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Synthetic Patient Perspective Data for the Curation and Evaluation of Rare Disease Patient-Facing Technology
,
Tom Owen ,
Matt Roach ,
Alan Dix
Lecture Notes in Computer Science, Volume: 14976, Pages: 330 - 343
Swansea University Authors:
Tom Owen , Matt Roach
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Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy
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DOI (Published version): 10.1007/978-3-031-67285-9_24
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...
| Published in: | Lecture Notes in Computer Science |
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| ISBN: | 9783031672842 9783031672859 |
| ISSN: | 0302-9743 1611-3349 |
| Published: |
Cham
Springer Nature Switzerland
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa67635 |
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| 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 |
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| 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 |