Wang, H., Abbas, K. M., Abbasifard, M., Abbasi-Kangevari, M., and Abbastabar, H., et al., Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the global burden of disease study 2019. Lancet. 396(10258):1160–1203, 2020. https://doi.org/10.1016/s0140-6736(20)30977-6

Article  Google Scholar 

Fertility, G., and COllaborators, F., Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the global burden of disease study 2021. Lancet. 2024. https://doi.org/10.1016/S0140-6736(24)00550-6

Population Division, D. O. E., and Social Affairs, U. N., World population prospects, 2022. https://population.un.org/wpp. Accessed 22 Aug 2023.

Ley, C., Martin, R. K., Pareek, A., Groll, A., Seil, R., and Tischer, T., Machine learning and conventional statistics: making sense of the differences. Knee Surgery, Sports Traumatology, Arthroscopy 30(3):753–757, 2022. https://doi.org/10.1007/s00167-022-06896-6

Article  PubMed  Google Scholar 

Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N., and Fanos, V., Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina. 56(9):455, 2020. https://doi.org/10.3390/medicina56090455

Article  PubMed  PubMed Central  Google Scholar 

Cifuentes, M., Davis, M., Fernald, D., Gunn, R., Dickinson, P., and Cohen, D. J., Electronic health record challenges, workarounds, and solutions observed in practices integrating behavioral health and primary care. J. Am. Board Fam. Med. 28(Supplement 1):63–72, 2015. https://doi.org/10.3122/jabfm.2015.s1.150133

Article  Google Scholar 

Camm, A. J., and Fox, K. A. A., Strengths and weaknesses of “real-world” studies involving non-vitamin k antagonist oral anticoagulants. Open Heart. 5(1), 2018. https://doi.org/10.1136/openhrt-2018-000788. https://openheart.bmj.com/content/5/1/e000788.full.pdf

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I., Attention is all you need, 2017.

Bahdanau, D., Cho, K., and Bengio, Y., Neural machine translation by jointly learning to align and translate, 2016.

Luong, M.-T., Pham, H., and Manning, C. D., Effective approaches to attention-based neural machine translation, 2015.

Ribeiro, M. T., Singh, S., and Guestrin, C., Why should i trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 1135–1144. New York,USA: Association for Computing Machinery, 2016. https://doi.org/10.1145/2939672.2939778

Lundberg, S. M., and Lee, S.-I., A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (Eds.), Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., 2017. https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

Zhang, J., Kowsari, K., Harrison, J. H., Lobo, J. M., and Barnes, L. E., Patient2vec: a personalized interpretable deep representation of the longitudinal electronic health record. IEEE Access. 6:65333–65346, 2018. https://doi.org/10.1109/access.2018.2875677

Article  Google Scholar 

Sha, Y., and Wang, M. D., Interpretable predictions of clinical outcomes with an attention-based recurrent neural network. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics. ACM-BCB ’17, pp. 233–240. New York, NY, USA: Association for Computing Machinery, 2017. https://doi.org/10.1145/3107411.3107445

Choi, E., Bahadori, M. T., Kulas, J. A., Schuetz, A., Stewart, W. F., and Sun, J., Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. NIPS’16, pp. 3512–3520. Red Hook, NY, USA: Curran Associates Inc., (2016).

Collins, G. S., Reitsma, J. B., Altman, D. G., Moons, and K. G. M., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 350(jan07 4):7594–7594, 2015. https://doi.org/10.1136/bmj.g7594

SIDIAP: SIDIAP. www.sidiap.org. Accessed 05 Jan 2023.

Recalde, M., Rodríguez, C., Burn, E., Far, M., and García, D., et al., Data resource profile: The information system for research in primary care (SIDIAP). Int. J. Epidemiol. 2022. https://doi.org/10.1093/ije/dyac068

Organization, W. H., International statistical classification of diseases and related health problems 10th Revision. https://icd.who.int/browse10/2019/en. Accessed 13 July 2023.

Public Health, N. I., ATC/DDD Index, 2023. https://www.whocc.no/atc_ddd_index/. Accessed 13 July 2023.

