AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第7页

Use of Family Relationships in Commercial Claims Data to Characterize Clinical Events of Patients with BRCA1/2 Cascade Testing. 利用商业索赔数据中的家庭关系来表征BRCA1/2级联检测患者的临床事件

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Kevin Gorman, Xuyang Li, Carolyn Applegate, Casey Overby Taylor

{"title":"Use of Family Relationships in Commercial Claims Data to Characterize Clinical Events of Patients with BRCA1/2 Cascade Testing.","authors":"Kevin Gorman, Xuyang Li, Carolyn Applegate, Casey Overby Taylor","doi":"","DOIUrl":"","url":null,"abstract":"BRCA1/2 genetic cascade testing has been studied in small-scale survey and controlled settings, but limited large-scale studies have been done because health records don't typically link family members. This study uses commercial claims data that connect family members on the same insurance plan to identify a large cohort of BRCA1/2 proband and cascade testers and characterizes their breast cancer screening and prevention service utilization. We identified 709 proband and 819 cascade testers. Consistent with clinical guidelines to test for a specific BRCA1/2 variant in a family, more cascade testers received known variant tests than proband testers. Also consistent with guidelines, more proband testers received BRCA1/2 testing after a breast cancer diagnosis compared to cascade testers, and more cascade testers had genetic counseling events pre-BRCA1/2 testing compared to proband testers. This technique and large BRCA1/2 genetic cascade testing cohort can open doors for new research.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"173-182"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280015","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

Vision Foundry: A System for Training Foundational Vision AI Models. Vision Foundry：一个训练基础视觉AI模型的系统。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Mahmut S Gokmen, Mitchell A Klusty, Evan W Damron, W Vaiden Logan, Aaron D Mullen, Caroline N Leach, Emily B Collier, Samuel E Armstrong, V K Cody Bumgardner

{"title":"Vision Foundry: A System for Training Foundational Vision AI Models.","authors":"Mahmut S Gokmen, Mitchell A Klusty, Evan W Damron, W Vaiden Logan, Aaron D Mullen, Caroline N Leach, Emily B Collier, Samuel E Armstrong, V K Cody Bumgardner","doi":"","DOIUrl":"","url":null,"abstract":"Self-supervised learning (SSL) leverages vast unannotated medical datasets, yet steep technical barriers limit adoption by clinical researchers. We introduce Vision Foundry, a code-free, HIPAA-compliant platform that democratizes pre-training, adaptation, and deployment of foundational vision models. The system integrates the DINO-MX framework, abstracting distributed infrastructure complexities while implementing specialized strategies like Magnification-Aware Distillation (MAD) and Parameter-Efficient Fine-Tuning (PEFT). We validate the platform across domains, including neuropathology segmentation, lung cellularity estimation, and coronary calcium scoring. Our experiments demonstrate that models trained via Vision Foundry significantly outperform generic baselines in segmentation fidelity and regression accuracy, while exhibiting robust zero-shot generalization across imaging protocols. By bridging the gap between advanced representation learning and practical application, Vision Foundry enables domain experts to develop state-of-the-art clinical AI tools with minimal annotation overhead, shifting focus from engineering optimization to clinical discovery.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"153-162"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279961","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 Secure Sandbox Environment for Orchestrating Medical AI Agents Using Model Context Protocols and Role-Based Access Control. 使用模型上下文协议和基于角色的访问控制协调医疗AI代理的安全沙盒环境。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

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

{"title":"A Secure Sandbox Environment for Orchestrating Medical AI Agents Using Model Context Protocols and Role-Based Access Control.","authors":"Samuel E Armstrong, Mitchell A Klusty, W Vaiden Logan, Caroline N Leach, V K Cody Bumgardner","doi":"","DOIUrl":"","url":null,"abstract":"The evolution of Large Language Models into autonomous agents presents significant opportunities for healthcare, yet no secure environment exists to experiment with these tools in clinical settings. Current agentic systems are unsuitable due to insufficient security mechanisms and the absence of visual components necessary for effective use. This paper introduces Astral, a secure sandbox environment utilizing an Orchestrator-Specialist model for the controlled, secure orchestration, experimentation, and evaluation of medical AI agents. Astral enforces safety and accountability through an auditable identity layer using OAuth 2.0 Token Exchange, which creates verifiable actor claims to log every agent action. To support clinical workflows, it introduces a \"generative visual layer\" built on persistent WebSockets and Model Context Protocol to enable low-latency, multimodal interaction. Controlled simulations validated that this architecture neutralizes Tool Poisoning Attacks and provides improved transport performance, establishing a pragmatic foundation for trustworthy clinical AI.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"57-66"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280121","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

