ArticleSignal, image and video processing2022
Forecasting of medical equipment demand and outbreak spreading based on deep long short-term memory network: the COVID-19 pandemic in Turkey.
Article in Signal, image and video processing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Integrating Predictive Analytics into Hospital Automation: Comparative Evaluation of Prediction Models for Patient Monitor Demand in Operating Rooms.Bioengineering (Basel, Switzerland) · 2026Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.Engineering applications of artificial intelligence · 2023Article
- Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Applications of deep learning into supply chain management: a systematic literature review and a framework for future research.Artificial intelligence review · 2023Article
- COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach.Applied soft computing · 2022Article
- Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature.International journal of intelligent systems · 2022Article
- A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The need for healthcare equipment has increased due to the COVID-19 outbreak. Forecasting of these demands allows states to use their resources effectively. Artificial intelligence-based forecasting models play an important role in the forecasting of medical equipment demand during infectious disease periods. In this study, a deep model approach is presented, which is based on a multilayer long short-term memory network for forecasting of medical equipment demand and outbreak spreading, during the coronavirus outbreak (COVID-19). The proposed model consists of stages: normalization, deep LSTM networks and dropout-dense-regression layers, in order of process. Firstly, the daily input data were subjected to a normalization process. Afterward, the multilayer LSTM network model, which was a deep learning approach, was created and then fed into a dropout layer and a fully connected layer. Finally, the weights of the trained model were used to predict medical equipment demand and outbreak spreading in the following days. In experimental studies, 77-day COVID-19 data collected from the statistics data put together in Turkey were used. In order to test the proposed system, the data belonging to last 9 days of this data set were used and the performance of the proposed system was calculated using statistical algorithms, MAPE and
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