Authors :
Soumya A.V; Archana Menon; Arathy Joshy; Devika Denson; Diya Santhosh
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/msfkzfkf
Scribd :
https://tinyurl.com/mt8nzk8r
DOI :
https://doi.org/10.5281/zenodo.14557989
Abstract :
This paper presents a survey of artificial
intelligence (AI) applications in blood testing for disease
detection and patient monitoring. It covers several
machine learning (ML) models like deep neural networks
(DNN), decision trees, and support vector machines
(SVM) used to interpret blood test results. The integration
of AI with routine blood tests promises to enhance the
diagnostic accuracy, reduce costs, and also improve
patient outcomes. This paper compares different AI
approaches in this domain, discusses the challenges and
limitations, and explores the future scope of AI in clinical
settings.AI in Healthcare, Blood Test Analysis, Machine
Learning, Disease Detection, Deep Learning, Clinical
Diagnostics.
Keywords :
AI in Healthcare, Blood Test Analysis, Machine Learning, Disease Detection, Deep Learning, Clinical Diagnostics.
References :
- Meiseles, A., et al. ”Explainable machine learning for chronic lymphocytic leukemia treatment prediction using only inexpensive tests.” Computers in Biology and Medicine, 2022.
- J1. Smith, J., & Johnson, A. ”AI in blood diagnostics: A review of applications and advancements.” Journal of Medical AI, 2021.
- Brown, C., et al. ”Machine learning applications in routine blood tests: A comprehensive review.” Clinical AI Journal, 2020.
- Turner, M., & Harris, B. ”Deep learning for blood disease detection: Challenges and opportunities.” Artificial Intelligence in Medicine, 2019.
- Clark, L., et al. ”Interpretable models for blood test analysis using SHAP.” Journal of Explainable AI, 2023
- Garcia, M. R., & Lee, A. ”Predicting disease outcomes using machine learning on blood biomarkers: A systematic review.” International Journal of Medical Informatics, 2021
- Lee, S., et al. ”AI-based hematological disease detection: A focus on leukemia using convolutional neural networks.” Journal of Biomedical Science and Engineering, 2020
- Zhao, Y., & Chen, K. ”Machine learning for sepsis prediction from blood tests in emergency departments.” Journal of Emergency Medical AI, 2021.
- Patel, R., & Kumar, N. ”The role of random forests in detecting anemia from routine blood tests.” Journal of Clinical Hematology, 2022
- Wang, X., et al. ”Blood test anomaly detection using support vector machines: A comparative study.” Computers in Healthcare, 2019
- Nguyen, T. D., & Miller, P. ”AI in blood diagnostics: From laboratory to bedside.” Artificial Intelligence in Healthcare, 2021.
- Xu, L., & Huang, J. ”Using XGBoost for early detection of chronic diseases based on blood test results.” Journal of Machine Learning in Medicine, 2022.
- Almeida, F., & Silva, R. ”Explainable AI in medical diagnostics: Case studies in blood test analysis.” AI and Health Informatics, 2020
- Kapoor, S., & Singh, M. ”The integration of artificial intelligence in routine blood tests for precision medicine.” Healthcare AI Innovations, 2023.
- Martin, P., & Wilson, J. ”Leveraging machine learning for cardiovascular risk prediction using routine blood tests.” Journal of Medical Data Science, 2022
- Anderson, C., et al. ”Interpreting AI predictions in blood diagnostics: A focus on SHAP values.” Journal of Explainable Healthcare AI, 2021
- Gupta, A., & Thomas, R. ”A comprehensive survey on deep learning applications in hematology.” Journal of AI in Medicine, 2020
- Fernandez, M., et al. ”AI-assisted blood diagnostics for early detection of infectious diseases.” Journal of Clinical AI, 2021.
- Kaur, P., & Mehta, D. ”Comparing decision trees and random forests in predicting blood-related disorders.” Journal of Healthcare Informatics Research, 2022.
- Liu, Y., & Zhang, W. ”AI-driven precision diagnostics: Applications of deep learning in routine blood tests.” Journal of Digital Medicine, 2020.
This paper presents a survey of artificial
intelligence (AI) applications in blood testing for disease
detection and patient monitoring. It covers several
machine learning (ML) models like deep neural networks
(DNN), decision trees, and support vector machines
(SVM) used to interpret blood test results. The integration
of AI with routine blood tests promises to enhance the
diagnostic accuracy, reduce costs, and also improve
patient outcomes. This paper compares different AI
approaches in this domain, discusses the challenges and
limitations, and explores the future scope of AI in clinical
settings.AI in Healthcare, Blood Test Analysis, Machine
Learning, Disease Detection, Deep Learning, Clinical
Diagnostics.
Keywords :
AI in Healthcare, Blood Test Analysis, Machine Learning, Disease Detection, Deep Learning, Clinical Diagnostics.