Feasibility of Automated Precharting using GPT-4 in New Specialty Referrals. 在新的专科转诊中使用GPT-4自动预诊的可行性。

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

April S Liang, Juan M Banda, Thomas Savage, Abby Pandya, Rebecca Carey, Uchechukwu C Megwalu, Michael T Chang, Dev Dash, Conor K Corbin, Aditya Sharma, Rahul Thapa, Nikesh Kotecha, Nigam H Shah, Jennifer Y Lee, Jonathan H Chen

{"title":"Feasibility of Automated Precharting using GPT-4 in New Specialty Referrals.","authors":"April S Liang, Juan M Banda, Thomas Savage, Abby Pandya, Rebecca Carey, Uchechukwu C Megwalu, Michael T Chang, Dev Dash, Conor K Corbin, Aditya Sharma, Rahul Thapa, Nikesh Kotecha, Nigam H Shah, Jennifer Y Lee, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"This study evaluates the feasibility of using GPT-4 to automate precharting for specialty referrals, focusing on new patients referred to an otolaryngology clinic for nasal congestion. We describe the design decisions and strategies tested in creating this precharting utility, including methods for prompt design and token limit handling. Through iterative testing and building, our tool achieved 95.0% agreement with physician consensus in a small retrospective test sample. Results from a small prospective pilot showed favorable feedback of summaries in a real-world clinical setting, though there was a discrepancy between high intention to use the summary but lower perception of time savings. Our results demonstrate that automated pre-charting with accuracy and clinical relevance can be feasible with large language models such as GPT-4. Our design features can inform the development of vendor chart summarization solutions.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"312-321"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276846","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

Sex-Based Differences in the Association of Epigenetic Age Acceleration with Alzheimer's Disease Biomarkers and Cognitive Measures. 表观遗传年龄加速与阿尔茨海默病生物标志物和认知测量之间的性别差异

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

Travyse A Edwards, Tianhua Zhai, Kwangsik Nho, Andrew J Saykin, Qi Long, Li Shen

{"title":"Sex-Based Differences in the Association of Epigenetic Age Acceleration with Alzheimer's Disease Biomarkers and Cognitive Measures.","authors":"Travyse A Edwards, Tianhua Zhai, Kwangsik Nho, Andrew J Saykin, Qi Long, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"Alzheimer's Disease (AD) is a neurodegenerative disorder marked by cognitive and functional decline. Biological sex has been linked to differences in lifetime AD risk, AD-related neuropathology, and the rate of cognitive decline, although the underlying biological mechanisms driving these disparities remain unclear. Epigenetic Age Acceleration-a metric derived from epigenetic aging clocks-has been associated with numerous aging-related conditions such as AD. Although there is promise in using Epigenetic age acceleration as a biomarker for several aging related diseases, the underlying mechanism that aging clocks are actually predicting is not well understood. In this study, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to examine how sex influences the relationship between age acceleration and cognitive performance as well as brain volume. Our findings suggest that, although epigenetic age acceleration can predict changes in brain structure, these changes don't appear to be different across sexes. Future research should focus on validating these findings in an external cohort and exploring them longitudinally.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"141-148"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276884","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

Systematic Exploration of Hospital Cost Variability: A Conformal Prediction-Based Outlier Detection Method for Electronic Health Records. 医院成本变异性的系统探索：一种基于适形预测的电子健康记录异常检测方法。

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

François Grolleau, Ethan Goh, Stephen P Ma, Jonathan Masterson, Ted Ross, Arnold Milstein, Jonathan H Chen

