No Cover Image

Book chapter 62 views 21 downloads

Synthetic Patient Perspective Data for the Curation and Evaluation of Rare Disease Patient-Facing Technology

Emily Nielsen Orcid Logo, Tom Owen Orcid Logo, Matt Roach Orcid Logo, Alan Dix Orcid Logo

Lecture Notes in Computer Science, Volume: 14976, Pages: 330 - 343

Swansea University Authors: Tom Owen Orcid Logo, Matt Roach Orcid Logo

  • AiiH_Paper.AAM.pdf

    PDF | Accepted Manuscript

    Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy

    Download (8.06MB)

Abstract

Patient-facing technology to support rare disease patients seeking diagnosis has received comparatively little focus from the literature, despite the recognition of its importance. We hypothesise that this is due to the challenges presented when designing pre-diagnostic patientfacing technology with...

Full description

Published in: Lecture Notes in Computer Science
ISBN: 9783031672842 9783031672859
ISSN: 0302-9743 1611-3349
Published: Cham Springer Nature Switzerland 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa67635
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Patient-facing technology to support rare disease patients seeking diagnosis has received comparatively little focus from the literature, despite the recognition of its importance. We hypothesise that this is due to the challenges presented when designing pre-diagnostic patientfacing technology within this area. A significant obstacle for research in this area is the lack of data which represents the patient’s perspective.Existing data typically does not present the temporal aspects of diagnosis which are crucial to evaluate the diagnosis time of technology and consists of clinical terminology which is not representative of patients. This work aims to bridge this gap by creating open-source data which: (i) utilises patient-friendly terms and (ii) facilitates the sequencing of phenotypes to temporally recreate the informational journey of a rare disease patient. Therefore, this work facilitates evaluations on whether pre-diagnostic technology reduces the time to a rare disease diagnosis, thus providing more meaningful metrics for success
Item Description: Artificial Intelligence in Healthcare, First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part II.
Keywords: Rare disease · Patient-facing technology · Diagnosis · Health · Synthetic data · Data generation
College: Faculty of Science and Engineering
Funders: The authors would like to thank Amicus Therapeutics for their support during this project. The main author is funded by the EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems (EP/S021892/1)
Start Page: 330
End Page: 343