Proceedings of the ... Workshop on Patient-Oriented Language Processing最新文献

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Reasoning, Contrastive, and In-Context Strategies for Opioid Use Stage Detection on Social Media. 社交媒体上阿片类药物使用阶段检测的推理，对比和情境策略。

Proceedings of the ... Workshop on Patient-Oriented Language Processing Pub Date : 2026-05-01 DOI: 10.63317/4eyh8vc3atun

Vinu Ekanayake, Ramakanth Kavuluru

{"title":"Reasoning, Contrastive, and In-Context Strategies for Opioid Use Stage Detection on Social Media.","authors":"Vinu Ekanayake, Ramakanth Kavuluru","doi":"10.63317/4eyh8vc3atun","DOIUrl":"10.63317/4eyh8vc3atun","url":null,"abstract":"The opioid epidemic has ravaged the US for the past two decades and is still a persistent threat. During the same time, the increasing use of social media has created a new avenue for people to share their journeys regarding opioid use. In this context, research in automatically determining opioid use stages (e.g., misuse, addiction, recovery) based on self disclosures in social media posts is gaining traction. In this paper, using a recent benchmark, we assess different supervised strategies for identifying self-disclosed opioid use stages from Reddit posts. We consider distilled reasoning traces from DeepSeek R1 (an open weights reasoning model), supervised contrastive learning (SCL), and few-shot in-context learning (ICL) with GPT-5 to conduct a variety of experiments with encoder and encoder-decoder models. We also conduct direct zero-shot (ZS) experiments with GPT 5 and GPT 5.2. Across different models and datasets, our strategies provide improvements in performance with some nuances that are too subtle to elaborate in the abstract. A surprising finding is that ZS results with GPT-5 are better than all supervised results, which ushers a new frontier for LLM-based classification of opioid use in social media posts. Our code is available for reuse and replication: https://github.com/bionlproc/Opioid-Stage.","PeriodicalId":524199,"journal":{"name":"Proceedings of the ... Workshop on Patient-Oriented Language Processing","volume":"2026 ","pages":"207-222"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13648211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mining Social Media for Barriers to Opioid Recovery with LLMs. 与法学硕士一起挖掘社交媒体对阿片类药物恢复的障碍。

Proceedings of the ... Workshop on Patient-Oriented Language Processing Pub Date : 2025-05-01 DOI: 10.18653/v1/2025.cl4health-1.7

Vinu H Ekanayake, Md Sultan Al Nahian, Ramakanth Kavuluru

{"title":"Mining Social Media for Barriers to Opioid Recovery with LLMs.","authors":"Vinu H Ekanayake, Md Sultan Al Nahian, Ramakanth Kavuluru","doi":"10.18653/v1/2025.cl4health-1.7","DOIUrl":"10.18653/v1/2025.cl4health-1.7","url":null,"abstract":"Opioid abuse and addiction remain a major public health challenge in the US. At a broad level, barriers to recovery often take the form of individual, social, and structural issues. However, it is crucial to know the specific barriers patients face to help design better treatment interventions and healthcare policies. Researchers typically discover barriers through focus groups and surveys. While scientists can exercise better control over these strategies, such methods are both expensive and time consuming, needing repeated studies across time as new barriers emerge. We believe, this traditional approach can be complemented by automatically mining social media to determine high-level trends in both well-known and emerging barriers. In this paper, we report on such an effort by mining messages from the r/OpiatesRecovery subreddit to extract, classify, and examine barriers to opioid recovery, with special attention to the COVID-19 pandemic's impact. Our methods involve multi-stage prompting to arrive at barriers from each post and map them to existing barriers or identify new ones. The new barriers are refined into coherent categories using embedding-based similarity measures and hierarchical clustering. Temporal analysis shows that some stigma-related barriers declined (relative to pre-pandemic), whereas systemic obstacles-such as treatment discontinuity and exclusionary practices-rose significantly during the pandemic. Our method is general enough to be applied to barrier extraction for other substance abuse scenarios (e.g., alcohol or stimulants).","PeriodicalId":524199,"journal":{"name":"Proceedings of the ... Workshop on Patient-Oriented Language Processing","volume":"2025 ","pages":"83-99"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13570455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Capturing Patients' Lived Experiences with Chronic Pain through Motivational Interviewing and Information Extraction. 通过动机性访谈和信息提取捕捉慢性疼痛患者的生活经历。

Proceedings of the ... Workshop on Patient-Oriented Language Processing Pub Date : 2025-05-01 DOI: 10.18653/v1/2025.cl4health-1.28

Hadeel Elyazori, Rusul Abdulrazzaq, Hana Al Shawi, Isaac Paki Elom Amouzou, Patrick King, Syleah Manns, Mahdia Popal, Zarna Narsihbhai Patel, Secili DeStefano, Jay Shah, Lynn H Gerber, Siddhartha Sikdar, Seiyon Lee, Samuel Acuña, Kevin Lybarger

{"title":"Capturing Patients' Lived Experiences with Chronic Pain through Motivational Interviewing and Information Extraction.","authors":"Hadeel Elyazori, Rusul Abdulrazzaq, Hana Al Shawi, Isaac Paki Elom Amouzou, Patrick King, Syleah Manns, Mahdia Popal, Zarna Narsihbhai Patel, Secili DeStefano, Jay Shah, Lynn H Gerber, Siddhartha Sikdar, Seiyon Lee, Samuel Acuña, Kevin Lybarger","doi":"10.18653/v1/2025.cl4health-1.28","DOIUrl":"https://doi.org/10.18653/v1/2025.cl4health-1.28","url":null,"abstract":"Chronic pain affects millions, yet traditional assessments often fail to capture patients' lived experiences comprehensively. In this study, we used a Motivational Interviewing framework to conduct semi-structured interviews with eleven adults experiencing chronic pain and then applied Natural Language Processing (NLP) to their narratives. We developed an annotation schema that integrates the International Classification of Functioning, Disability, and Health (ICF) with Aspect-Based Sentiment Analysis (ABSA) to convert unstructured narratives into structured representations of key patient experience dimensions. Furthermore, we evaluated whether Large Language Models (LLMs) can automatically extract information using this schema. Our findings advance scalable, patient-centered approaches to chronic pain assessment, paving the way for more effective, data-driven management strategies.","PeriodicalId":524199,"journal":{"name":"Proceedings of the ... Workshop on Patient-Oriented Language Processing","volume":"2025 ","pages":"321-330"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149123875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0