Novel method for predicting nonvisible symptoms using machine learning in cancer palliative care

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

BACKGROUND/OBJECTIVES

Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

METHODS

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

RESULTS

Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).

CONCLUSION

Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Data - Knowledge driven machine learning model for cancer pain medication decisions

BACKGROUND

Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. This study aims to develop machine learning models using real-world clinical data and prior knowledge to support drug decision-making in cancer pain management.

METHODS

Clinical records from the Xiangya Hospital information system and a specialized cancer pain platform were used to develop two machine learning models: one for patients newly experiencing pain and one for patients with inadequate pain control. A total of 10,317 clinical records were used for model training, and 1,000 external records were obtained from the Cancer Hospital of the Chinese Academy of Medical Sciences for validation. Model performance was evaluated based on accuracy, AUC, and brier score.

RESULTS

Decision Tree and Gradient Boosting algorithms were selected for the two models, achieving an average accuracy of 98.47% and 94.74%, respectively, with AUCs of 99.62% and 94.74%. External validation accuracy was 97.4% and 93.1%, respectively, with AUCs of 99.83% and 97.01%.

CONCLUSION

The models proposed in this study can serve as decision support tools for healthcare professionals, assisting physicians in making optimized medication decisions in the absence of pharmacists.

Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy

CONTEXT

Early palliative care is recommended within eight-week of diagnosing advanced cancer. Although guidelines suggest routine screening to identify cancer patients who could benefit from palliative care, implementing screening can be challenging due to understaffing and time constraints.

OBJECTIVES

To develop and evaluate machine learning models for predicting specialist palliative care needs in advanced cancer patients undergoing chemotherapy, and to investigate if predictive models could substitute screening tools.

METHODS

We conducted a retrospective cohort study using supervised machine learning. The study included patients aged 18 or older, diagnosed with metastatic or stage IV cancer, who underwent chemotherapy and distress screening at a designated cancer hospital in Japan from April 1, 2018, to March 31, 2023. Specialist palliative care needs were assessed based on distress screening scores and expert evaluations. Data sources were hospital's cancer registry, health claims database, and nursing admission records. The predictive model was developed using XGBoost, a machine learning algorithm.

RESULTS

Out of the 1878 included patients, 561 were analyzed. Among them, 114 (20.3%) exhibited needs for specialist palliative care. After under-sampling to address data imbalance, the models achieved an Area Under the Curve (AUC) of 0.89 with 95.8% sensitivity and a specificity of 71.9%. After feature selection, the model retained five variables, including the patient-reported pain score, and showcased an 0.82 AUC.

CONCLUSION

Our models could forecast specialist palliative care needs for advanced cancer patients on chemotherapy. Using five variables as predictors could replace screening tools and has the potential to contribute to earlier palliative care.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Background/objective

Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.

Results

This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).

Conclusion

Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.