9786209063152 - data-driven models for covid-19 severity analysis in comorbid patients: an ai-based clinical risk assessment approach di h s, suresh kumar; c n, pushpa (11 risultati)

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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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Da: California Books, Miami, FL, U.S.A.California Books
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condizione: new. Paperback. This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning technique…s to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naive Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Lingua: Inglese
Editore: Omniscriptum, LAP Lambert Academic Publishing, 2026
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management…. It integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naïve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. 196 pp. Englisch.

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Paperback. Condizione: new. Paperback. This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It integrates Machine Learning and Deep Learning technique…s to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naive Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It… integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naïve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 196 pp. Englisch.

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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Lingua: Inglese
Editore: Omniscriptum, LAP Lambert Academic Publishing, 2026
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a comprehensive Artificial Intelligence driven framework for predicting COVID-19 severity in patients with comorbidities, addressing critical challenges in diagnosis, prognosis, and healthcare resource management. It…integrates Machine Learning and Deep Learning techniques to analyze large-scale clinical, demographic, and medical imaging data. Imbalanced clinical datasets are handled using advanced preprocessing and resampling strategies, enabling robust prediction of mortality, survival, and disease severity. The book serves as a comprehensive guide for researchers, data scientists, and healthcare professionals interested in AI-based Prediction of COVID-19 Severity in Patients with Comorbidities. It highlights that classical Machine Learning models, including Decision Tree, Random Forest, and Gaussian Naïve Bayes, achieve high precision, while neural network-based models demonstrate strong generalization and robustness.

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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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