AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第8页

Exploring ChatGPT 3.5 for structured data extraction from oncological notes. 探索ChatGPT 3.5从肿瘤笔记中提取结构化数据。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Ty J Skyles, Isaac J Freeman, Georgewilliam Kalibbala, David Davila-Garcia, Kendall Kiser, Silpa Raju, Adam Wilcox

引用次数: 0

Temporal Rule Mining for Enhanced Risk Pattern Extraction: A Case Study with Acute Kidney Injury. 增强风险模式提取的时间规则挖掘：以急性肾损伤为例。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Ho Yin Chan, Alan S Yu, Mei Liu

{"title":"Temporal Rule Mining for Enhanced Risk Pattern Extraction: A Case Study with Acute Kidney Injury.","authors":"Ho Yin Chan, Alan S Yu, Mei Liu","doi":"","DOIUrl":"","url":null,"abstract":"Association rule mining is a widely used data mining technique to uncover knowledge from large datasets. In healthcare, it can reveal meaningful patterns within electronic health records (EHR) that inform clinical decision-making and treatment strategies. However, many studies neglect the temporal aspects of EHR data, potentially overlooking patterns linked to specific time periods or sequence of clinical events. Recent advancements have introduced methods for mining temporal association rules, offering enhanced predictive and descriptive insights. We propose a multi-step framework that utilizes temporal pattern mining algorithm to extract actionable and temporal risk patterns for acute kidney injury (AKI) from EHR data. Our algorithm identified approximately 3,313 rules with 10 actionable features, characterized by low support and high confidence. These rules have a median support of 0.055 and a median confidence of 0.58. We highlight key rules, explore their potential clinical implications, and present a network-based view to provide actionable insights.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"115-123"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276890","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

Transfer Learning with Clinical Concept Embeddings from Large Language Models. 基于大型语言临床概念嵌入的迁移学习。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Yuhe Gao, Runxue Bao, Yuelyu Ji, Yiming Sun, Chenxi Song, Jeffrey P Ferraro, Ye Ye

{"title":"Transfer Learning with Clinical Concept Embeddings from Large Language Models.","authors":"Yuhe Gao, Runxue Bao, Yuelyu Ji, Yiming Sun, Chenxi Song, Jeffrey P Ferraro, Ye Ye","doi":"","DOIUrl":"","url":null,"abstract":"Knowledge exchange is crucial in healthcare, particularly when leveraging data from multiple clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer learning can facilitate cross-site knowledge transfer, yet a significant challenge is the heterogeneity in clinical concepts across different sites. Recently, Large Language Models (LLMs) have shown significant potential in capturing the semantic meanings of clinical concepts and mitigating heterogeneity in biomedicine. This study analyzed electronic health records from two large healthcare systems to assess the impact of semantic embeddings from LLMs on local models, shared models, and transfer learning models. The results indicate that domain-specific LLMs, such as Med-BERT, consistently outperform in local and direct transfer scenarios, whereas generic models like OpenAI embeddings may need fine-tuning for optimal performance. This study emphasizes the importance of domain-specific embeddings and meticulous model tuning for effective knowledge transfer in healthcare. It remains essential to investigate the balance the balance between the complexity of downstream tasks, the size of training samples, and the extent of model tuning.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"167-176"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276904","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

Institutional Platform for Secure Self-Service Large Language Model Exploration. 安全自助服务大型语言模式探索的机构平台。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

V K Cody Bumgardner, Mitchell A Klusty, W Vaiden Logan, Samuel E Armstrong, Caroline N Leach, Caylin Hickey, Jeff Talbert

{"title":"Institutional Platform for Secure Self-Service Large Language Model Exploration.","authors":"V K Cody Bumgardner, Mitchell A Klusty, W Vaiden Logan, Samuel E Armstrong, Caroline N Leach, Caylin Hickey, Jeff Talbert","doi":"","DOIUrl":"","url":null,"abstract":"This paper introduces a user-friendly platform developed by the University of Kentucky Center for Applied AI, designed to make customized large language models (LLMs) more accessible. By capitalizing on recent advancements in multi-LoRA inference, the system efficiently accommodates custom adapters for a diverse range of users and projects. The paper outlines the system's architecture and key features, encompassing dataset curation, model training, secure inference, and text-based feature extraction. We illustrate the establishment of a tenant-aware computational network using agent-based methods, securely utilizing islands of isolated resources as a unified system. The platform strives to deliver secure, affordable LLM services, emphasizing process and data isolation, end-to-end encryption, and role-based resource authentication. This contribution aligns with the overarching goal of enabling simplified access to cutting-edge AI models and technology in support of scientific discovery and the development of biomedical informatics.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"105-114"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276851","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

SLR: A Modified Logistic Regression Model with Sinkhorn Divergence for Alzheimer's Disease Classification. 具有Sinkhorn散度的修正Logistic回归模型用于阿尔茨海默病分类。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Qipeng Zhan, Zhuoping Zhou, Zixuan Wen, Zexuan Wang, Boning Tong, Heng Huang, Andrew J Saykin, Paul M Thompson, Christos Davatzikos, Li Shen

引用次数: 0

Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models. 利用快速调优的大语言模型增强健康提取社会决定因素的跨领域泛化性。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Cheng Peng, Zehao Yu, Kaleb E Smith, Wei-Hsuan Lo-Ciganic, Jiang Bian, Yonghui Wu

