2026· Methods in molecular biology· Vol 3058, pp.
135-156
· 0 citations
Medicine
TL;DR
This chapter aims to provide a comprehensive overview of how ML and DL can be applied to EHRs for early disease detection and prevention, and highlights methodological advances, practical applications, and two real-world case studies, while addressing the challenges that must be overcome for safe and trustworthy clinical integration.
This Review highlights AI and ML frameworks for integrating genomic, multi-omics, and EHR data, and discusses how these approaches are reshaping genomics research as well as clinical practice.
Rasika Venkatesh, M. Ritchie· Nature reviews genetics· 0 citations
This study introduces a practical and data-driven approach to capturing and predicting patient outcomes using machine learning algorithms that can assist healthcare professionals in identifying high-risk patients earlier, planning targeted interventions, reducing readmission rates, and ultimately improving overall pat...
Victor Chisom Diogu· INTERNATIONAL JOURNAL OF APP...· 0 citations
Early disease detection is not a single classification problem. It includes population screening, presymptomatic risk estimation, identification of early pathological change, and detection of disease before conventional clinical recognition. Machine learning and deep learning systems approach these targets through medi...
The research explores the transformative impact of Artificial Intelligence (AI) on healthcare, focusing on its advancements in diagnostics and disease prediction. It traces the evolution of machine learning algorithms from rule‑based systems to sophisticated deep learning techniques, such as CNN for medical imaging and...
Hanan R. Alnjar· International journal of re...· 0 citations
A simple logistic regression – with excellent recall and competitive AUC – appears preferable for population screening, where transparency, robustness to reporting noise, and ease of implementation are paramount.
Shaked Shechter, N. Shomron· Journal of Scientific Innova...· 0 citations
This comprehensive project focuses on Multi-Disease Detection, employing a diverse set of algorithms, encompassing deep learning, standard machine learning, transfer learning, and hybrid models such as VGG-19, ResNet50, Random Forest, and Gradient Boosting.
V. Rani, Ajit Kumar Rout, Padmavathi Pragada et al.· International Journal of Inn...· 0 citations
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