In this paper, we discuss our models applied to Task 4: Human Value Detection of SemEval 2023, which incorporated two different embedding techniques to interpret the data. Preliminary experiments were conducted to observe important word types. Subsequently, we explored an XGBoost model, an unsupervised learning model, and two Ensemble learning models were then explored. The best performing model, an ensemble model employing a soft voting technique, secured the 34th spot out of 39 teams, on a class imbalanced dataset. We explored the inclusion of different parts of the provided knowledge resource and found that considering only specific parts assisted our models.

Pain is the main symptom that patients present with to the emergency department (ED). Pain management, however, is often poorly done aspect of emergency care and patients with painful conditions can endure long waits before their pain is assessed or treated. To improve pain management quality, identifying whether or not an ED patient presents with pain is an important task and allows for further investigation of the quality of care provided. In this paper, machine learning was utilised to handle the task of automatically detecting patients who present at EDs with pain from retrospective data. Experimental results on a manually annotated dataset show that our proposed machine learning models achieve high performances, in which the highest accuracy and macro-averaged F1 are 91.00% and 90.96%, respectively.