SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes

ACL ARR 2025 May Submission5837 Authors

Abstract: We present SMARTMiner, a framework for extracting and evaluating SMART (Specific, Measurable, Attainable, Relevant, Time-bound) goals from unstructured health coaching (HC) notes. Developed in response to challenges observed during a clinical trial, the SMARTMiner achieves two tasks: (i) extracting behavior-change goal spans, and (ii) categorizing their SMARTness. We introduce SMARTSpan, the first publicly available dataset of 173 HC notes annotated with 266 goals and SMART attributes. SMARTMiner incorporates an extractive goal retriever with a component-wise SMART classifier. Experiment results show that extractive models significantly outperformed their generative counterparts in low-resource settings, and that two-stage fine-tuning substantially boosted performance. The classifier achieved up to 0.91 SMART F1 score, while the full SMARTMiner maintained high end-to-end accuracy. This work bridges healthcare, behavioral science, and natural language processing to support health coaches and clients with structured goal tracking--paving way for automated weekly goal reviews between human-led HC sessions. Code and the dataset will be released upon acceptance.

Paper Type: Long

Research Area: Special Theme (conference specific)

Research Area Keywords: event extraction, document-level extraction, open information extraction, data-efficient training, healthcare applications, clinical NLP, corpus creation

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English

Submission Number: 5837