On the Intensive Care Unit Admission During the COVID-19 Pandemic in the Region of Lleida, Spain: A Machine Learning Study

  1. Florensa, Didac
  2. Mateo, Jordi
  3. Solsona, Francesc
  4. Godoy, Pere
  5. Espinosa-Leal, Leonardo
Libro:
Proceedings of ELM 2021

ISSN: 2363-6084 2363-6092

ISBN: 9783031216770 9783031216787

Año de publicación: 2023

Páginas: 92-103

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-031-21678-7_9 GOOGLE SCHOLAR lock_openAcceso abierto editor

Referencias bibliográficas

  • Idescat. Anuari estadístic de Catalunya. Densitat de població. Comarques i Aran, àmbits i províncies (2014). https://www.idescat.cat/pub/?id=aec &n=249 &t=2014
  • Akusok, A., Espinosa Leal, L., Björk, K.M., Lendasse, A.: Scikit-elm: an extreme learning machine toolbox for dynamic and scalable learning. In: International Conference on Extreme Learning Machine, pp. 69–78. Springer (2019)
  • Ao, Y., Li, H., Zhu, L., Ali, S., Yang, Z.: The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. J. Pet. Sci. Eng. 174, 776–789 (Mar 2019). https://doi.org/10.1016/J.PETROL.2018.11.067
  • Barua, S., Islam, M.M., Yao, X., Murase, K.: MWMOTE - Majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405–425 (Feb 2014). https://doi.org/10.1109/TKDE.2012.232
  • Buetti, N., Ruckly, S., de Montmollin, E., Reignier, J., Terzi, N., Cohen, Y., Siami, S., Dupuis, C., Timsit, J.F.: COVID-19 increased the risk of ICU-acquired bloodstream infections: a case-cohort study from the multicentric OUTCOMEREA network. Intensive Care Med. 47(2), 180–187 (Jan 2021). https://doi.org/10.1007/S00134-021-06346-W, https://link.springer.com/article/10.1007/s00134-021-06346-w
  • Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (Jun 2002). https://doi.org/10.1613/jair.953, https://www.jair.org/index.php/jair/article/view/10302
  • Cheng, F.Y., Joshi, H., Tandon, P., Freeman, R., Reich, D.L., Mazumdar, M., Kohli-Seth, R., Levin, M.A., Timsina, P., Kia, A.: Using machine learning to predict ICU transfer in hospitalized COVID-19 patients. J. Clin. Med. 9(6), 1668 (Jun 2020). https://doi.org/10.3390/JCM9061668, https://www.mdpi.com/2077-0383/9/6/1668/htmhttps://www.mdpi.com/2077-0383/9/6/1668
  • Cutler, A., Cutler, D.R., Stevens, J.R.: Random forests. Springer, Boston, MA, Boston, MA (2012). https://link.springer.com/chapter/10.1007/978-1-4419-9326-7_5
  • Esai Selvan, M.: Risk factors for death from COVID-19. Nat. Rev. Immun. 20(7), 407–407 (May 2020). https://doi.org/10.1038/s41577-020-0351-0, https://www.nature.com/articles/s41577-020-0351-0
  • Espinosa-Leal, L., Akusok, A., Lendasse, A., Björk, K.M.: Website classification from webpage renders. In: International Conference on Extreme Learning Machine, pp. 41–50. Springer (2019)
  • Florensa, D., Godoy, P., Mateo, J., Solsona, F., Pedrol, T., Mesas, M., Pinol, R.: The use of multiple correspondence analysis to explore associations between categories of qualitative variables and cancer incidence. IEEE J. Biomed. Health Inf. 25(9), 3659–3667 (Sep 2021). https://doi.org/10.1109/JBHI.2021.3073605
  • Geetha, R., Sivasubramanian, S., Kaliappan, M., Vimal, S., Annamalai, S.: Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier. J. Med. Syst. 43(9), 1–19 (2019). https://doi.org/10.1007/s10916-019-1402-6
  • Gianfrancesco, M.A., Tamang, S., Yazdany, J., Schmajuk, G.: Potential biases in machine learning algorithms using electronic health record data. JAMA Intern. Med. 178(11), 1544–1547 (Nov 2018). https://doi.org/10.1001/JAMAINTERNMED.2018.3763, https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2697394
  • H, B., JM, H., M, T., VM, K., A, M.: Predicting breast cancer risk using interacting genetic and demographic factors and machine learning. Sci. Rep. 10(1) (Dec 2020). https://doi.org/10.1038/S41598-020-66907-9, https://pubmed.ncbi.nlm.nih.gov/32632202/
  • Han, H., Wang, W.Y., Mao, B.H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS. vol. 3644, pp. 878–887. Springer (2005). https://link.springer.com/chapter/10.1007/11538059_91
  • He, F., Quan, Y., Lei, M., Liu, R., Qin, S., Zeng, J., Zhao, Z., Yu, N., Yang, L., Cao, J.: Clinical features and risk factors for ICU admission in COVID-19 patients with cardiovascular diseases. Aging Dis. 11(4), 763 (2020). https://doi.org/10.14336/AD.2020.0622, /pmc/articles/PMC7390529/ /pmc/articles/PMC7390529/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390529/
  • Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (Dec 2006). https://doi.org/10.1016/J.NEUCOM.2005.12.126
  • KaurHarsurinder, Singh, P., Kaur, M.: A Systematic review on imbalanced data challenges in machine learning. ACM Comput. Surv. (CSUR) 52(4) (Aug 2019). https://doi.org/10.1145/3343440, https://dl.acm.org/doi/abs/10.1145/3343440
  • Lemon, S.C., Roy, J., Clark, M.A., Friedmann, P.D., Rakowski, W.: Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Ann. Behav. Med. 26(3), 172–181 (2003). https://link.springer.com/article/10.1207/S15324796ABM2603_02
  • Lighter, J., Phillips, M., Hochman, S., Sterling, S., Johnson, D., Francois, F., Stachel, A.: Obesity in patients younger than 60 years is a risk factor for COVID-19 hospital admission. Clin. Infect. Dis. 71(15), 896–897 (Jul 2020). https://doi.org/10.1093/CID/CIAA415, https://academic.oup.com/cid/article/71/15/896/5818333
  • Lu, W., Hou, H., Chu, J.: Feature fusion for imbalanced ECG data analysis. Biomed. Sig. Process. Control 41, 152–160 (Mar 2018). https://doi.org/10.1016/J.BSPC.2017.11.010
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
  • Rahman, M.M., Davis, D.N.: Addressing the class imbalance problem in medical datasets. Int. J. Mach. Learn. Comput. 224–228 (2013). https://doi.org/10.7763/IJMLC.2013.V3.307
  • RD, N., T, A., L, L., I, D.: Diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation: a meta-analysis. Asian Pac. J. Cancer Prev. : APJCP 19(7), 1747–1752 (Jul 2018). https://doi.org/10.22034/APJCP.2018.19.7.1747, https://pubmed.ncbi.nlm.nih.gov/30049182/
  • Roncon, L., Zuin, M., Rigatelli, G., Zuliani, G.: Diabetic patients with COVID-19 infection are at higher risk of ICU admission and poor short-term outcome. J. Clin. Virol. 127, 104354 (Jun 2020). https://doi.org/10.1016/J.JCV.2020.104354
  • Subudhi, S., Verma, A., Patel, A.B., Hardin, C.C., Khandekar, M.J., Lee, H., McEvoy, D., Stylianopoulos, T., Munn, L.L., Dutta, S., Jain, R.K.: Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19. npj Digit. Med. 4(1), 1–7 (May 2021). https://doi.org/10.1038/s41746-021-00456-x, https://www.nature.com/articles/s41746-021-00456-x
  • Sun, Z., Song, Q., Zhu, X., Sun, H., Xu, B., Zhou, Y.: A novel ensemble method for classifying imbalanced data. Pattern Recogn. 48(5), 1623–1637 (May 2015). https://doi.org/10.1016/J.PATCOG.2014.11.014
  • Tartari, F., Guglielmo, A., Fuligni, F., Pileri, A.: Changes in emergency service access after spread of COVID19 across Italy. J. Eur. Acad. Dermatol. Venereology 34(8), e350–e351 (Aug 2020). https://doi.org/10.1111/JDV.16553,/pmc/articles/PMC7267617/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267617/
  • Tharwat, A.: Classification assessment methods. Appl. Comput. Inf. 17(1), 168–192 (2020)
  • Ting, W.C., Lu, Y.C.A., Ho, W.C., Cheewakriangkrai, C., Chang, H.R., Lin, C.L.: Machine learning in prediction of second primary cancer and recurrence in colorectal cancer. Int. J. Med. Sci. 17(3), 280–291 (2020). https://doi.org/10.7150/IJMS.37134
  • Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., et. al: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323(11), 1061–1069 (Mar 2020). https://doi.org/10.1001/JAMA.2020.1585, https://jamanetwork.com/journals/jama/fullarticle/2761044
  • Yan, S., Qian, W., Guan, Y., Zheng, B.: Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method. Med. Phys. 43(6Part1), 2694–2703 (May 2016). https://doi.org/10.1118/1.4948499, http://doi.wiley.com/10.1118/1.4948499
  • Zhang, Y., Zhu, S., Yuan, Z., Li, Q., Ding, R., Bao, X., Zhen, T., Fu, Z., Fu, H., Xing, K., Yuan, H., Chen, T.: Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis. BMC Cancer 20(1), 1–12 (Nov 2020). https://doi.org/10.1186/S12885-020-07626-2, https://bmccancer.biomedcentral.com/articles/10.1186/s12885-020-07626-2
  • Zhao, Z., Chen, A., Hou, W., Graham, J.M., Li, H., Richman, P.S., Thode, H.C., Singer, A.J., Duong, T.Q.: Prediction model and risk scores of ICU admission and mortality in COVID-19. PLOS ONE 15(7), e0236618 (Jul 2020). https://doi.org/10.1371/JOURNAL.PONE.0236618, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236618