AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第3页

Right Patient, Right Specialist, Right Time: Retrieval Augmented Generation for Specialty Referral Routing. 正确的病人，正确的专家，正确的时间：检索增强生成专科转诊路径。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Fateme Nateghi Haredasht, Ethan Goh, Vishnu Ravi, Pooya Ashtari, Yixing Jiang, Nodir Yuldashev, François Grolleau, Robert J Gallo, Aaryan Shah, Evelyn Hur, Kanav Chopra, Olivia Jee, Julie J Lee, Leah Rosengaus, Lena Giang, Kevin Schulman, Jason Hom, Arnold Milstein, Andrew Y Ng, Jonathan H Chen

{"title":"Right Patient, Right Specialist, Right Time: Retrieval Augmented Generation for Specialty Referral Routing.","authors":"Fateme Nateghi Haredasht, Ethan Goh, Vishnu Ravi, Pooya Ashtari, Yixing Jiang, Nodir Yuldashev, François Grolleau, Robert J Gallo, Aaryan Shah, Evelyn Hur, Kanav Chopra, Olivia Jee, Julie J Lee, Leah Rosengaus, Lena Giang, Kevin Schulman, Jason Hom, Arnold Milstein, Andrew Y Ng, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"We present an embedding-based retrieval system that automatically directs physician clinical questions to the most relevant specialist-curated question template, which is necessary for the specialist to provide a clinically relevant response. The system utilizes MPNet, a transformer-based model, to generate dense vector representations of both clinical queries and 24 predefined clinical templates. Given a clinical question, the system computes cosine similarity between the query and template embeddings to retrieve the most relevant matches. When validated against real-world, retrospective eConsults across five specialties, the system accurately identified the most relevant template in 87% of cases (success@1) and included it in the top three results 99% of the time (success@3). Automating specialty selection and clinical question referrals reduces the administrative burden on physicians, minimizes care delivery delays, and improves specialist responses by providing proper context.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"443-450"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273123","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

PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping. 基于大语言模型的计算表型方法的评估框架。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Sarah A Pungitore, Shashank Yadav, Vignesh Subbian

引用次数: 0

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community. 开发以用户为中心的移动应用程序原型：将下肢骨折护理从熟练护理机构连接到社区。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Min-Jeoung Kang, Veysel Karani Baris, Alice Kim, Kumiko O Schnock, Pamela M Garabedian, Nancy K Latham, Denise Orwig, Jay Magaziner, Rodrigo Valderrábano, Ling Tang, Elizabeth Dennis, Jason Falvey, Patricia C Dykes

{"title":"Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.","authors":"Min-Jeoung Kang, Veysel Karani Baris, Alice Kim, Kumiko O Schnock, Pamela M Garabedian, Nancy K Latham, Denise Orwig, Jay Magaziner, Rodrigo Valderrábano, Ling Tang, Elizabeth Dennis, Jason Falvey, Patricia C Dykes","doi":"","DOIUrl":"","url":null,"abstract":"This study is part of the OsteoPorotic fracTure preventION System (OPTIONS) project which aims to develop an evidence-based mobile application for older adults transitioning from skilled nursing facilities (SNFs) back to the community after lower limb fractures. The app promotes exercise, nutrition, and bone health medications to prevent future fractures. Using a Design science framework, app requirements were identified by synthesizing scientific knowledge, clinical expertise, and end-user needs. An initial mockup was developed based on these specifications and iteratively refined through design sessions incorporating end-user feedback. The final OPTIONS app features four core functions: (1) task-based self-management support, (2) personalized exercises and education, (3) motivational messaging, and (4) progress tracking allowing users to monitor their progress through visualizations. By addressing usability challenges for older adults, the app provides a personalized, engaging experience for continuous health management.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"566-574"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273056","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

Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models. 使用大型语言模型的儿童脓毒症队列的上下文表型分析。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Aditya Nagori, Ayush Gautam, Matthew O Wiens, Vuong Nguyen, Nathan Kenya Mugisha, Jerome Kabakyenga, Niranjan Kissoon, John Mark Ansermino, Rishikesan Kamaleswaran

