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A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster
| Content Provider | MDPI |
|---|---|
| Author | Lanera, Corrado Baldi, Ileana Francavilla, Andrea Barbieri, Elisa Tramontan, Lara Scamarcia, Antonio Cantarutti, Luigi Giaquinto, Carlo Gregori, Dario |
| Copyright Year | 2022 |
| Description | 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’s 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. |
| Starting Page | 5959 |
| e-ISSN | 16604601 |
| DOI | 10.3390/ijerph19105959 |
| Journal | International Journal of Environmental Research and Public Health |
| Issue Number | 10 |
| Volume Number | 19 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-13 |
| Access Restriction | Open |
| Subject Keyword | International Journal of Environmental Research and Public Health Medical Informatics Electronic Health Records Infectious Disease Varicella-zoster Deep Learning Natural Language Processing |
| Content Type | Text |
| Resource Type | Article |
