AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第2页

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

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

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

Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes. 评估大型语言模型在眼科临床自由文本笔记中命名实体识别的性能。

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

Iyad Majid, Vaibhav Mishra, Rohith Ravindranath, Sophia Y Wang

引用次数: 0

Leveraging Cluster Causal Diagrams for Determining Causal Effects in Medicine. 利用聚类因果图确定医学中的因果效应。

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

Tara V Anand, George Hripcsak

引用次数: 0

Modeling Precision Feedback Knowledge for Healthcare Professional Learning and Quality Improvement. 为医疗保健专业人员学习和质量改进建模精确反馈知识。

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

Zach Landis-Lewis, Yidan Cao, Hana Chung, Peter Boisvert, Anjana Deep Renji, Patrick Galante, Ayshwarya Jagadeesan, Farid Seifi, Allison Janda, Nirav Shah, Andrew Krumm, Allen Flynn

{"title":"Modeling Precision Feedback Knowledge for Healthcare Professional Learning and Quality Improvement.","authors":"Zach Landis-Lewis, Yidan Cao, Hana Chung, Peter Boisvert, Anjana Deep Renji, Patrick Galante, Ayshwarya Jagadeesan, Farid Seifi, Allison Janda, Nirav Shah, Andrew Krumm, Allen Flynn","doi":"","DOIUrl":"","url":null,"abstract":"Healthcare providers learn continuously, but better support for provider learning is needed as new biomedical knowledge is produced at an increasing rate alongside widespread use of EHR data for clinical performance measurement. Precision feedback is an approach to improve support for provider learning by prioritizing coaching and appreciation messages based on each message's motivational potential for a specific recipient. We developed a Precision Feedback Knowledge Base as an open resource to support precision feedback systems, containing knowledge models that hold potential as key infrastructure for learning health systems. We describe the design and development of the Precision Feedback Knowledge Base, as well as its key components, including quality measures, feedback message templates, causal pathway models, signal detectors, and prioritization algorithms. Presently, the knowledge base is implemented in a national-scale quality improvement consortium for anesthesia care, to enhance provider feedback email messages.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"628-637"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144613","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

Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data. 使用真实世界跨省初级保健数据的加拿大成人联合糖尿病预测

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

Guojun Tang, Jason E Black, Tyler S Williamson, Steve H Drew

{"title":"Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.","authors":"Guojun Tang, Jason E Black, Tyler S Williamson, Steve H Drew","doi":"","DOIUrl":"","url":null,"abstract":"Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models can provide early identification of patients with high risk for diabetes, potentially leading to more effective therapeutic strategies and reduced healthcare costs. However, regulation restrictions create barriers to developing centralized predictive models. This paper addresses the challenges by introducing a federated learning approach, which amalgamates predictive models without centralized data storage and processing, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) without cross-province patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. Experimental results show that the federated MLP model presents a similar or higher performance compared to the model trained with the centralized approach. However, the federated logistic regression model showed inferior performance compared to its centralized peer.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1099-1108"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144645","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

Identifying acute kidney injury subtypes based on serum electrolyte data in ICU via K-medoids clustering. 基于ICU患者血清电解质数据的K-medoids聚类识别急性肾损伤亚型。

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

Wentie Liu, Tongyue Shi, Haowei Xu, Huiying Zhao, Jianguo Hao, Guilan Kong

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

Impact of Automated Transfer of Semi-Automated Segmentation and Structured Report Rule Requirements on Cardiac MRI Report Quality, Standardization, and Efficiency. 半自动分割和结构化报告规则要求的自动传递对心脏MRI报告质量、标准化和效率的影响。

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

Diane Rizkallah, Neil L Greenberg, Rishabh Khurana, Vadivelan Palanisamy, Ben Alencherry, Carl Ammoury, Yezan Salam, Lisa Lamovsky, Haitham Fares, Robert Geschke, Richard Grimm, Christopher Nguyen, David Chen, Deborah H Kwon

{"title":"Impact of Automated Transfer of Semi-Automated Segmentation and Structured Report Rule Requirements on Cardiac MRI Report Quality, Standardization, and Efficiency.","authors":"Diane Rizkallah, Neil L Greenberg, Rishabh Khurana, Vadivelan Palanisamy, Ben Alencherry, Carl Ammoury, Yezan Salam, Lisa Lamovsky, Haitham Fares, Robert Geschke, Richard Grimm, Christopher Nguyen, David Chen, Deborah H Kwon","doi":"","DOIUrl":"","url":null,"abstract":"Clinical reporting of cardiac magnetic resonance (CMR) imaging exams is commonly performed with a dictation approach which requires great care to capture both consistent and comprehensive data. We sought to transform the reporting process by utilizing a structured report framework for reporting standardization, by incorporating automated transfer of data semi-automated segmentation tools for efficiency, and rule-based reporting requirements to improve quality and standardization. Interfaces between the applications used to schedule and protocol exams and to analyze the acquired images were created to bring the source information directly into the structured reporting environment. The physicians reporting CMR were surveyed to determine satisfaction and improved efficiency with the new process through self-reported reporting time. Quality improvement was assessed by examining the consistency of reported parameters with the inclusion of rule-based requirements. The designed structured reporting process with automated measurements and rule-based requirements resulted in significant improvement in report efficiency and quality.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"950-959"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144657","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