Opportunities and challenges in healthcare with the management of big biomedical data

It is a challenge to develop supervised models in clinical evaluation. Although the profundities of this challenge are frequently learned at that point, they failed to remember or willfully overlooked. This should be the situation, since dwelling too long on this limitation may bring about a skeptical viewpoint. Disregarding this challenge, we keep on using supervised model learning algorithms, and they perform better in practice. The timely translation of the machine learning model in clinical practice needs to be validated and accurately measured and the models with benefits for everyone are a big challenge. Number of factors, including the size of biomedical datasets and model fitting issues, with unbalanced datasets create unnecessary biases in medical practice. Therefore, robust clinical evaluation using simple datasets helps in simple model learning and also understands how much data is precisely needed to do performance calculations. In this chapter, the author explores the opportunities and medical challenges associated with large datasets in deep learning, including data streaming, model scalability, and distributed computing.