About this Research Topic
Machine learning systems using automated electronic health record data inputs have the potential to revolutionize clinical decision-support by obviating manual data entry and providing accurate risk assessments and recommendations tailored to individual patients. Several significant barriers to clinical adoption remain:
1) The use of biased or misrepresentative training data could produce erroneous model outputs,
2) Large, robust datasets are needed to allow self-learning, minimize the impact of outliers in small data, and ensure model validity and generalizability, and
3) Many patients could be harmed in a short time frame if model outputs are not carefully monitored and interpreted.
These challenges could be addressed by applying data standardization frameworks to large-scale, multi-institutional electronic health record data and actionable, interpretable machine learning models that provide outputs to clinicians’ mobile devices. The goal of the Research Topic is to assimilate evidence and perspectives from researchers and thought leaders that are pursuing the safe, effective development and clinical application of machine learning systems to augment clinical decision-making.
This Research Topic will include the following topics, addressing promises and pitfalls for each:
• Clinical Decision Analysis;
• Machine Learning Predictive Analytics for Clinical Decision-Support;
• Markov Decision Processes and Reinforcement Learning;
• Machine learning with Electronic Health Record Data;
• Mobile Device Interfaces for Clinical Decision-Support Tools;
• Clinical Implementation and Investigation of Clinical Decision-Support Tools.
Keywords: artificial intelligence, machine learning, reinforcement learning, decision analysis, decision-making
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.