Deep learning for healthcare: review, opportunities and challenges

Understanding and using complex, high-dimensional, and heterogeneous biological data remains a major obstacle in healthcare transformation. Electronic health records, imaging, -omics, sensor data, and text, all of which are
complicated, diverse, poorly annotated, and typically unstructured, have all been growing in contemporary biomedical research. Before building prediction or clustering models on top of the features, traditional data mining and statistical learning techniques frequently need feature engineering to extract useful and more robust features from the data. In the
case of complex data and insufficient domain expertise, both phases have several problems. The most recent deep
learning technology advancements provide new efficient paradigms for creating end-to-end learning models from
complex data. This post examines the most recent research on using deep learning techniques to benefit the healthcare
industry. We propose that deep learning technologies could be the means of converting large-scale biomedical data into
better human health based on the reviewed studies. We also draw attention to several drawbacks and the need for better
technique development and implementation, particularly in terms of simplicity of comprehension for subject matter
experts and citizen scientists. To connect deep learning models with human interpretability, we examine these problems and recommend creating comprehensive and meaningful interpretable architectures.