With the advent of digital technology, machine learning and deep learning in particular, is increasingly making it possible to utilize big data to more precisely risk stratify and prognosticate how an individual patient will behave based on a given a disease or intervention. Machine learning has already been used in other realms such as retail and search engines. However, healthcare has lagged in the uptake of newer techniques to leverage the rich information contained in electronic health records.
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
A study demonstrates that generating personalized and robust deep learning-based analytics for outcome prediction is feasible even with limited amounts of center-specific data. With prospective validation, the ability to preoperatively and reliably inform patients about the likelihood of symptom improvement could prove useful in patient counselling and shared decision-making 1).