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Intelligent Palliative Care Based on Patient-Reported Outcome Measures
Margaret H Sandham1, Emma A Hedgecock2, Richard J Siegert1
1School of Clinical Sciences (M.S., R.S.), Auckland University of Technology, Auckland, New Zealand.
Journal of Pain and Symptom Management
|January 13, 2022
Summary
Machine learning and network analysis identified key symptoms in palliative care, helping to detect patient illness phases. This can advance digital therapeutics for continuous monitoring.
Area of Science:
- Palliative Care Medicine
- Machine Learning Applications
- Network Analysis in Healthcare
Background:
- Patient-reported outcome measures (PROMs) in palliative care are growing, offering opportunities for data-driven insights.
- Machine learning (ML) can analyze complex PROM data to identify patterns related to illness progression.
- Understanding patient status transitions is crucial for timely and effective palliative care.
Purpose of the Study:
- To investigate the utility of ML and network analysis in identifying palliative care patient phases using symptom data.
- To explore symptom patterns and their associations with different phases of illness.
- To identify key variables predictive of phase transitions in palliative care.
Main Methods:
- An observational study using a combination of cross-sectional and longitudinal data from 804 adult patients.
- Utilized the Australasian Karnofsky Performance Scale (AKPS), Integrated Palliative Care Outcome Scale (IPOS), and Phase of Illness (POI) data.
- Applied statistical, machine learning (six techniques), and network analysis to patient records (n=1507).
Main Results:
- Integrated Palliative Care Outcome Scale (IPOS) data exhibited significant variation across different illness phases.
- Network analysis revealed distinct symptom associations within each phase.
- Six ML techniques highlighted crucial variables for predicting transitions between illness phases. Poor Appetite and Loss of Energy emerged as central symptoms.
Conclusions:
- Preliminary findings suggest ML and network analysis can effectively delineate palliative care patient phases.
- Identified central symptoms (Poor Appetite, Loss of Energy) and their related symptom clusters.
- Results support the integration of digital therapeutics and continuous monitoring in palliative care, leveraging technological advancements.
Keywords:
Australasian Karnofsky Performance Scale AKPSIntegrated Palliative Outcome Scale IPOSMachine learningPhase of Illness POInetwork analysispalliative carepsychometricswearable electronic devicesMore Related Videos
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