Bayesian optimization for clinical pathway decomposition from aggregate data
File(s)
Accepted version
Author(s)
Plumb, William
Bottle, Robert
Casale, Giuliano
Type
Conference Paper
Abstract
Data protection rules often impose anonymization requirements on datasets by means of aggregations that hinder the exact simulation of individual subjects. For example, clinical pathways that disclose medical conditions of patients may typically need to be aggregated to preserve anonymity of the subjects. However, aggregation unavoidably results in biasing the simulation process, for example, by introducing spurious pathways that can skew the simulated trajectories. In this paper, we study this problem and develop approximate decomposition methods that mitigate its impact. Our method is shown to produce from the raw aggregates pathways with higher fidelity than sampling a Markov chain model of the aggregate data, even preserving the same length of the original pathways. In particular, we observe a relative increase in average cosine similarity of up to 52% with respect to the true pathways compared with aggregate Markov chain sampling.
Date Issued
2025-01-20
Date Acceptance
2024-06-05
Citation
2024 Winter Simulation Conference (WSC), 2025
ISBN
979-8-3315-3420-2
ISSN
1558-4305
Publisher
IEEE
Journal / Book Title
2024 Winter Simulation Conference (WSC)
Copyright Statement
© Copyright 2025 IEEE, This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
Winter Simulation Conference 2024
Publication Status
Published
Start Date
2024-12-15
Finish Date
2024-12-18
Coverage Spatial
Orlando, Florida, USA
Date Publish Online
2025-01-20