Predicting financial toxicity in cancer patients using machine learning: a Twitter or X-based approach

Background: Financial strain resulting from cancer treatment correlates with reduced quality of life, treatment nonadherence, bankruptcy, and maladaptive behaviours. This study aims to explore the potential of a supervised machine learning algorithm in predicting financial toxicity in cancer patients based on their Tweets. Methods: A dataset of Tweets related to cancer and financial toxicity was constructed using Twitter's API. The dataset was curated, and synthetic Tweets were generated to augment the final dataset. A supervised machine learning algorithm, specifically Multinomial Naïve Bayes, was trained and tested to predict financial toxicity in cancer patients. Results: The model demonstrated high accuracy (0.97), precision (0.95), recall (0.99), specificity (0.96), F-1 score (0.97) and area-under-the-receiver-operating-characteristics (0.98) in predicting financial toxicity from Tweets. Wordcloud visualizations illustrated distinct linguistic patterns between Tweets related to financial toxicity and those unrelated to financial toxicity. The study also outlined potential proactive strategies for leveraging social media platforms like Twitter to identify and support cancer patients experiencing financial toxicity. Conclusions: This study marks the first attempt to construct a dataset of Tweets related to financial toxicity in cancer patients and to evaluate a predictive model trained on this dataset. The findings highlight the predictive capabilities of the model and its potential utility in guiding health systems and cancer center financial navigators to alleviate economic burdens associated with cancer treatment.