ArticleInternational journal of environmental research and public health2020
Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Beyond National Averages: Spatial and Temporal Patterns in Type 2 Diabetes Dynamics in Romania-A 10-Year Time Series Study.Clinics and practice · 2026Article
- Trends in epidemics pertaining to notifiable infectious diseases in China and prediction models for key diseases: a case study of Ziyang County.BMC public health · 2025Article
- Prediction of the disease burden of nasopharyngeal carcinoma in China: A comparative analysis of ARIMA, LSTM, and DNN models.Medicine · 2025Article
- Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.Journal of the American Heart Association · 2025Review
- Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.BMC public health · 2025Article
- AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania's First Wave of COVID-19.Bioengineering (Basel, Switzerland) · 2024Article
- Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study.JMIR formative research · 2024Article
- Cleaned Meta Pseudo Labels-Based Pet Behavior Recognition Using Time-Series Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study.Journal of medical Internet research · 2023Article
- Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.Environmental science and pollution research international · 2023Article
- Big Data, Decision Models, and Public Health.International journal of environmental research and public health · 2022Article
- Hypertension and Diabetes in Akatsi South District, Ghana: Modeling and Forecasting.BioMed research international · 2022Article
- Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.PLoS neglected tropical diseases · 2021Article
- Big Data, Decision Models, and Public Health.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.