Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.
Vivian SalamaLaia Humbert-VidanBrandon GodinichKareem A WahidDina M ElHabashyMohammed A NaserRenjie HeAbdallah Sherif Radwan MohamedAriana J SahliKatherine A HutchesonGary Brandon GunnDavid I RosenthalClifton David FullerAmy C MorenoPublished in: medRxiv : the preprint server for health sciences (2024)
These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.
Keyphrases
- pain management
- chronic pain
- radiation therapy
- patients undergoing
- liver failure
- clinical practice
- primary care
- respiratory failure
- healthcare
- neuropathic pain
- intensive care unit
- deep learning
- drug induced
- aortic dissection
- cross sectional
- spinal cord injury
- squamous cell carcinoma
- combination therapy
- young adults
- locally advanced