Abstract

Pain assessment and management are essential components of patient care, yet the relationship between physiological variables and pain levels is not fully explored. This study leverages the Medical Information Mart for Intensive Care (MIMIC-IV-ED) database to investigate the correlation between various physiological variables and pain levels using machine learning techniques. Our findings suggest that currently available aggregated data may not be sufficient to accurately predict pain levels. This highlights the critical need for incorporating raw physiological data, such as continuous waveforms, to capture the complex relationship between pain and physiological parameters. Specifically, our study emphasizes the potential value of analyzing raw heart rate variability (HRV) data, as well as detailed electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. By focusing on raw data, one can extract more nuanced features that may be lost in summarized or averaged measurements. For example, subtle changes in ECG morphology or beat-to-beat variability in PPG signals could provide valuable insights into pain levels that are not apparent in standard vital sign readings. The integration of raw physiological data in pain research is crucial to address the current scarcity of comprehensive pain studies. This approach has the potential to significantly improve pain assessment accuracy and develop more effective pain management strategies. Additionally, the analysis of raw data could lead to the discovery of novel biomarkers or patterns associated with pain, improving our understanding of pain mechanisms. While emphasizing the importance of raw data, our study also raises important ethical and philosophical questions about the use of technology in pain assessment and its potential impact on patient care, equity, and decision-making in healthcare. It explores the broader implications of advanced pain monitoring on society and individuals’ lifestyles, considering the existing bias in pain management within the medical field. In conclusion, we believe that is a need for a paradigm shift in pain research, advocating for the widespread collection and analysis of raw physiological data. This approach promises to provide a more comprehensive and accurate understanding of pain, ultimately leading to improved assessment tools and personalized pain management strategies.