  With increasing and extensive use of electronic health records, clinicians
are often under time pressure when they need to retrieve important information
efficiently among large amounts of patients' health records in clinics. While a
search function can be a useful alternative to browsing through a patient's
record, it is cumbersome for clinicians to search repeatedly for the same or
similar information on similar patients. Under such circumstances, there is a
critical need to build effective recommender systems that can generate accurate
search term recommendations for clinicians. In this manuscript, we developed a
hybrid collaborative filtering model using patients' encounter and search term
information to recommend the next search terms for clinicians to retrieve
important information fast in clinics. For each patient, the model will
recommend terms that either have high co-occurrence frequencies with his/her
most recent ICD codes or are highly relevant to the most recent search terms on
this patient. We have conducted comprehensive experiments to evaluate the
proposed model, and the experimental results demonstrate that our model can
outperform all the state-of-the-art baseline methods for top-N search term
recommendation on different datasets.
