Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

Heliyon, Journal Year: 2023, Volume and Issue: 9(2), P. e13323 - e13323

Published: Jan. 30, 2023

The use of biomarkers as early warning systems in the evaluation disease risk has increased markedly last decade. Biomarkers are indicators typical biological processes, pathogenic or pharmacological reactions to therapy. application and identification medical clinical fields have an enormous impact on society. In this review, we discuss history, various definitions, classifications, characteristics, discovery biomarkers. Furthermore, potential diagnosis, prognosis, treatment diseases over decade reviewed. present review aims inspire readers explore new avenues biomarker research development.

Language: Английский

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A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting

Mathematics, Journal Year: 2023, Volume and Issue: 11(1), P. 224 - 224

Published: Jan. 2, 2023

Accurate and real-time forecasting of the price oil plays an important role in world economy. Research interest this type time series has increased considerably recent decades, since, due to characteristics series, it was a complicated task with inaccurate results. Concretely, deep learning models such as Convolutional Neural Networks (CNNs) Recurrent (RNNs) have appeared field promising results compared traditional approaches. To improve performance existing networks forecasting, work two types neural are brought together, combining Graph Network (GCN) Bidirectional Long Short-Term Memory (BiLSTM) network. This is novel evolution that improves literature provides new possibilities analysis series. The confirm better combined BiLSTM-GCN approach BiLSTM GCN separately, well models, lower error all metrics used: Root Mean Squared Error (RMSE), (MSE), Absolute Percentage (MAPE) R-squared (R2). These represent smaller difference between result returned by model real value and, therefore, greater precision predictions model.

Language: Английский

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Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTM

The Lancet Regional Health - Americas, Journal Year: 2025, Volume and Issue: 43, P. 101010 - 101010

Published: Feb. 5, 2025

Language: Английский

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Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach

Information Processing & Management, Journal Year: 2022, Volume and Issue: 59(6), P. 103085 - 103085

Published: Sept. 8, 2022

Language: Английский

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A novel XGBoost-based featurization approach to forecast renewable energy consumption with deep learning models

Sustainable Computing Informatics and Systems, Journal Year: 2023, Volume and Issue: 38, P. 100863 - 100863

Published: March 11, 2023

Language: Английский

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Effectiveness of case-based learning in medical and pharmacy education: A meta-analysis

Electronic Journal of General Medicine, Journal Year: 2023, Volume and Issue: 20(5), P. em515 - em515

Published: May 25, 2023

Case-based learning has drawn a lot of attention in medical education because it is student-centered teaching model that exposes students to real-world situations they must answer using their reasoning abilities and prior theoretical knowledge. The purpose this meta-analysis see how successful case-based pharmacy education. For purpose, the PubMed Medline databases were searched for related research through April 2023, qualifying papers chosen thorough selection procedure based on PRISMA technique. 21 randomized controlled trials comparing other methodologies used educate found as result current search. highest percentage selected studies been conducted USA (33%) followed by China (24%). comprehensive analysis each parameter from revealed high level heterogeneity (I²=93%, p<0.00001). Between traditional learning, random effects models significant difference academic performance. when compared techniques, can increase undergraduate students’ performance well capacity analyze cases. It be concluded an active method.

Language: Английский

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Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique

Diagnostics, Journal Year: 2023, Volume and Issue: 13(11), P. 1923 - 1923

Published: May 31, 2023

In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics infected uninfected people worldwide contribute growing number publicly available datasets that can be used predict early-stage monkeypox through machine-learning models. Thus, this paper proposes a novel filtering combination technique accurate short-term forecasts cases. To end, we first filter original time series cumulative into two subseries: long-term trend residual series, using proposed one benchmark filter. Then, filtered subseries five standard learning models all their possible Hence, combine individual forecasting directly obtain final forecast newly day ahead. Four mean errors statistical test performed verify methodology's performance. experimental results show efficiency accuracy methodology. prove superiority approach, four different were included benchmarks. comparison dominance method. Finally, based on best model, achieved fourteen days (two weeks). This help understand spread lead an understanding risk, which utilized prevent further enable timely effective treatment.

Language: Английский

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Spatio-temporal prediction of deep excavation-induced ground settlement: A hybrid graphical network approach considering causality

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 146, P. 105605 - 105605

Published: Feb. 21, 2024

Language: Английский

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Projecting Road Traffic Fatalities in Australia: Insights for Targeted Safety Interventions

Injury, Journal Year: 2025, Volume and Issue: unknown, P. 112166 - 112166

Published: Jan. 1, 2025

Language: Английский

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A novel hybrid model to forecast seasonal and chaotic time series

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 239, P. 122461 - 122461

Published: Nov. 7, 2023

Language: Английский

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