Hummel, P. A. ; Jäkel, F. ; Lange, S. ; Mertelsmann, R.
Hrsg.: Cox, M. ; Funk, P. ; Begum, S. (2018)
A Textual Recommender System for Clinical Data.
International Conference on Case-Based Reasoning 2018.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Herausgeber: | Cox, M. ; Funk, P. ; Begum, S. |
| Autor(en): | Hummel, P. A. ; Jäkel, F. ; Lange, S. ; Mertelsmann, R. |
| Art des Eintrags: | Bibliographie |
| Titel: | A Textual Recommender System for Clinical Data |
| Sprache: | Englisch |
| Publikationsjahr: | 15 Oktober 2018 |
| Ort: | Sitockholm (Sweden) |
| (Heft-)Nummer: | 11156 |
| Reihe: | Lecture Notes in Computer Science |
| Veranstaltungstitel: | International Conference on Case-Based Reasoning 2018 |
| URL / URN: | https://link.springer.com/chapter/10.1007/978-3-030-0108-2_1... |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition |
| Hinterlegungsdatum: | 22 Okt 2018 10:35 |
| Letzte Änderung: | 08 Okt 2020 12:50 |
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