Objectives This study developed deep neural network (DNN) models capable of accurately classifying major adverse cardiac events (MACE) in patients with acute myocardial infarction (AMI) after hospital discharge, across 3 follow-up intervals: 1, 6, and 12 months. Methods: DNN models were constructed to predict post-discharge MACE across 4 categories. Multiple traditional machine learning models were implemented as controls to benchmark the performance of our DNN approach. All models were evaluated based on their ability to predict MACE occurrence during the specified follow-up periods. Results: The DNN models demonstrated superior predictive performance over conventional machine learning methods, achieving high accuracies of 0.922, 0.884, and 0.913 for the 1-month, 6-month, and 12-month follow-up periods, respectively. Conclusion: The high accuracy of our DNN models highlights their practical advantages for AMI diagnosis and guidance of follow-up treatment. These models can serve as valuable decision support tools, enabling clinicians to optimize the overall management of AMI patients and potentially enhance their hospitalization experience.
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Artificial intelligence and biohealth: the Republic of Korea’s emerging priorities in health care R&D Jong-Koo Lee Osong Public Health and Research Perspectives.2025; 16(4): 309. CrossRef
Objectives The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
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<sec>
<title>Objectives</title>
<p>This study presents the development and validation of a risk assessment program of highly pathogenic avian influenza (HPAI). This program was developed by the Korean government (Animal and Plant Quarantine Agency) and a private corporation (Korea Telecom, KT), using a national database (Korean animal health integrated system, KAHIS).</p></sec>
<sec>
<title>Methods</title>
<p>Our risk assessment program was developed using the multilayer perceptron method using R Language. HPAI outbreaks on 544 poultry farms (307 with H5N6, and 237 with H5N8) that had available visit records of livestock-related vehicles amongst the 812 HPAI outbreaks that were confirmed between January 2014 and June 2017 were involved in this study.</p></sec>
<sec>
<title>Results</title>
<p>After 140,000 iterations without drop-out, a model with 3 hidden layers and 10 nodes per layer, were selected. The activation function of the model was hyperbolic tangent. Precision and recall of the test gave F1 measures of 0.41, 0.68 and 0.51, respectively, at validation. The predicted risk values were higher for the “outbreak” (average ± SD, 0.20 ± 0.31) than “non-outbreak” (0.18 ± 0.30) farms (<italic>p</italic> < 0.001).</p></sec>
<sec>
<title>Conclusion</title>
<p>The risk assessment model developed was employed during the epidemics of 2016/2017 (pilot version) and 2017/2018 (complementary version). This risk assessment model enhanced risk management activities by enabling preemptive control measures to prevent the spread of diseases.</p></sec>
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Farm-level transmission dynamics and risk assessment of H5 Avian Influenza Juan Zhang, Jialin Wang, Xiaomeng Wei, Li Li, Ming-Tao Li, Xin Pei, Zhen Jin Chaos, Solitons & Fractals.2026; 204: 117767. CrossRef
Quantitative risk assessment of avian influenza: A scoping review Mina Khoshbazm, Kelsey Spence, Marzieh Soltani, Lauren Grant, Yan Yan, Shayan Sharif, Rozita Dara Infectious Disease Modelling.2026; 11(4): 1583. CrossRef
A risk prediction model for HPAI outbreaks in Kuwait Musab Alshatti, Ali Al-Hemoud, Ahmad Othman, Hanan Al-Khalaifah Frontiers in Veterinary Science.2026;[Epub] CrossRef
A systematic review of mathematical and machine learning models of Avian Influenza Shixun Huang, Nicola Luigi Bragazzi, Zahra Movahedi Nia, Murray Gillies, Emma Gardner, Doris Leung, Itlala Gizo, Jude D. Kong One Health.2025; 21: 101203. CrossRef
Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks Emmanuel Musa, Zahra Movahhedi Nia, Nicola Luigi Bragazzi, Doris Leung, Nelson Lee, Jude Dzevela Kong Healthcare.2024; 12(19): 1959. CrossRef
Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea Hachung Yoon, Ilseob Lee, Hyeonjeong Kang, Kyung-Sook Kim, Eunesub Lee, Mathilde Richard PLOS ONE.2022; 17(6): e0269311. CrossRef
Artificial Intelligence Models for Zoonotic Pathogens: A Survey Nisha Pillai, Mahalingam Ramkumar, Bindu Nanduri Microorganisms.2022; 10(10): 1911. CrossRef
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