Artificial Intelligence in Public Health and Epidemiology

Thiébaut, Rodolphe; Thiessard, Frantz

As quoted in the synopsis of the Public Health and Epidemiology Informatics section of the 2017 IMIA Yearbook [1] , precision public/global health and digital epidemiology are terms that are still in use in 2018 [2, 3] . The first term is about providing the right intervention to the right population at the right time [2] . The second term is about the use of digital data, especially those that were not collected on purpose, to answer epidemiologic questions [3] . Both refer to the unforeseen opportunities provided by our digital world and new technologies. Although genomics (and more broadly any "-omics") data continue to contribute, as it is the case for precision medicine, there are many other sources of information that can be used: social networks, internet search engines, cell phone data, electronic health data, and more. The challenge today is to analyze these big data in a meaningful way. One recently improved method that showed very nice success especially in image analysis is deep learning [4] . Applications of this method appear to be only limited by the quantity of information available. Predicting the unplanned readmission at the hospital within 6 months based on electronic health data [5] , de-identifying electronic health records (EHRs) [6] , analyzing social media [7] [8] [9] are various types of applications relevant in epidemiology and public health. But artificial intelligence covers many other techniques, such as machine learning approaches and statistical learning that offer a panel of methods which usefulness is only limited by pairing them with the right question; the two best papers of this year section are very good examples [6, 7] . Naïvely mining any large dataset will not give immediate answers. Epidemiologic approaches start with clever and appropriate questions, careful collection of relevant data with the most appropriate design, and validation of the results.