Deep learning techniques for electronic health record analysis

Following the general trend, the amount of digital information in the stored electronic health records (EHRs) had an explosion in the last decade. EHRs are not anymore used, as in the past, to store basic information of the patient and administrative tasks, but they may include a range of data, including the medical history of the patient, laboratory test results, demographics, medication and allergies, immunization status, radiology images, vital signs. At the present, the problem has shifted from collecting massive amounts of data to understanding it, i.e. use EHRs for turning data into knowledge, conclusions and actions. EHRs were not designed to forecast disease risk or disease progression or to determine the right treatment, but if they are combined with artificial intelligence (AI) algorithm this issue became possible. The need for tools allowing to construct predictive models capturing disease progression is a priority. In the recent past EHRs were analyzed using traditional machine learning techniques, whereas recently the progress in the field of deep learning let to the application of deep learning techniques to EHRs. This paper reports a brief overview of some recently developed deep learning tools for EHRs.