Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19 - Ramanna, Sheela; Ashrafi, Negin; Loster, Evan; Debroni, Karen; Turner, Shelley (Frontiers in Artificial Intelligence, 2023)
COVID-19 is an unprecedented health crisis causing a great deal of stress and mental health challenges for populations in Canada. Recently, research is emerging highlighting the potential of cannabinoids' beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of medicinal cannabinoid since COVID-19 was declared a pandemic. Furthermore, evidence points to a correlation between mental health and sleep patterns. The objective of this research is threefold: i) to evaluate the relationship of the clinical delivery of cannabinoid medicine for anxiety, depression and sleep scores by utilizing machine learning; ii) to discover patterns based on patient features such as specific cannabinoid recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (GAD-7, PHQ-9 and PSQI) scores over a period of time (including during the COVID timeline); and iii) to predict whether new patients could potentially experience either an increase or decrease in clinical assessment tool scores. The dataset for this study was derived from patient visits to Ekosi Health Centres in Manitoba and Ontario, Canada from January, 2019 to April, 2021. Extensive pre-processing and feature engineering was performed on the dataset. To determine the outcome of a patient’s treatment, a class feature indicative of their progress or lack thereof due to the treatment received was introduced. Six rough and fuzzy classification experiments were conducted using a 10-fold CV method. The highest overall accuracy of 99.34% was obtained using the rule-based rough-set learning method. Sensitivity and specificity measures were also over 99% using the rule-based rough-set learning method.