Machine learning to identify changes in symptoms and phase of illness
24th May 2022
The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness.
A study being carried out in New Zealand is exploring whether machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS).
804 adult patients enrolled in a New Zealand palliative care service were analysed using a combination of statistical, machine learning and network analysis techniques. Data from IPOS showed considerable variation with phase. Six machine-learning techniques identified the most important variables for predicting possible transition between phases of illness. Poor Appetite and Loss of Energy were central IPOS items, with Loss of Energy linked to Drowsiness, Shortness of Breath and Lack of Mobility on the one hand, and Poor Appetite linked to Nausea, Vomiting, Constipation and Sore and Dry Mouth on the other.
These preliminary results, when coupled with the latest technological developments in mobile apps and wearable technology, could point the way to increased use of digital therapeutics in continuous palliative care monitoring.
Sandham MH, Hedgecock EA, Siegert RJ, Narayanan A, Hocaoglu MB, Higginson IJ. Intelligent Palliative Care Based on Patient-Reported Outcome Measures. Journal of pain and symptom management. 2022 May;63(5):747-757. https://doi.org/10.1016/j.jpainsymman.2021.11.008