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A Textual Recommender System for Clinical Data

Hummel, P. A. and Jäkel, F. and Lange, S. and Mertelsmann, R.
Cox, M. and Funk, P. and Begum, S. (eds.) :

A Textual Recommender System for Clinical Data.
In: International Conference on Case-Based Reasoning 2018. In: Lecture Notes in Computer Science (11156).
[Conference or Workshop Item] , (2018)

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.

Item Type: Conference or Workshop Item
Erschienen: 2018
Editors: Cox, M. and Funk, P. and Begum, S.
Creators: Hummel, P. A. and Jäkel, F. and Lange, S. and Mertelsmann, R.
Title: A Textual Recommender System for Clinical Data
Language: English
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.

Series Name: Lecture Notes in Computer Science
Number: 11156
Divisions: 03 Department Human Sciences
03 Department Human Sciences > Institute for Psychology
03 Department Human Sciences > Institute for Psychology > Models of Higher Cognition
Event Title: International Conference on Case-Based Reasoning 2018
Date Deposited: 22 Oct 2018 10:35
DOI: https://doi.org/10.1007/978-3-030-01081-2₁₀
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