AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Health

We describe an AI-powered chatbot to aid with health improvement by generating Episodic Future Thinking (EFT) cue texts that should reduce delay discounting. In prior studies, EFT has been shown to address maladaptive health behaviors. Those studies involved participants, working with researchers, vividly imagining future events, and writing a description that they subsequently will frequently review, to ensure a shift from an inclination towards immediate rewards. That should promote behavior change, aiding in health tasks such as treatment adherence and lifestyle modifications. The AI chatbot is designed to guide users in generating personalized EFTs, automating the current labor-intensive interview-based process. This can enhance the efficiency of EFT interventions and make them more accessible, targeting specifically those with limited educational backgrounds or communication challenges. By leveraging AI for EFT intervention, we anticipate broadened access and improved health outcomes across diverse populations

Retrieval-based Text Selection for Addressing Class-Imbalanced Data in Classification

This paper addresses the problem of selecting of a set of texts for annotation in text classification using retrieval methods when there are limits on the number of annotations due to constraints on human resources. An additional challenge addressed is dealing with binary categories that have a small number of positive instances, reflecting severe class imbalance. In our situation, where annotation occurs over a long time period, the selection of texts to be annotated can be made in batches, with previous annotations guiding the choice of the next set. To address these challenges, the paper proposes leveraging SHAP to construct a quality set of queries for Elasticsearch and semantic search, to try to identify optimal sets of texts for annotation that will help with class imbalance. The approach is tested on sets of cue texts describing possible future events, constructed by participants involved in studies aimed to help with the management of obesity and diabetes. We introduce an effective method for selecting a small set of texts for annotation and building high-quality classifiers. We integrate vector search, semantic search, and machine learning classifiers to yield a good solution. Our experiments demonstrate improved F1 scores for the minority classes in binary classification.