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

Victor Olsavszky, Mihnea Dosius, Cristian Vladescu and Johannes Benecke
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Victor Olsavszky: Department of Dermatology, Venereology and Allergy, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Theodor-Kutzer-Ufer 1–3, 68167 Mannheim, Germany
Mihnea Dosius: National School of Public Health Management and Professional Development, Str. Vaselor, nr. 31, 030167 Bucharest, Romania
Cristian Vladescu: National School of Public Health Management and Professional Development, Str. Vaselor, nr. 31, 030167 Bucharest, Romania
Johannes Benecke: Department of Dermatology, Venereology and Allergy, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Theodor-Kutzer-Ufer 1–3, 68167 Mannheim, Germany

IJERPH, 2020, vol. 17, issue 14, 1-17

Abstract: 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.

Keywords: automated machine learning; deep learning; artificial intelligence; deadliest diseases; time series; disease prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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