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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. 

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe that AIoT is emerging as an essential research field at the intersection of IoT and modern AI. It is our hope that this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field.