  Clinicians prescribe antibiotics by looking at the patient's health record
with an experienced eye. However, the therapy might be rendered futile if the
patient has drug resistance. Determining drug resistance requires
time-consuming laboratory-level testing while applying clinicians' heuristics
in an automated way is difficult due to the categorical or binary medical
events that constitute health records. In this paper, we propose a novel
framework for rapid clinical intervention by viewing health records as graphs
whose nodes are mapped from medical events and edges as correspondence between
events in given a time window. A novel graph-based model is then proposed to
extract informative features and yield automated drug resistance analysis from
those high-dimensional and scarce graphs. The proposed method integrates
multi-task learning into a common feature extracting graph encoder for
simultaneous analyses of multiple drugs as well as stabilizing learning. On a
massive dataset comprising over 110,000 patients with urinary tract infections,
we verify the proposed method is capable of attaining superior performance on
the drug resistance prediction problem. Furthermore, automated drug
recommendations resemblant to laboratory-level testing can also be made based
on the model resistance analysis.