Calderón-Larrañaga, A., Vetrano, D. L., Onder, G., Gimeno-Feliu, L. A., and Coscollar-Santaliestra, C., et al., Assessing and measuring chronic multimorbidity in the older population: A proposal for its operationalization. J. Gerontol. A Biol. Sci. Med. Sci. 233, 2016. https://doi.org/10.1093/gerona/glw233

Orfila, F., Carrasco-Ribelles, L. A., Abellana, R., Roso-Llorach, A., Cegri, F., Reyes, C., and Violán, C., Validation of an electronic frailty index with electronic health records: eFRAGICAP index. BMC Geriatrics. 22(1), 2022. https://doi.org/10.1186/s12877-022-03090-8

Duque, I., Domínguez-Berjón, M. F., Cebrecos, A., Prieto-Salceda, M. D., and Esnaola, S., et al.: Índice de privación en españa por sección censal en 2011. Gac. Sanit. 35(2):113–122, 2021. https://doi.org/10.1016/j.gaceta.2019.10.008

Carrasco-Ribelles, L. A., GitHub/ARIADNEhr. https://github.com/IDIAPJGol/ARIADNEhr

Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., Au, Q., Casalicchio, G., Kotthoff, L., Bischl, B.: mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. 2019.https://doi.org/10.21105/joss.01903

Carrasco-Ribelles, L. A., Roso-Llorach, A., Cabrera-Bean, M., Costa-Garrido, A., and Zabaleta-del-Olmo, E., et al., Dynamics of multimorbidity and frailty, and their contribution to mortality, nursing home and home care need: A primary care cohort of 1 456 052 ageing people. eClinicalMedicine. 52:101610, 2022. https://doi.org/10.1016/j.eclinm.2022.101610

Qiu, W., Chen, H., Dincer, A. B., Lundberg, S., Kaeberlein, M., and Lee, S.-I., Interpretable machine learning prediction of all-cause mortality. Commun. Med. 2(1), 2022. https://doi.org/10.1038/s43856-022-00180-x

Krasowski, A., Krois, J., Kuhlmey, A., Meyer-Lueckel, H., and Schwendicke, F., Predicting mortality in the very old: a machine learning analysis on claims data. Sci. Rep. 12(1), 2022. https://doi.org/10.1038/s41598-022-21373-3

Greiner, M. A., Qualls, L. G., Iwata, I., White, H. K., Molony, S. L., Sullivan, M. T., Burke, B., Schulman, K. A., and Setoguchi, S., Predicting nursing home placement among home- and community-based services program participants. Am. J. Manag. Care. 20(12), 535–536, 2014.

Google Scholar 

Kobewka, D. M., Mulpuru, S., Chassé, M., Thavorn, K., Lavallée, L. T., English, S. W., Neilipovitz, B., Neilipovitz, J., Forster, A. J., and McIsaac, D. I., Predicting the need for supportive services after discharged from hospital: a systematic review. BMC Health Serv. Res. 20(1), 2020. https://doi.org/10.1186/s12913-020-4972-6

Carrasco-Ribelles, L. A., Llanes-Jurado, J., Gallego-Moll, C., Cabrera-Bean, M., Monteagudo-Zaragoza, M., Violán, C., and Zabaleta-del-Olmo, E., Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review. J. Am. Med. Inform. Assoc. 2023. https://doi.org/10.1093/jamia/ocad168

Article  PubMed  PubMed Central  Google Scholar 

Lipton, Z. C., The Mythos of Model Interpretability, 2017

Wiegreffe, S., Pinter, Y., Attention is not not explanation. CoRR. arXiv:1908.04626, 2019

Jain, S., and Wallace, B. C., Attention is not explanation. CoRR. arXiv:1902.10186, 2019.

Bibal, A., Cardon, R., Alfter, D., Wilkens, R., Wang, X., François, T., Watrin, P., Is attention explanation? an introduction to the debate. In: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3889–3900. Dublin, Ireland: Association for Computational Linguistics, 2022. https://doi.org/10.18653/v1/2022.acl-long.269. https://aclanthology.org/2022.acl-long.269

SIDIAP: SIDIAP. https://www.sidiap.org/index.php/en/solicituds-en. Accessed 05 Jan 2023.