1 research outputs found
Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions
Deep learning, an area of machine learning, is set to revolutionize patient
care. But it is not yet part of standard of care, especially when it comes to
individual patient care. In fact, it is unclear to what extent data-driven
techniques are being used to support clinical decision making (CDS).
Heretofore, there has not been a review of ways in which research in machine
learning and other types of data-driven techniques can contribute effectively
to clinical care and the types of support they can bring to clinicians. In this
paper, we consider ways in which two data driven domains - machine learning and
data visualizations - can contribute to the next generation of clinical
decision support systems. We review the literature regarding the ways heuristic
knowledge, machine learning, and visualization are - and can be - applied to
three types of CDS. There has been substantial research into the use of
predictive modeling for alerts, however current CDS systems are not utilizing
these methods. Approaches that leverage interactive visualizations and
machine-learning inferences to organize and review patient data are gaining
popularity but are still at the prototype stage and are not yet in use. CDS
systems that could benefit from prescriptive machine learning (e.g., treatment
recommendations for specific patients) have not yet been developed. We discuss
potential reasons for the lack of deployment of data-driven methods in CDS and
directions for future research