AI-powered Patient Risk Stratification for Preventive Healthcare: Developing AI-powered models to stratify patient risk profiles and prioritize preventive healthcare interventions
Published 13-09-2024
Keywords
- AI,
- predictive analytics.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
In the realm of healthcare, the proactive identification of individuals at risk of developing certain conditions is crucial for effective preventive care. Artificial intelligence (AI) has emerged as a powerful tool for patient risk stratification, offering the potential to enhance personalized healthcare interventions. This paper explores the development and implementation of AI-powered models for patient risk stratification, focusing on preventive healthcare. The study aims to develop robust AI models that can effectively stratify patient risk profiles, enabling healthcare providers to prioritize and tailor preventive interventions. By leveraging diverse datasets and advanced machine learning algorithms, these models aim to enhance the accuracy and efficiency of risk stratification, ultimately leading to improved health outcomes and resource allocation in preventive healthcare.
Downloads
References
- Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
- N. Pushadapu, “AI-Driven Solutions for Seamless Integration of FHIR in Healthcare Systems: Techniques, Tools, and Best Practices ”, Journal of AI in Healthcare and Medicine, vol. 3, no. 1, pp. 234–277, Jun. 2023
- Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
- Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
- Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.