Abstract
We propose FedCovid, a new federated learning system based on electronic health records (EHR), to predict COVID-19 vaccination side effects. Federated learning allows diverse data owners to work together to train machine learning models without sharing data, ensuring the privacy of EHR data. However, because EHR data is unique, directly using existing federated learning models may fail. The EHR data is diverse, with numerical and categorical characteristics as well as consecutive visits. Furthermore, each client’s data size is unequal, and the data labels are skewed due to the small number of patients that experience serious side effects. We present an adaptive approach to fuse heterogeneous EHR data and apply data augmentation techniques working with a margin loss to overcome the data imbalance issue in the client model training to address both challenges simultaneously in FedCovid. We recommend that when the server is updated, the data size of each client be taken into account to lessen the impact of clients with small data volumes. Finally, in order to train a stable and successful federated learning model, we suggest a new ordinal training technique. Experiments on a real-world dataset reveal that the suggested model is effective at predicting COVID-19 vaccination adverse effects. The performance increases by 14.35%, 17.81%, and 129.36% on the F1 score, Cohen’s Kappa, and PR-AUC, respectively, compared with the best baseline (The source code of the proposed FedCovid is available at https://github.com/JackqqWang/FedCovid.git).
J. Wang—This work was done when Jiaqi Wang interned at IQVIA.
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Abiodun, K.M., Awotunde, J.B., Aremu, D.R., Adeniyi, E.A.: Explainable ai for fighting covid-19 pandemic: Opportunities, challenges, and future prospects. In: Computational Intelligence for COVID-19 and Future Pandemics, pp. 315–332. Springer, Heidelberg (2022). https://doi.org/10.1007/978-981-16-3783-4_15
Almars, A.M., Gad, I., Atlam, E.-S.: Applications of AI and IoT in COVID-19 vaccine and its impact on social life. In: Hassanien, A.E., Bhatnagar, R., Snášel, V., Yasin Shams, M. (eds.) Medical Informatics and Bioimaging Using Artificial Intelligence. SCI, vol. 1005, pp. 115–127. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-91103-4_7
Borriello, A., Master, D., Pellegrini, A., Rose, J.M.: Preferences for a covid-19 vaccine in australia. Vaccine 39(3), 473–479 (2021)
Chlap, P., Min, H., Vandenberg, N., Dowling, J., Holloway, L., Haworth, A.: A review of medical image data augmentation techniques for deep learning applications. J. Med. Imaging Radiat. Oncol. 65(5), 545–563 (2021)
Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J.: Doctor AI: predicting clinical events via recurrent neural networks. In: MLHC, pp. 301–318 (2016)
Cui, L., Biswal, S., Glass, L.M., Lever, G., Sun, J., Xiao, C.: Conan: complementary pattern augmentation for rare disease detection. In: AAAI, pp. 614–621 (2020)
Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: Proceedings of ICML, pp. 933–941. PMLR (2017)
Dayan, I., et al.: Federated learning for predicting clinical outcomes in patients with covid-19. Nat. Med. 27(10), 1735–1743 (2021)
Diaz, G.A., Parsons, G.T., Gering, S.K., Meier, A.R., Hutchinson, I.V., Robicsek, A.: Myocarditis and pericarditis after vaccination for covid-19. Jama 326(12), 1210–1212 (2021)
Elnaem, M.H., et al.: Covid-19 vaccination attitudes, perceptions, and side effect experiences in Malaysia: do age, gender, and vaccine type matter? Vaccines 9(10), 1156 (2021)
Fallah, A., Mokhtari, A., Ozdaglar, A.: Personalized federated learning: a meta-learning approach. arXiv preprint arXiv:2002.07948 (2020)
Feng, S.Y., et al.: A survey of data augmentation approaches for nlp. arXiv:2105.03075 (2021)
