Identifying Clinical Sub-Groups Using Machine Learning: Functional Status, Symptom Burden, and Healthcare Utilisation From National Palliative Care Data
Battushig Migiddorj, David Currow, Marijka Batterham, Khin Than WinBackground
As the patterns of chronic complex disease and death continue to evolve, the demand for palliative care continues to increase. Understanding key patterns in the population using machine learning allows for new insights.
Aim
Using a large prospectively collected dataset on function and symptom burden to explore clinically distinct sub-groups of palliative care. These sub-groups were used to explore patterns of healthcare utilisation.
Design
The Australian national Palliative Care Outcomes Collaboration data were used for K-means clustering and multivariable regression.
Setting/participants
Adults aged ≥18 years who received palliative care and died between 1 January 2014 and 31 December 2023 (261 290 patients; 350 512 episodes of care).
Results
Three stable episode-level clinical sub-groups were identified: •
Sub-group 1
(119 901 episodes; 42.7%):
Conclusions
K-means clustering provided a concise, clinically interpretable representation of heterogeneity in specialist palliative care. Episode-level clinical sub-groups captured meaningful variation in service use while retaining much of the information contained in clinical measures, supporting prospective evaluation for service planning and care delivery.