ArticleApplied soft computing2022
COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach.
Article in Applied soft computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Performance evaluation of ARIMA, VAR, ANN, GRU, and LSTM for multivariate healthcare time-series prediction: a rigorous, reproducible benchmarking study with ablation analysis on MIMIC-III and eICU.Frontiers in artificial intelligence · 2026Article
- How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025Review
- Taxing mechanisms on salty foods: investigation of effectiveness through price elasticity and cross price elasticity of demand.Journal of health, population, and nutrition · 2025Article
- CoViNAR: a context-aware social media dataset for pandemic severity level prediction and analysis.Frontiers in artificial intelligence · 2025Article
- Comparative analysis prediction of prostate and testicular cancer mortality using machine learning: accuracy study.Sao Paulo medical journal = Revista paulista de medicina · 2025Article
- A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness.Health care science · 2024Article
- Article
- Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.Scientific reports · 2024Article
- A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets.JMIR formative research · 2024Article
- A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM.BMC infectious diseases · 2023Article
- Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases.Frontiers in artificial intelligence · 2023Article
- Predicting mild cognitive impairment among Chinese older adults: a longitudinal study based on long short-term memory networks and machine learning.Frontiers in aging neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent literature has revealed a growing interest in methods for anticipating the demand for medical items and personnel at hospital, especially during turbulent scenarios such as the COVID-19 pandemic. In times like those, new variables appear and affect the once known demand behavior. This paper investigates the hypothesis that the combined Prophet-LSTM method results in more accurate forecastings for COVID-19 hospital Intensive Care Units (ICUs) demand than both standalone models, Prophet and LSTM (Long Short-Term Memory Neural Network). We also compare the model to well-established demand forecasting benchmarks. The model is tested to a representative Brazilian municipality that serves as a medical reference to other cities within its region. In addition to traditional time series components, such as trend and seasonality, other variables such as the current number of daily COVID-19 cases, vaccination rates, non-pharmaceutical interventions, social isolation index, and regional hospital beds occupation are also used to explain the variations in COVID-19 hospital ICU demand. Results indicate that the proposed method produced Mean Average Errors (MAE) from 13% to 45% lower than well established statistical and machine learning forecasting models, including the standalone models.
Indexed as
Identifiers
What OpenQuestion holds
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.