Georgiadis, A., Babbar, V., Silavong, F., Moran, S., Otter, R.: St-fl: Style transfer preprocessing in federated learning for covid-19 segmentation. arXiv (2022)
Gupta, A., Gharehgozli, A.: Developing a machine learning framework to determine the spread of covid-19. Available at SSRN 3635211 (2020)
Hause, A.M., et al.: Safety monitoring of covid-19 vaccine booster doses among adultsâ’’ United States, september 22, 2021-february 6, 2022. Morb. Mortal. Weekly Rep. 71(7), 249 (2022)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429–450 (2020)
Luo, J., Ye, M., Xiao, C., Ma, F.: Hitanet: hierarchical time-aware attention networks for risk prediction on electronic health records. In: KDD, pp. 647–656 (2020)
Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., Gao, J.: Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: KDD, pp. 1903–1911 (2017)
Ma, F., Gao, J., Suo, Q., You, Q., Zhou, J., Zhang, A.: Risk prediction on electronic health records with prior medical knowledge. In: KDD, pp. 1910–1919 (2018)
Ma, F., et al.: A general framework for diagnosis prediction via incorporating medical code descriptions. In: BIBM, pp. 1070–1075. IEEE (2018)
Mariappan, M.B., Devi, K., Venkataraman, Y., Lim, M.K., Theivendren, P.: Using AI and ml to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during covid-19 pandemic. Int. J. Logist. Manag. (2022)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)
Mohamed, K., et al.: Covid-19 vaccinations: the unknowns, challenges, and hopes. J. Med. Virol. 94(4), 1336–1349 (2022)
Napolitano, F., Xu, X., Gao, X.: Impact of computational approaches in the fight against covid-19: an AI guided review of 17 000 studies. Brief. Bioinf. 23(1), bbab456 (2022)
Rahimi, K.: Guillain-barre syndrome during covid-19 pandemic: an overview of the reports. Neurol. Sci. 41(11), 3149–3156 (2020)
Schultz, N.H.: Thrombosis and thrombocytopenia after chadox1 ncov-19 vaccination. New Engl. J. Med. 384(22), 2124–2130 (2021)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Shimabukuro, T.T., Cole, M., Su, J.R.: Reports of anaphylaxis after receipt of mrna covid-19 vaccines in the usâ’’december 14, 2020-january 18, 2021. Jama 325(11), 1101–1102 (2021)
Sprent, J., King, C.: Covid-19 vaccine side effects: the positives about feeling bad. Science Immunol. 6(60), eabj9256 (2021)
Vaid, A., et al.: Federated learning of electronic health records to improve mortality prediction in hospitalized patients with covid-19: Machine learning approach. JMIR Med. Inf. 9(1), e24207 (2021)
Vaswani, A., et al.: Attention is all you need. In: NeurIPS 30 (2017)
Wang, Y., Hu, M., Li, Q., Zhang, X.P., Zhai, G., Yao, N.: Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with covid-19 in an accurate and unobtrusive manner. arXiv preprint arXiv:2002.05534 (2020)
Zhavoronkov, A., et al.: Potential non-covalent sars-cov-2 3c-like protease inhibitors designed using generative deep learning approaches and reviewed by human medicinal chemist in virtual reality (2020)
Zhou, Y., He, J.: A randomized approach for crowdsourcing in the presence of multiple views. In: ICDM, pp. 685–694. IEEE Computer Society (2017)
Zhou, Y., Wu, J., Wang, H., He, J.: Adversarial robustness through bias variance decomposition: a new perspective for federated learning. arXiv (2020)
Zhou, Y., Ying, L., He, J.: Multic\(^2\): an optimization framework for learning from task and worker dual heterogeneity. In: SDM, pp. 579–587. SIAM (2017)
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Wang, J., Qian, C., Cui, S., Glass, L., Ma, F. (2023). Towards Federated COVID-19 Vaccine Side Effect Prediction. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13718. Springer, Cham. https://doi.org/10.1007/978-3-031-26422-1_27
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