patients with severe COVID-19. Viral Immunol 34:342\u2013351","journal-title":"Viral Immunol"},{"key":"9312_CR66","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ajem.2021.01.006","volume":"42","author":"DM Simadibrata","year":"2021","unstructured":"Simadibrata DM, Calvin J, Wijaya AD, Ibrahim NAA (2021) Neutrophil-to-lymphocyte ratio on admission to predict the severity and mortality of COVID-19 patients: a meta-analysis. Am J Emerg Med 42:60\u201369","journal-title":"Am J Emerg Med"},{"key":"9312_CR67","doi-asserted-by":"crossref","DOI":"10.1016\/j.ces.2020.116324","volume":"231","author":"GY Ottaiano","year":"2021","unstructured":"Ottaiano GY, da Cruz INS, da Cruz HS, Martins TD (2021) Estimation of vaporization properties of pure substances using artificial neural networks. Chem Eng Sci 231:116324","journal-title":"Chem Eng Sci"},{"key":"9312_CR68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cherd.2021.03.008","volume":"170","author":"VY Valera","year":"2021","unstructured":"Valera VY, Codolo MC, Martins TD (2021) Artificial neural network for prediction of SO2 removal and volumetric mass transfer coefficient in spray tower. Chem Eng Res Des 170:1\u201312","journal-title":"Chem Eng Res Des"},{"key":"9312_CR69","volume-title":"Neural networks \u2013 a comprehensive foundation","author":"S Haykin","year":"2005","unstructured":"Haykin S (2005) Neural networks \u2013 a comprehensive foundation. Prentice Hall, Delhi"},{"key":"9312_CR70","doi-asserted-by":"crossref","first-page":"107602961989532","DOI":"10.1177\/1076029619895323","volume":"25","author":"TD Martins","year":"2019","unstructured":"Martins TD, Annichino-Bizzacchi JM, Romano AVC, Filho RM (2019) Principal component analysis on recurrent venous thromboembolism. Clin Appl Thromb Hemost 25:1076029619895323","journal-title":"Clin Appl Thromb Hemost"},{"key":"9312_CR71","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","volume":"5","author":"JN Mandrekar","year":"2010","unstructured":"Mandrekar JN (2010) Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 5:1315\u20131316","journal-title":"J Thorac Oncol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09312-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09312-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09312-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:17:00Z","timestamp":1707733020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09312-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"references-count":71,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9312"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09312-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]},"assertion":[{"value":"6 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was approved by the ethical comitee of the Sociedade Beneficente de Senhoras Hospital S\u00edrio-Liban\u00eas (CAAE:\u00a030439920.9.0000.5461).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}