Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives
XU Duo, XU Zeshui - Artificial Intelligence in Medicine, 2024 - Elsevier
Artificial intelligence is constantly revolutionizing biomedical research and healthcare
management. Disease comorbidity is a major threat to the quality of life for susceptible …
management. Disease comorbidity is a major threat to the quality of life for susceptible …
Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida
Introduction The SARS-CoV-2 (COVID-19) pandemic has created substantial health and
economic burdens in the US and worldwide. As new variants continuously emerge …
economic burdens in the US and worldwide. As new variants continuously emerge …
Intuitionistic Fuzzy Multi-criteria Hybrid Approach for Prioritizing Seasonal Respiratory Diseases Patients within the Public Emergency Departments
A Perez-Aguilar, P Pancardo, M Ortiz-Barrios… - IEEE …, 2024 - ieeexplore.ieee.org
When several patients with Seasonal Respiratory Diseases (SRDs) arrive at Emergency
Departments (EDs) and healthcare resources are scarce, physicians need to decide which …
Departments (EDs) and healthcare resources are scarce, physicians need to decide which …
[HTML][HTML] Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida
Objective: The objective of the study was to establish an AI-driven decision support system
by identifying the most important features in the severity of disease for I ntensive C are U nit …
by identifying the most important features in the severity of disease for I ntensive C are U nit …
Comprehensive In silico and In vitro evaluation of Plectranthus amboinicus essential oil: A promising inhibitor of SARS-CoV-2 B. 1.1. 529 Omicron variant …
Existing challenges, such as the viral SARS-CoV-2 B. 1.1. 529 Omicron variants, adverse
reactions, and limited availability of drugs for COVID-19 continue to challenge global health …
reactions, and limited availability of drugs for COVID-19 continue to challenge global health …
Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic
Introduction Vaccines are the most important instrument for bringing the pandemic to a close
and saving lives and helping to reduce the risks of infection. It is important that everyone has …
and saving lives and helping to reduce the risks of infection. It is important that everyone has …
[HTML][HTML] Charting Paths to Recovery: Navigating Traumatic Brain Injury Comorbidities through Graph Theory–Exploring Benefits and Challenges
SK Sudhakar, K Mehta - Brain Organoid and Systems Neuroscience …, 2024 - Elsevier
Traumatic brain injuries (TBIs) are characterized by widespread complications that exert a
debilitating effect on the well-being of the affected individual. TBIs are associated with a …
debilitating effect on the well-being of the affected individual. TBIs are associated with a …
Machine-learning analysis of mortality due to comorbidities and resulting microvascular complications in covid patients with TYPE-2 diabetes mellitus
S Alla, N Bheesetty, Y Prakash… - Proceedings of the …, 2023 - search.proquest.com
During the COVID-19 pandemic, one of the main problems healthcare providers have faced
is the shortage of medical resources and a proper plan to distribute them efficiently. An influx …
is the shortage of medical resources and a proper plan to distribute them efficiently. An influx …
Enhancing Accuracy and Efficiency in Diabetic Retinopathy Detection: A Deep Learning Framework for Fundus Image Analysis
M Tayal, J Singh, V Kumar - International Conference on Advanced …, 2023 - Springer
The present research investigates the challenge of accurately and efficiently diagnosing
diabetic retinopathy using a deep learning architecture. Our research is based on a sizable …
diabetic retinopathy using a deep learning architecture. Our research is based on a sizable …
[HTML][HTML] Utilizing Multi-layer Perceptron for Esophageal Cancer Classification Through Machine Learning Methods
Aims This research paper aims to check the effectiveness of a variety of machine learning
models in classifying esophageal cancer through MRI scans. The current study …
models in classifying esophageal cancer through MRI scans. The current study …