  Both electronic health records and personal health records are typically
organized by data type, with medical problems, medications, procedures, and
laboratory results chronologically sorted in separate areas of the chart. As a
result, it can be difficult to find all of the relevant information for
answering a clinical question about a given medical problem. A promising
alternative is to instead organize by problems, with related medications,
procedures, and other pertinent information all grouped together. A recent
effort by Buchanan (2017) manually defined, through expert consensus, 11
medical problems and the relevant labs and medications for each. We show how to
use machine learning on electronic health records to instead automatically
construct these problem-based groupings of relevant medications, procedures,
and laboratory tests. We formulate the learning task as one of knowledge base
completion, and annotate a dataset that expands the set of problems from 11 to
32. We develop a model architecture that exploits both pre-trained concept
embeddings and usage data relating the concepts contained in a longitudinal
dataset from a large health system. We evaluate our algorithms' ability to
suggest relevant medications, procedures, and lab tests, and find that the
approach provides feasible suggestions even for problems that are hidden during
training. The dataset, along with code to reproduce our results, is available
at https://github.com/asappresearch/kbc-pomr.
