[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-289748-105":3,"detail-sidebar-cat-0-en-105":73,"doc-detail-289748-en":123},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":66,"head_meta":68,"extra_data":70,"updated_unix":72},105,"en","lamar-at-archehr-qa-2025-clinically-aligned-llm-generated-few-shot-learning-for-ehr-grounded-patient-question-answering","LAMAR at ArchEHR-QA 2025 - Clinically Aligned LLM-Generated Few-Shot Learning for EHR-Grounded Patient Question Answering","","This paper presents an approach to answering patient-specific medical questions using electronic health record (EHR) grounding with ArchEHR-QA 2025 datasets. It formulates medical question answering as an alignment problem, generating responses factually consistent with patient-specific clinical notes via in-context learning. LLM-generated responses used as few-shot examples with GPT-4.1 and Gemini-2.5-Pro significantly outperform baselines, reaching strict precision, recall, and F1-micro scores of 60.6, 53.6, and 56.9 on the test leaderboard, and strong evidence-text similarity using multiple metrics.",{"@graph":14,"@context":65},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/lamar-at-archehr-qa-2025-clinically-aligned-llm-generated-few-shot-learning-for-ehr-grounded-patient-question-answering/289748/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/lamar-at-archehr-qa-2025-clinically-aligned-llm-generated-few-shot-learning-for-ehr-grounded-patient-question-answering/289748.png","ImageObject",300,407,{"name":42,"@type":43},"Skyler","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59],{"name":60,"@type":61,"acceptedAnswer":62},"What results are reported on the ArchEHR-QA 2025 test leaderboard?","Question",{"text":63,"@type":64},"The approach reports an overall score of 49.1 and strict precision, recall, and F1-micro scores of 60.6, 53.6, and 56.9, alongside evaluation using several textual similarity and evidence-related metrics.","Answer","https://schema.org",{"og:url":32,"og:type":67,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":69,"canonical":32},"index,follow",{"doc_id":71,"site_id":7},289748,1790133015,{"code":4,"msg":74,"data":75},"success",[76,80,84,88,93,98,103,107,112,115,119],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":77,"show_sort_weight":78,"slug":79},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":81,"show_sort_weight":82,"slug":83},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Exam",70,"exam",{"id":89,"doc_module":4,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},5,"Comic",60,"comic",{"id":94,"doc_module":4,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},6,"Technology",50,"technology",{"id":99,"doc_module":4,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},7,"Healthcare",40,"healthcare",{"id":104,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":105,"slug":106},8,30,"research-report",{"id":108,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},9,"Religion & Spirituality",20,"religion-spirituality",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":113,"show_sort_weight":110,"slug":114},"World Cup","world-cup",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":116,"slug":118},10,"Lifestyle","lifestyle",{"id":120,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":89,"slug":122},19,"General","general",{"code":4,"msg":74,"data":124},{"doc_id":71,"user_id":125,"nickname":42,"user_avatar":126,"doc_module":4,"category_id":104,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":127,"file_id":128,"file_url":129,"file_type":130,"file_size":131,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":104,"language":132,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":133,"faqs":134,"seo_title":135,"seo_description":12,"update_tm":136,"read_time":110,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","LAMAR at ArchEHR-QA 2025: Clinically Aligned LLM-Generated  \nFew-Shot Learning for EHR-Grounded Patient Question Answering Seksan Yoadsanit* 1,2 , Nopporn Lekuthai* 1,2 , Watcharitpol Sermsrisuwan1,2 ,  \nTitipat Achakulvisut1  \n1 Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand  \n2 Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand Correspondence: [titipat.ach@mahidol.ac.th](titipat.ach@mahidol.ac.th)  \nAbstract  \nThis paper presents an approach to answering patient-specific medical questions using electronic health record (EHR) grounding with ArchEHR-QA 2025 datasets. We address medical question answering as an alignment problem, focusing on generating responses factually consistent with patient-specific clinical notes through in-context learning techniques. We show that LLM-generated responses, used as few-shot examples with GPT- 4.1 and Gemini-2.5-Pro, significantly outperform baseline approaches (overall score = 49.1), achieving strict precision, recall, and F1-micro scores of 60 .6, 53 .6, and 56 .9, respectively, on the ArchEHR-QA 2025 test leaderboard. It achieves textual similarity between answers and essential evidence using BLEU, ROUGE, SARI, BERTScore, AlignScore, and MEDCON scores of 6.0, 32.1, 65.8, 36.4, 64.3, and 43.6, respectively. Our findings highlight the effectiveness of combining EHR grounding with few-shot examples for personalized medical question answering, establishing a promising approach for developing accurate and personalized medical question answering systems. We release our code at [https://github.com/biodatlab/archehr](https://github.com/biodatlab/archehr)qa-lamar.  \n1 Introduction  \nLarge language models (LLMs) have significantly influenced medical question-answering systems by generating clinically relevant content grounded in electronic health records (EHRs) for more personalized and context-aware patient care (Yang et al., 2022) . Clinical-related questions are among the most frequently asked topics, reflecting the public’s natural curiosity about their health literacy and the rising healthcare costs in many countries, which drive individuals to seek alternative sources of information (Savery et al., 2020) . Despite some hal-  \n*Equal Contribution  \nlucinations, recent frontier models typically maintain reasonable factual accuracy. We theorized that aligning with human expectations on answering style, citation practices, and information structuring is the main challenge. Thus, we formulate our approach to align the model response to human expectation with the limited data provided in this shared task.  \nWhile fine-tuning LLMs on medical records or textbooks can improve alignment, it demands extensive datasets, limiting scalability (Singhal et al., 2023) . Few-shot learning offers a promising alternative by guiding models with representative examples that demonstrate task-specific reasoning patterns without requiring fine-tuning, though designing optimal examples remains challenging (Brown et al., 2020) . Similarly, Retrieval-augmented generation (RAG) enables LLMs to access external knowledge sources such as structured medical databases and clinical literature, providing accurate, up-to-date answers by incorporating the medical knowledge without retraining (Alkhalaf et al., 2024 ; Lewis et al., 2020) . However, questions remain about how effectively retrieved information is integrated and grounded in the model’s final output, particularly in clinical contexts where alignment with human preferences is crucial.  \nIn this paper, we present an approach to answering patient-specific medical questions using electronic health record (EHR) grounding with the ArchEHR-QA 2025 dataset (Soni and DemnerFushman, 2025b) . We address medical question answering as an alignment problem, focusing on generating responses factually consistent with patientspecific clinical notes through in-context learning techniques. Our system","cbCair1eKLgKTxf5","https://ap.wps.com/l/cbCair1eKLgKTxf5","pdf",273092,"English","# Abstract\n# 1 Introduction\n# 2 Related work","[{\"question\":\"What results are reported on the ArchEHR-QA 2025 test leaderboard?\",\"answer\":\"The approach reports an overall score of 49.1 and strict precision, recall, and F1-micro scores of 60.6, 53.6, and 56.9, alongside evaluation using several textual similarity and evidence-related metrics.\"}]","LAMAR at ArchEHR-QA 2025 - Clinically Aligned LLM-Generated Few-Shot Learning for EHR-Grounded Patient Question Answering | PDF",1789636833]