A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children
This work addresses the need for clinical decision support systems in pediatric critical care, but it is incremental as it adapts an existing framework to a specific domain.
The authors tackled the problem of automatically extracting medical policies from electronic health records for critically ill children on extracorporeal membrane oxygenation, using a boosted Statistical Relational Learning framework to learn probabilistic rules, with results showing promising consistency with medical reasoning.
The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision support systems. We adapted a boosted Statistical Relational Learning (SRL) framework to learn probabilistic rules from clinical hospital records for the management of physiologic parameters of children with severe cardiac or respiratory failure who were managed with extracorporeal membrane oxygenation. In this preliminary study, the results were promising. In particular, the algorithm returned logic rules for medical actions that are consistent with medical reasoning.