Dealing with opioid addiction and its long-term consequences is of great importance, as the addiction to opioids is emerged gradually, and established strongly in a given patient's body. Based on recent research, quitting the opioid requires clinicians to arrange a gradual plan for the patients who deal with the difficulties of overcoming addiction. This, in turn, necessitates observing the patients' wellness periodically, which is conventionally made by setting clinical appointments. With the advent of wearable sensors continuous patient monitoring becomes possible. However, the data collected through the sensors is pervasively noisy, where using sensors with different sampling frequency challenges the data processing. In this work, we handle this problem by using data from cognitive tests, along with heart rate (HR) and heart rate variability (HRV). The proposed recipe enables us to interpret the data as a feature space, where we can predict the wellness of the opioid patients by employing extreme gradient boosting (XGBoost), which results in 96.12% average accuracy of prediction as the best achieved performance.
[1]
Tianqi Chen,et al.
XGBoost: A Scalable Tree Boosting System
,
2016,
KDD.
[2]
E. Mason.
Cause of Death
,
2007,
Obesity surgery.
[3]
Thomas Kubiak,et al.
The impact of heart rate variability on subjective well-being is mediated by emotion regulation
,
2010
.
[4]
Thomas Ledermann,et al.
Variability of Sleep Duration Is Related to Subjective Sleep Quality and Subjective Well-Being: An Actigraphy Study
,
2013,
PloS one.