{"title":"Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models.","authors":"Cheng Peng, Zehao Yu, Kaleb E Smith, Wei-Hsuan Lo-Ciganic, Jiang Bian, Yonghui Wu","doi":"","DOIUrl":"","url":null,"abstract":"The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs. We examined two types of LLM architectures, including encoder-only GatorTron and decoder-only GatorTronGPT, and evaluated their performance for the extraction of social determinants of health (SDoH) using a cross-institution dataset from the 2022 n2c2 challenge and a cross-disease dataset from the University of Florida (UF) Health. The results show that decoder-only LLMs with prompt tuning achieved better performance in cross-domain applications. GatorTronGPT achieved the best F1 scores for both datasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a cross-institution setting, and 5.5% and 14.5% in a cross-disease setting.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"432-440"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276867","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

Determining the Importance of Clinical Modalities for NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis. 使用机器学习和生存分析确定神经退行性疾病和患者损伤风险的临床模式的重要性。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Kazi Noshin, Mary Regina Boland, Bojian Hou, Weiqing He, Victoria Lu, Carol Manning, Li Shen, Aidong Zhang

{"title":"Determining the Importance of Clinical Modalities for NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis.","authors":"Kazi Noshin, Mary Regina Boland, Bojian Hou, Weiqing He, Victoria Lu, Carol Manning, Li Shen, Aidong Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Falls among the elderly and especially those with NeuroDegenerative Disorders (NDD) reduces life expectancy. The purpose of this study is to explore the role of Machine Learning on Electronic Health Records (EHR) data for time-to-event survival analysis prediction of injuries, and role of sensitive attributes, e.g., Race, Ethnicity, Sex, in these models. We used multiple survival analysis methods on a cohort of 29,045 patients 65 years and older treated at PennMedicine for either NDD, Mild Cognitive Impairment (MCI), or another disease. We compare the algorithms and explore the role of multiple modalities on improving prediction of injuries among NDD patients, specifically medications and laboratory tests. Overall, we found that medication features resulted in either increased Hazard Ratios (HR) or reduced HR depending on the NDD type. We found that being of Black race significantly increased the risk offall/injury in the models that included only medication and sensitive attribute features. The combined model that used both modalities (medications and laboratory information) removed this relationship between being of Black race and increases in fall/injury. Therefore, we found that combining modalities in these survival models in the prediction offall/injury risk among NDD and MCI individuals results in findings that are robust to different Racial and Ethnic groups with no biases apparent in our final combined modality results. Furthermore, combining modalities (both medications and laboratory values) improved the survival analysis performance across multiple survival analysis methods, when compared using the C-index.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"385-394"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276862","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

A Method for Enabling Digital Health Technologies in Clinical and Translational Research at an Academic Medical Center. 在学术医疗中心的临床和转化研究中启用数字健康技术的方法。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Cindy Chen, Laura R Bradford, Melissa A Epstein, J Travis Gossey, Mohammad N Mansour, Christy M O'Connor, Brian J Tschinkel, Thomas R Campion

{"title":"A Method for Enabling Digital Health Technologies in Clinical and Translational Research at an Academic Medical Center.","authors":"Cindy Chen, Laura R Bradford, Melissa A Epstein, J Travis Gossey, Mohammad N Mansour, Christy M O'Connor, Brian J Tschinkel, Thomas R Campion","doi":"","DOIUrl":"","url":null,"abstract":"Federal- and state-level governance as well as local institutional oversight are changing rapidly to address the accelerated growth in the usage of digital health technologies (DHT) -such as apps, wearables, and websites-to enable clinical and translational research. While studies have described frameworks for assessing and/or implementing individual DHTs, to our knowledge there are none describing how to implement and support multiple DHTs at an academic medical center (AMC). A multi-disciplinary team including information technology, institutional review board, legal, and privacy professionals identified 33 items to evaluate as part of onboarding studies using DHTs. In a one-year period at one AMC, we applied the novel instrument to review 98 requests for research (93) and non-research (5) use of DHTs. The 33-item instrument may be valuable to researchers and practitioners in other settings seeking to scale institutional support for DHTs.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"134-140"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276833","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

Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning. 基于提示调优的大型语言模型的医患对话自动摘要。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Mengxian Lyu, Cheng Peng, Xiaohan Li, Patrick Balian, Jiang Bian, Yonghui Wu

{"title":"Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning.","authors":"Mengxian Lyu, Cheng Peng, Xiaohan Li, Patrick Balian, Jiang Bian, Yonghui Wu","doi":"","DOIUrl":"","url":null,"abstract":"Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters. We compared GatorTronGPT with a previous solution based on fine-tuning of a widely used T5 model, using a clinical benchmark dataset MTS-DIALOG. The experimental results show that the GatorTronGPT-20B model achieved the best performance on all evaluation metrics. The proposed solution has a low computing cost as the LLM parameters are not updated during prompt-tuning. This study demonstrates the efficiency of generative clinical LLMs for clinical ATS through prompt tuning.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"342-349"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276839","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

Early Alzheimer's Detection Through Voice Analysis: Harnessing Locally Deployable LLMs via ADetectoLocum, a privacy-preserving diagnostic system. 通过语音分析早期检测阿尔茨海默氏症：利用本地可部署的llm通过ADetectoLocum，一个隐私保护诊断系统。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01

Genevieve A Mortensen, Rui Zhu

引用次数: 0