Background: Although telehealth strategies can be effectively adopted to manage cancer pain, identifying the optimal 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). For preprocessing, datasets from the two cohorts were harmonized by aligning variable definitions, coding schemes, and data formats. Six models were tested: logistic regression, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), k-nearest neighbors (KNNs), 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 statistically significant associations were identified between the available variables and 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). Although the MLP achieved the highest F1-score, it exhibited instability, with 91% of null F1-scores. Incorporating class weights slightly improved SVM (F1 = 0.58 and AUC = 0.62) and LR (F1 = 0.53 and AUC = 0.63) though not significantly. 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. Trial registration: ClinicalTrials.gov identifier: NCT04726228 and NCT07038434.
Feasibility Assessment of Telehealth‐Based Cancer Pain Management Through Machine Learning: A Prospective Clinical Study
Bruno, Maria Pia;Sabbatino, Francesco;Franci, Gianluigi;Cascella, Marco
2026
Abstract
Background: Although telehealth strategies can be effectively adopted to manage cancer pain, identifying the optimal 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). For preprocessing, datasets from the two cohorts were harmonized by aligning variable definitions, coding schemes, and data formats. Six models were tested: logistic regression, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), k-nearest neighbors (KNNs), 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 statistically significant associations were identified between the available variables and 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). Although the MLP achieved the highest F1-score, it exhibited instability, with 91% of null F1-scores. Incorporating class weights slightly improved SVM (F1 = 0.58 and AUC = 0.62) and LR (F1 = 0.53 and AUC = 0.63) though not significantly. 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. Trial registration: ClinicalTrials.gov identifier: NCT04726228 and NCT07038434.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
