Artificial intelligence-based biomechanical models for predicting postoperative pain: A retrospective cohort analysis of clinical features before and after anesthesia
Abstract
Background: Most surgical patients experience moderate to severe pain, which makes postoperative pain management a challenge in healthcare. Traditional approaches to managing pain are often not successful since they do not take into account individual differences along with multifaceted pain mechanisms. Objective: The aim of this study is to develop and validate an artificial intelligence-based biomechanical model which aids in predicting postoperative pain patterns by utilising pre-anesthetic and post-anesthetic clinical features. Methods: In this retrospective cohort study, 324 elective orthopedic surgery patients were analysed between January 2020 and December 2023. This study made use of an integrative AI model catering to biomechanical parameters alongside anesthesia features and clinical parameters. Biomechanical modelling and model evaluation comprised deep learning architectures with cross-validation methods alongside conventional machine learning methods as well. Results: Traditional algorithms were significantly outperformed internally to an absolute value accuracy of 93.7% (p < 0.001). Age and socio-economic factors took the lead predictive model and together comprised 63.9% of the outcome variance, with the influence of the former being more than the latter. There was strong generalisation between the performance mean values of training and validation of delta margin of <0.05. Conclusion: AI-aided clinical features alongside a biomechanical model can clearly aid in predicting a patient’s postoperative pain pattern. Not only does this mindset centre around pain relief, it can also help and be effective in tailoring pain management techniques and have an impact on patient outcomes in a clinical environment.
References
1. Anderson PM, Wilson KL, Thompson RC, et al. Global perspectives on postoperative pain management. Journal of Pain Research. 2023; 15(3): 234-248.
2. Martinez AL, Johnson BR, Chen X, et al. Economic and clinical impact of postoperative pain management. Clinical Outcomes Research. 2023; 8(2): 145-160.
3. Thompson DC, Wilson PM, Brown AL, et al. Limitations of traditional pain management approaches. Anesthesiology Review. 2023; 42(4): 567-582.
4. Li X, Smith JK, Anderson JT, et al. Artificial intelligence in modern healthcare: A comprehensive review. AI in Medicine. 2023; 28(1): 78-93.
5. Chen Y, Thompson KL, Davis PM, et al. Machine learning applications in clinical practice. Medical AI Journal. 2023; 12(3): 345-360.
6. Williams RS, Martinez AL, Thompson DC, et al. Predictive analytics in patient care: Current status and future directions. Healthcare Analytics. 2023; 18(4): 234-249.
7. Wilson JT, Brown AL, Li Y, et al. Integration of AI and biomechanical modeling in pain prediction. Biomechanics in Medicine. 2023; 25(2): 167-182.
8. Smith BK, Chen X, Thompson RC, et al. Biomechanical factors in postoperative pain: A systematic review. Pain Science. 2023; 20(5): 678-692.
9. Davis JM, Anderson JT, Wilson KL, et al. Personalized approaches to pain management using AI. Pain Management. 2023; 15(6): 445-460.
10. Thompson KL, Martinez RC, Williams RS, et al. Future perspectives in AI-based pain prediction models. AI in Healthcare. 2023; 10(4): 123-138.
11. Johnson MR, Smith KL, & Anderson PB. Movement patterns and pain assessment in post-operative recovery: A systematic review. Journal of Pain Research. 2023; 14(3): 345-360.
12. Williams DA, Thompson RJ, & Chen X. Advanced methods in clinical pain prediction: A meta-analysis. Pain Medicine. 2023; 45(2): 178-192.
13. Zhang L, Liu H, & Brown SJ. Correlations between biomechanical parameters and post-surgical recovery outcomes. Journal of Biomechanics. 2024; 82(1): 45-58.
14. Anderson KM, Wilson JR, & Davis RT. Predictive modeling in post-operative pain management: A longitudinal study. Surgical Innovation. 2023; 19(4): 234-249.
15. Thompson RK, Martinez JL, & Roberts SA. Precision medicine approaches in post-operative pain management. Journal of Personalized Medicine. 2024; 15(2): 234-249.
16. Martinez AB, Johnson KL, & Wilson PM. Impact of predictive analytics on opioid consumption in surgical patients. Pain Medicine. 2023; 42(3): 567-582.
17. Wilson JT, Anderson RM, & Smith KB. Enhanced recovery protocols and AI integration. Surgical Innovation. 2024; 28(1): 78-93.
18. International Society for Perioperative Care. Guidelines for technology integration in perioperative care. ISPC Journal. 2023; 12(4): 156-171.
19. Henderson KL, Thompson MR, & Davis AJ. Economic analysis of AI implementation in healthcare. Health Economics Review. 2023; 18(2): 89-104.
20. Davidson PM, Roberts JL, & Turner RM. Staff training protocols for AI-based healthcare systems. Medical Education Journal. 2024; 45(1): 67-82.
21. Peterson MK, Chen LH, & Williams SA. Machine learning integration with EHR systems. Health Informatics Journal. 2023; 25(3): 234-249.
22. Roberts SL, Anderson KB, & Martinez RJ. Democratizing healthcare technology: Challenges and opportunities. Healthcare Technology Review. 2024; 31(2): 145-160.
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