Optimizing Telehealth-Based Cancer Pain Management through Machine Learning

Background. Although telehealth strategies can be effectively used for managing cancer pain, identifying the best care pathway for tailoring interventions and allocating resources remains difficult. Artificial intelligence and machine learning (ML) may help clinicians develop more accurate strategies for predicting whether patients need remote consultations or in-person evaluations. Methods. Data from two cohorts of cancer pain patients were analyzed. Variables included sociodemographic and clinical data, including age, sex, ECOG performance status, metastases, bone metastases, pain type, breakthrough cancer pain (BTCP), and rapid onset opioids (ROOs) therapy. The main outcome was the number of televisits (one versus multiple). After harmonizing the dataset, categorical variables were one-hot encoded, and age was standardized. Six models were tested: logistic regression, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron (MLP). Training and tuning used a 7-repeated 5-fold cross-validation approach. Performance was evaluated on a hold-out test set using F1-score, accuracy, and AUC-ROC. A sensitivity analysis with two scenarios was performed to verify the effects of class weighting and excluding the cohort variable. Results. The final dataset included 270 patients. No variable was significantly linked to the number of televisits. F1-scores across models ranged from 0.33 (RF) to 0.65 (MLP), accuracy from 0.45 (RF) to 0.55 (SVM), and AUC-ROC from 0.43 (RF) to 0.65 (LR). DeLong tests showed no significant differences between algorithms (p > 0.05). While the MLP achieved the highest F1-score, it showed instability with 91% null F1-scores. Incorporating class weights slightly improved SVM (F1 = 0.58; AUC = 0.62) and LR (F1 = 0.53; AUC = 0.63), though not significantly. Removing the cohort variable reduced training time by about one hour and yielded similar results. Conclusion. Although no model demonstrated strong predictive power, this ML-based framework shows the potential of using structured telemedicine data to model clinical workload and optimize follow-up strategies in cancer pain care. Further studies with feature-rich datasets are needed to improve clinical usefulness.