Abstract
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.
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Notes
- 1.
e.g., Journal of Medical Cases: http://www.journalmc.org/index.php/JMC/index, British Medical Journal Case Reports: http://casereports.bmj.com/site/about/index.xhtml, American Journal of Medical Case Reports: http://www.sciepub.com/journal/AJMCR.
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Hummel, P.A., Jäkel, F., Lange, S., Mertelsmann, R. (2018). A Textual Recommender System for Clinical Data. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_10
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