Federated Machine Learning in Medical Science
Vrinda Sachdeva, Gaurav Agarwal, Arun Kumar, Shailja Varshney, Dina Rajput · 2024
Many scientific and industrial domains use machine learning, including medicine. Machine learning helps medical researchers uncover health patterns, correlations, and forecasts in massive medical data sets. It might revolutionize disease diagnosis, treatment planning, medication discovery, and personalized medicine. Machine algorithms may analyze patient records, genetic data, medical imaging, and clinical trial outcomes to improve clinical decision-making and disease causes. Machine learning models may also help healthcare providers save costs, improve patient experiences, and improve results. Given these factors, we anticipate that machine learning will continue to gain importance in medical research. In this chapter, machine learning and predictive analysis are used in medical informatics. The first step is to examine predictive modeling in diagnostic medicine, including job descriptions and research difficulties. This uses the traditional supervised, unsupervised, and reinforcement learning theories. Next, we examine recent advances in semi-supervised learning, deep learning, and transfer learning. Self-supervised learning and deep neural networks in medicine dominate this chapter. Image processing uses convolutional neural networks, whereas anomaly detection and differential diagnosis use generative adversarial models. Next, we examine the most important relationships between machine learning research and healthcare analytics, focusing on diagnosis and predictive analytics. In conclusion, we link the unresolved problem of using machine learning for predictive medical informatics to current research’s impact restrictions, such as patient privacy and security. This chapter explores federated machine learning in medical science, including its possible uses, limitations, and future research and deployment in healthcare.