{"title":"Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models.","authors":"Aditya Nagori, Ayush Gautam, Matthew O Wiens, Vuong Nguyen, Nathan Kenya Mugisha, Jerome Kabakyenga, Niranjan Kissoon, John Mark Ansermino, Rishikesan Kamaleswaran","doi":"","DOIUrl":"","url":null,"abstract":"The clustering of patient subgroups is essential for personalized care and efficient use of resources. Traditional clustering methods struggle with high-dimensional heterogeneous healthcare data and lack contextual understanding. This study evaluates clustering based on the Large Language Model (LLM) against classical methods using a pediatric sepsis dataset from a low-income country (LIC), containing 2,686 records with 28 numerical variables and 119 categorical variables. Patient records were serialized into text with and without a clustering objective. Embeddings were generated using quantized LLAMA 3.1 8B, DeepSeek-R1-Distill-Llama-8B with low-rank adaptation(LoRA), and Stella-En-400M-V5 models. K-means clustering was applied to these embeddings. Classical comparisons included K-Medoids clustering on UMAP and FAMD-reduced mixed data. Silhouette scores and statistical tests evaluated the quality and distinctiveness of the cluster. Stella-En-400M-V5 achieved the highest Silhouette Score (0.86). LLAMA 3.1 8B with the clustering objective performed better with a higher number of clusters, identifying subgroups with distinct nutritional, clinical, and socioeconomic profiles. LLM-based methods outperformed classical techniques by capturing richer context and prioritizing key features. These results highlight the potential of LLMs for contextual phenotyping and informed decision making in resource-limited settings.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"929-938"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273189","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

Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses. 医学诊断自然语言生成的人类评估框架及其与自动度量的关联。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Emma Croxford, Yanjun Gao, Brian Patterson, Daniel To, Samuel Tesch, Dmitriy Dligach, Anoop Mayampurath, Matthew M Churpek, Majid Afshar

{"title":"Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.","authors":"Emma Croxford, Yanjun Gao, Brian Patterson, Daniel To, Samuel Tesch, Dmitriy Dligach, Anoop Mayampurath, Matthew M Churpek, Majid Afshar","doi":"","DOIUrl":"","url":null,"abstract":"In the evolving landscape of clinical Natural Language Generation (NLG), assessing abstractive text quality remains challenging, as existing methods often overlook generative task complexities. This work aimed to examine the current state of automated evaluation metrics in NLG in healthcare. To have a robust and well-validated baseline with which to examine the alignment of these metrics, we created a comprehensive human evaluation framework. Employing ChatGPT-3.5-turbo generative output, we correlated human judgments with each metric. None of the metrics demonstrated high alignment; however, the SapBERT score-a Unified Medical Language System (UMLS)- showed the best results. This underscores the importance of incorporating domain-specific knowledge into evaluation efforts. Our work reveals the deficiency in quality evaluations for generated text and introduces our comprehensive human evaluation framework as a baseline. Future efforts should prioritize integrating medical knowledge databases to enhance the alignment of automated metrics, particularly focusing on refining the SapBERT score for improved assessments.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"309-318"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144496","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

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure. 叙事特征还是结构特征？大型语言模型识别心脏衰竭风险癌症患者的研究。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed, Yi Guo, Thomas J George, Jiang Bian, Yonghui Wu

{"title":"Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure.","authors":"Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed, Yi Guo, Thomas J George, Jiang Bian, Yonghui Wu","doi":"","DOIUrl":"","url":null,"abstract":"Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-LSTM), and large language models (LLMs) using novel narrative features derived from the structured medical codes. We identified a cancer cohort of 12,806 patients from the University of Florida Health, diagnosed with lung, breast, and colorectal cancers, among which 1,602 individuals developed HF after cancer. The LLM, GatorTron-3.9B, achieved the best F1 scores, outperforming the traditional support vector machines by 39%, the T-LSTM deep learning model by 7%, and a widely used transformer model, BERT, by 5.6%. The analysis shows that the proposed narrative features remarkably increased feature density and improved performance.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"242-251"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144634","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

