AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第2页

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

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

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

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

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

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

Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences. 用于临床结果预测的可解释人工智能：临床医生感知和偏好的调查。

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

Jun Hou, Lucy Lu Wang

引用次数: 0

Outpatient Portal Use and Blood Pressure Management during Pregnancy. 门诊门静脉使用与妊娠期血压管理。

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

Athena Stamos, Naleef Fareed

引用次数: 0

Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors. 基因组研究中的隐私保护：作者的综合框架。

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

Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday

{"title":"Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors.","authors":"Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday","doi":"","DOIUrl":"","url":null,"abstract":"As genomic research continues to advance, sharing of genomic data and research outcomes has become increasingly important for fostering collaboration and accelerating scientific discovery. However, such data sharing must be balanced with the need to protect the privacy of individuals whose genetic information is being utilized. This paper presents a bidirectional framework for evaluating privacy risks associated with data shared (both in terms of summary statistics and research datasets) in genomic research papers, particularly focusing on re-identification risks such as membership inference attacks (MIA). The framework consists of a structured workflow that begins with a questionnaire designed to capture researchers' (authors') self-reported data sharing practices and privacy protection measures. Responses are used to calculate the risk of re-identification for their study (paper) when compared with the National Institutes of Health (NIH) genomic data sharing policy. Any gaps in compliance help us to identify potential vulnerabilities and encourage the researchers to enhance their privacy measures before submitting their research for publication. The paper also demonstrates the application of this framework, using published genomic research as case study scenarios to emphasize the importance of implementing bidirectional frameworks to support trustworthy open science and genomic data sharing practices.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"177-186"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276883","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