  Electronic health record is an important source for clinical researches and
applications, and errors inevitably occur in the data, which could lead to
severe damages to both patients and hospital services. One of such error is the
mismatches between diagnoses and prescriptions, which we address as 'medication
anomaly' in the paper, and clinicians used to manually identify and correct
them. With the development of machine learning techniques, researchers are able
to train specific model for the task, but the process still requires expert
knowledge to construct proper features, and few semantic relations are
considered. In this paper, we propose a simple, yet effective detection method
that tackles the problem by detecting the semantic inconsistency between
diagnoses and prescriptions. Unlike traditional outlier or anomaly detection,
the scheme uses continuous bag of words to construct the semantic connection
between specific central words and their surrounding context. The detection of
medication anomaly is transformed into identifying the least possible central
word based on given context. To help distinguish the anomaly from normal
context, we also incorporate a ranking accumulation strategy. The experiments
were conducted on two real hospital electronic medical records, and the topN
accuracy of the proposed method increased by 3.91 to 10.91% and 0.68 to 2.13%
on the datasets, respectively, which is highly competitive to other traditional
machine learning-based approaches.