Towards Optimizing LLM Use in Healthcare: Identifying Patient Questions in MyChart Messages. 优化LLM在医疗保健中的应用：在MyChart消息中识别患者问题。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Akhila Chekuri, Armaan S Johal, Matthew R Allen, John W Ayers, Michael Hogarth, Emilia Farcas

引用次数: 0

Does Transparency Promote Engagement? Cancer Patients' Access of Electronic Medical Records Before and After the Information Blocking Rule. 透明度能促进参与吗？信息封锁规则实施前后癌症患者对电子病历的访问

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Fernanda C G Polubriaginof, Susan Chimonas, Allison Lipitz-Snyderman, Zoe Spiegelhoff, Kenneth Seier, Charlie White, Joshua Jorvina, Gilad Kuperman

引用次数: 0

Identifying Genomic Data Sources from Biomedical Literature. 从生物医学文献中识别基因组数据来源。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Xu Zuo, Ashley Gilliam, Yan Hu, Kalpana Raja, Kirk Roberts, Hua Xu

{"title":"Identifying Genomic Data Sources from Biomedical Literature.","authors":"Xu Zuo, Ashley Gilliam, Yan Hu, Kalpana Raja, Kirk Roberts, Hua Xu","doi":"","DOIUrl":"","url":null,"abstract":"Genomic research is becoming increasingly data-intensive, yet the proper reference of data remains a persistent challenge. Despite various efforts to establish and standardize data citation practices, scientists frequently fall short of accurately referencing data in their papers. This deficiency complicates the attribution of contributions to data providers and impedes the reproducibility of findings in genomic research. This study addresses this gap by introducing a gold standard corpus designed to identify mentions of genomic data sources and associated attributes, thereby offering insights into data source availability and accessibility. Within this corpus, we categorize entities into six classes, encompassing three primary entities (Dataset, Repository, and Contributor) and three attributes (Accession Number, URL, and DOI). We also define and annotate the relations between these main entities and attributes. We perform a comprehensive analysis of the corpus, by assessing inter-annotator agreements and implementing an information extraction pipeline using BERT-based models. Our BERT-based models achieve a best F1 score of 0.94 in recognizing mentions of genomic data sources and 0.76 in extracting relationships between these mentions and associated attributes. By introducing this genomic data source mention corpus, we aim to propel the progress of data sharing and reuse in forthcoming genomic research.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1350-1359"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144652","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

Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs. 优化用于出院预测的大型语言模型：利用电子健康记录审计日志的最佳实践。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Xinmeng Zhang, Chao Yan, Yuyang Yang, Zhuohang Li, Yubo Feng, Bradley A Malin, You Chen

{"title":"Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs.","authors":"Xinmeng Zhang, Chao Yan, Yuyang Yang, Zhuohang Li, Yubo Feng, Bradley A Malin, You Chen","doi":"","DOIUrl":"","url":null,"abstract":"Electronic Health Record (EHR) audit log data are increasingly utilized for clinical tasks, from workflow modeling to predictive analyses of discharge events, adverse kidney outcomes, and hospital readmissions. These data encapsulate user-EHR interactions, reflecting both healthcare professionals' behavior and patients' health statuses. To harness this temporal information effectively, this study explores the application of Large Language Models (LLMs) in leveraging audit log data for clinical prediction tasks, specifically focusing on discharge predictions. Utilizing a year's worth of EHR data from Vanderbilt University Medical Center, we fine-tuned LLMs with randomly selected 10,000 training examples. Our findings reveal that LLaMA-2 70B, with an AUROC of 0.80 [0.77-0.82], outperforms both GPT-4 128K in a zero-shot, with an AUROC of 0.68 [0.65-0.71], and DeBERTa, with an AUROC of 0.78 [0.75-0.82]. Among various serialization methods, the first-occurrence approach-wherein only the initial appearance of each event in a sequence is retained-shows superior performance. Furthermore, for the fine-tuned LLaMA-2 70B, logit outputs yield a higher AUROC of 0.80 [0.77-0.82] compared to text outputs, with an AUROC of 0.69 [0.67-0.72]. This study underscores the potential of fine-tuned LLMs, particularly when combined with strategic sequence serialization, in advancing clinical prediction tasks.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1323-1331"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144658","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