@inproceedings{bojic-etal-2025-smartminer,
title = "{SMARTM}iner: Extracting and Evaluating {SMART} Goals from Low-Resource Health Coaching Notes",
author = "Bojic, Iva and
Ong, Qi Chwen and
Ma, Stephanie Hilary Xinyi and
Ai, Lin and
Liu, Zheng and
Gong, Ziwei and
Hirschberg, Julia and
Ho, Andy Hau Yan and
Khong, Andy W. H.",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.885/",
pages = "16288--16305",
ISBN = "979-8-89176-335-7",
abstract = "We present SMARTMiner, a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) 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 also 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 SMARTness 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 SMARTness 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. Both the code and the dataset are available at: https://github.com/IvaBojic/SMARTMiner."
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<abstract>We present SMARTMiner, a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) 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 also 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 SMARTness 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 SMARTness 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. Both the code and the dataset are available at: https://github.com/IvaBojic/SMARTMiner.</abstract>
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%0 Conference Proceedings
%T SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes
%A Bojic, Iva
%A Ong, Qi Chwen
%A Ma, Stephanie Hilary Xinyi
%A Ai, Lin
%A Liu, Zheng
%A Gong, Ziwei
%A Hirschberg, Julia
%A Ho, Andy Hau Yan
%A Khong, Andy W. H.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F bojic-etal-2025-smartminer
%X We present SMARTMiner, a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) 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 also 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 SMARTness 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 SMARTness 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. Both the code and the dataset are available at: https://github.com/IvaBojic/SMARTMiner.
%U https://aclanthology.org/2025.findings-emnlp.885/
%P 16288-16305
Markdown (Informal)
[SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes](https://aclanthology.org/2025.findings-emnlp.885/) (Bojic et al., Findings 2025)
ACL
- Iva Bojic, Qi Chwen Ong, Stephanie Hilary Xinyi Ma, Lin Ai, Zheng Liu, Ziwei Gong, Julia Hirschberg, Andy Hau Yan Ho, and Andy W. H. Khong. 2025. SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 16288–16305, Suzhou, China. Association for Computational Linguistics.