METHOD: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms.
RESULTS: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs.
CONCLUSIONS: This study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients.
Aim: This study aims to study the efficacy of 5-min mindful breathing for rapid reduction of pain in a palliative care setting.
Methods: This is a sub-analysis of the previous randomized controlled study on distress reduction. Sixty patients were recruited and randomly assigned to either the intervention (5-min mindful breathing) or the control (5-min normal listening) group. Participants reported their pain on a 10-item analog scale at baseline, immediately after intervention and 10 min postintervention. Changes in pain scores were further analyzed.
Results: Pain scores decreased for both the intervention and control groups. However, the reduction of pain did not reach statistical difference in both groups (P > 0.05).
Conclusion: Five-minute mindful breathing is a quick and easy to administer therapy but does not have significant effects in terms of pain reduction in palliative settings. Future research and directions are nonetheless suggested and encouraged to look for short-term mindfulness-based therapies on pain reduction for this population.