A Textual Recommender System for Clinical Data

When faced with an exceptional clinical case, doctors like to review information about similar patients to guide their decision-making. Retrieving relevant cases, however, is a hard and time-consuming task: Hospital databases of free-text physician letters provide a rich resource of information but are usually only searchable with string-matching methods. Here, we present a recommender system that automatically finds physician letters similar to a specified reference letter using an information retrieval procedure. We use a small-scale, prototypical dataset to compare the system’s recommendations with physicians’ similarity judgments of letter pairs in a psychological experiment. The results show that the recommender system captures expert intuitions about letter similarity well and is usable for practical applications