Leveraging natural language processing and machine learning to characterize psychological stress and life meaning and purpose in pediatric cancer survivors: a preliminary validation study.
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OBJECTIVE: To determine if natural language processing (NLP) and machine learning (ML) techniques accurately identify interview-based psychological stress and meaning/purpose data in child/adolescent cancer survivors. MATERIALS AND METHODS: Interviews were conducted with 51 survivors (aged 8-17.9 years; ≥5-years post-therapy) from St Jude Children's Research Hospital. Two content experts coded 244 and 513 semantic units, focusing on attributes of psychological stress (anger, controllability/manageability, fear/anxiety) and attributes of meaning/purpose (goal, optimism, purpose). Content experts extracted specific attributes from the interviews, which were designated as the gold standard. Two NLP/ML methods, Word2Vec with Extreme Gradient Boosting (XGBoost), and Bidirectional Encoder Representations from Transformers Large (BERTLarge), were validated using accuracy, areas under the receiver operating characteristic curves (AUROCC), and under the precision-recall curves (AUPRC). RESULTS: BERTLarge demonstrated higher accuracy, AUROCC, and AUPRC in identifying all attributes of psychological stress and meaning/purpose versus Word2Vec/XGBoost. BERTLarge significantly outperformed Word2Vec/XGBoost in characterizing all attributes (P <.05) except for the purpose attribute of meaning/purpose. DISCUSSION: These findings suggest that AI tools can help healthcare providers efficiently assess emotional well-being of childhood cancer survivors, supporting future clinical interventions. CONCLUSIONS: NLP/ML effectively identifies interview-based data for child/adolescent cancer survivors.