Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

• Main Authors: Victor Olsavszky, Mihnea Dosius, Cristian Vladescu, Johannes Benecke
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: automated machine learning; deep learning; artificial intelligence; deadliest diseases; time series; disease prediction
• Online Access: https://www.mdpi.com/1660-4601/17/14/4979
• ISSN: 1661-7827; 1660-4601

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.