Abstract
Hospital overloads and limited healthcare resources (ICU beds, ventilators, etc.) are fundamental issues related to the outbreak of the COVID-19 pandemic. Machine learning techniques can help the hospitals to recognise in advance the patients at risk of death, and consequently to allocate their resources in a more efficient way. In this paper we present a tool based on Recurrent Neural Networks to predict the risk of death for hospitalised patients with COVID-19. The features used in our predictive models consist of demographics information, several laboratory tests, and a score that indicates the severity of the pulmonary damage observed by chest X-ray exams. The networks were trained and tested using data of 2000 patients hospitalised in Lombardy, the region most affected by COVID-19 in Italy. The experimental results show good performance in solving the addressed task.
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References
Abdulaal, A., Patel, A., Charani, E., Denny, S., Mughal, N., Moore, L.: Prognostic modeling of COVID-19 using artificial intelligence in the united kingdom: model development and validation. J. Med. Internet. Res. 22(8) (2020)
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
An, C., Lim, H., Kim, D.W., Chang, J.H., Choi, Y.J., Kim, S.W.: Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study. Sci. Rep. 10(1), 1–11 (2020)
Anysz, H., Zbiciak, A., Ibadov, N.: The influence of input data standardization method on prediction accuracy of artificial neural networks. Procedia Eng. 153, 66–70 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015 (2015)
Beeksma, M., Verberne, S., van der Bosch, A., Das, E., Hendrickx, I., Groenewoud, S.: Predicting life expectancy with a LSTM neural network using electronic medical records. BMC Med. Inf. Decis. Making (2019)
Borghesi, A., Maroldi, R.: COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. La Radiol. Med. 125(5), 509–513 (2020). https://doi.org/10.1007/s11547-020-01200-3
Bullock, J., Luccioni, A., Pham, K.H., Lam, C.S.N., Luengo-Oroz, M.A.: Mapping the landscape of artificial intelligence applications against COVID-19. J. Artif. Intell. Res. 69, 807–845 (2020)
Fakhfakh, M., Bouaziz, B., Gargouri, F., Chaari, L.: Prognet: Covid-19 prognosis using recurrent and convolutional neural networks. medRxiv (2020)
Gama, J.A., Žliobaitundefined, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4) (2014)
Gerevini, A.E., Maroldi, R., Olivato, M., Putelli, L., Serina, I.: Prognosis prediction in COVID-19 patients from lab tests and x-ray data through randomized decision trees. In: Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data. CEUR Workshop Proceedings, vol. 2675 (2020)
Gruber, N., Jockisch, A.: Are GRU cells more specific and LSTM cells more sensitive in motive classification of text? Front. Artif. Intell. 3, 40 (2020)
He, H., Sun, X.: F-score driven max margin neural network for named entity recognition in Chinese social media. arXiv preprint arXiv:1611.04234 (2016)
Kawahara, J., Hamarneh, G.: Fully convolutional networks to detect clinical dermoscopic features. In: International Skin Imaging Collaboration (ISIC) 2017 Challenge at the International Symposium on Biomedical Imaging (ISBI) (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)
Liu, L., et al.: Multi-task learning via adaptation to similar tasks for mortality prediction of diverse rare diseases. arXiv preprint arXiv:2004.05318 (2020)
Mangal, S., Joshi, P., Modak, R.: LSTM vs. GRU vs. bidirectional RNN for script generation. CoRR abs/1908.04332 (2019). http://arxiv.org/abs/1908.04332
Mazzatorta, P., Benfenati, E., Neagu, D., Gini, G.: The importance of scaling in data mining for toxicity prediction. J. Chem. Inf. Comput. Sci. 42(5), 1250–1255 (2002)
Pastor-Pellicer, J., Zamora-Martínez, F., España-Boquera, S., Castro-Bleda, M.J.: F-measure as the error function to train neural networks. In: Rojas, I., Joya, G., Gabestany, J. (eds.) IWANN 2013. LNCS, vol. 7902, pp. 376–384. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38679-4_37
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset Shift in Machine Learning. The MIT Press, Cambridge (2009)
Rakitianskaia, A.S., Engelbrecht, A.P.: Training feedforward neural networks with dynamic particle swarm optimisation. Swarm Intell. 6(3), 233–270 (2012)
Yan, L., et al.: An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2, 1–6 (2020)
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Chiari, M., Gerevini, A.E., Olivato, M., Putelli, L., Rossetti, N., Serina, I. (2021). An Application of Recurrent Neural Networks for Estimating the Prognosis of COVID-19 Patients in Northern Italy. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_36
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