Text summarisation in healthcare to reduce workload

Summarising patient experiences for healthcare professionals

Master thesis (2024)

Authors

J.M. Dannenberg Electrical Engineering, Mathematics and Computer Science

Contributors

Jiwon Jung DesIgning Value in Ecosystems (graduation committee member)
C. Lofi Web Information Systems - EEMCS (graduation committee member)
J. Yang Web Information Systems - EEMCS (mentor)
N. Yorke-Smith Algorithmics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
Noise Text summarisation Healthcare professionals Patient interactions
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Published Date

03-07-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Summarising patient interactions creates a huge workload for the healthcare professionals. This research finds that patient interactions contain a lot of noise that is subjective of nature. To explore the problem area interviews with a summarisation prototype have been conducted to extract system requirements and validate those by implementing them in the prototype. Filtering noise, reinforcement learning and numerical factual correctness are of paramount importance to a successful summarisation system.