{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T10:29:21Z","timestamp":1787740161539,"version":"build-2784847793"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children\u2019s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.<\/jats:p>","DOI":"10.3390\/ijerph19105959","type":"journal-article","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T09:27:56Z","timestamp":1652520476000},"page":"5959","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0520-7428","authenticated-orcid":false,"given":"Corrado","family":"Lanera","sequence":"first","affiliation":[{"name":"Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8578-9164","authenticated-orcid":false,"given":"Ileana","family":"Baldi","sequence":"additional","affiliation":[{"name":"Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Francavilla","sequence":"additional","affiliation":[{"name":"Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0850-8784","authenticated-orcid":false,"given":"Elisa","family":"Barbieri","sequence":"additional","affiliation":[{"name":"Division of Pediatric Infectious Diseases, Department of Women\u2019s and Children\u2019s Health, University of Padova, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lara","family":"Tramontan","sequence":"additional","affiliation":[{"name":"Consorzio Arsen\u00e0l.IT, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Scamarcia","sequence":"additional","affiliation":[{"name":"Societ\u00e0 Servizi Telematici\u2013Pedianet, 35138 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"Cantarutti","sequence":"additional","affiliation":[{"name":"Societ\u00e0 Servizi Telematici\u2013Pedianet, 35138 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Giaquinto","sequence":"additional","affiliation":[{"name":"Division of Pediatric Infectious Diseases, Department of Women\u2019s and Children\u2019s Health, University of Padova, 35131 Padova, Italy"},{"name":"Societ\u00e0 Servizi Telematici\u2013Pedianet, 35138 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7906-0580","authenticated-orcid":false,"given":"Dario","family":"Gregori","sequence":"additional","affiliation":[{"name":"Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kretzschmar, M., Mangen, M.J., Pinheiro, P., Jahn, B., Fevre, E.M., Longhi, S., Lai, T., Havelaar, A.H., Stein, C., and Cassini, A. 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