Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

Area of Science:

• Anesthesiology and Pain Management
• Health Informatics
• Machine Learning Applications

Background:

• Effective postoperative pain management is crucial for patient recovery and reducing healthcare costs.
• Under-treated pain can negatively impact short-term recovery and long-term health outcomes.
• Patient-controlled analgesia (PCA) is a key method for delivering pain medication post-surgery.

Purpose of the Study:

• To develop and demonstrate machine learning and data mining techniques for predicting analgesic requirements.
• To predict patient-controlled analgesia (PCA) setting readjustment.
• To enhance postoperative pain management through data-driven insights.

Main Methods:

• Utilized decision tree-based learning algorithms to predict analgesic consumption and PCA readjustment.
• Analyzed data from 1099 patients, with each described by 280 attributes, including PCA-specific factors.
• Developed a nearest neighbor-based data cleaning method to address class imbalance in PCA readjustment prediction.

Main Results:

• Achieved 80.9% accuracy in predicting total analgesic consumption and 73.1% in predicting PCA analgesic requirement using ensemble decision trees.
• Decision tree-based learning outperformed other classifiers like ANNs and SVMs for analgesic consumption prediction.
• The proposed data cleaning method significantly improved the performance of all learning methods for PCA setting readjustment prediction.

Conclusions:

• Demonstrated a practical application of data mining in anesthesiology for optimizing PCA administration.
• The study incorporated a broader range of predictive factors, including temporal PCA demands, compared to prior research.
• Results confirm the feasibility of an ensemble machine learning approach to improve postoperative pain management via PCA.