Anzeige
Erschienen in:
2021 | OriginalPaper | Buchkapitel
Customized Neural Predictive Medical Text: A Use-Case on Caregivers
verfasst von : John Pavlopoulos, Panagiotis Papapetrou
Erschienen in: Artificial Intelligence in Medicine
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Predictive text can speed up authoring of everyday tasks, such as writing an SMS or a URL. When deployed in a clinical setting, it can enable practitioners to compile diagnostic text reports in a speedier manner, hence allowing them to be more time-efficient when examining patients. The language used by medical practitioners when authoring clinical reports is, however, far from common, not only between practitioners but also between medical units. In this paper, we demonstrate this clinical language variation, by showing that a model trained on texts written by some physicians may not work for predicting the text of others. We use a dataset created out of the clinical notes of 17 caregivers to show that language models trained on the notes of each caregiver outperform the ones trained with texts from several ones.