{"title":"Systematic Exploration of Hospital Cost Variability: A Conformal Prediction-Based Outlier Detection Method for Electronic Health Records.","authors":"François Grolleau, Ethan Goh, Stephen P Ma, Jonathan Masterson, Ted Ross, Arnold Milstein, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"Marked variability in inpatient hospitalization costs poses significant challenges to healthcare quality, resource allocation, and patient outcomes. Traditional methods like Diagnosis-Related Groups (DRGs) aid in cost management but lack practical solutions for enhancing hospital care value. We introduce a novel methodology for outlier detection in Electronic Health Records (EHRs) using Conformal Prediction. This approach identifies and prioritizes areas for optimizing high-value care processes. Unlike conventional predictive models that neglect uncertainty, our method employs Conformal Quantile Regression (CQR) to generate robust prediction intervals, offering a comprehensive view of cost variability. By integrating Conformal Prediction with machine learning models, healthcare professionals can more accurately pinpoint opportunities for quality and efficiency improvements. Our framework systematically evaluates unexplained hospital cost variations and generates interpretable hypotheses for refining clinical practices associated with atypical costs. This data-driven approach offers a systematic method to generate clinically sound hypotheses that may inform processes to enhance care quality and optimize resource utilization.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"187-195"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276889","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

powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities. powerROC：一个用于评估模型判别能力的样本量计算的交互式网络工具。

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

François Grolleau, Robert Tibshirani, Jonathan H Chen

{"title":"powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities.","authors":"François Grolleau, Robert Tibshirani, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC's utility through a case study on hospital mortality prediction using the MIMIC database.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"196-204"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276878","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

Studying Veteran food insecurity longitudinally using electronic health record data and natural language processing. 利用电子健康记录数据和自然语言处理技术对退伍军人食品安全状况进行纵向研究。

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

Alec B Chapman, Talia Panadero, Rachel Dalrymple, Alicia Cohen, Nipa Kamdar, Farhana Pethani, Andrea Kalvesmaki, Richard E Nelson, Jorie Butler

引用次数: 0

RD-LIVES: A Living Evidence Synthesis System for Rare Disease Treatment Efficacy and Safety. RD-LIVES：罕见病治疗疗效和安全性的活证据合成系统。

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

Jinlian Wang, Hui Li, Hongfang Liu

引用次数: 0

Enhancing Disease Detection in Radiology Reports Through Fine-tuning Lightweight LLM on Weak Labels. 通过微调弱标签上的轻量级LLM来增强放射学报告中的疾病检测。

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

Yishu Wei, Xindi Wang, Hanley Ong, Yiliang Zhou, Adam Flanders, George Shih, Yifan Peng

{"title":"Enhancing Disease Detection in Radiology Reports Through Fine-tuning Lightweight LLM on Weak Labels.","authors":"Yishu Wei, Xindi Wang, Hanley Ong, Yiliang Zhou, Adam Flanders, George Shih, Yifan Peng","doi":"","DOIUrl":"","url":null,"abstract":"Despite significant progress in applying large language models (LLMs) to the medical domain, several limitations still prevent them from practical applications. Among these are the constraints on model size and the lack of cohort-specific labeled datasets. In this work, we investigated the potential of improving a lightweight LLM, such as Llama 3.1-8B, through fine-tuning with datasets using synthetic labels. Two tasks are jointly trained by combining their respective instruction datasets. When the quality of the task-specific synthetic labels is relatively high (e.g., generated by GPT4-o), Llama 3.1-8B achieves satisfactory performance on the open-ended disease detection task, with a micro F1 score of 0.91. Conversely, when the quality of the task-relevant synthetic labels is relatively low (e.g., from the MIMIC-CXR dataset), fine-tuned Llama 3.1-8B is able to surpass its noisy teacher labels (micro F1 score of 0.67 v.s. 0.63) when calibrated against curated labels, indicating the strong inherent underlying capability of the model. These findings demonstrate the potential offine-tuning LLMs with synthetic labels, offering a promising direction for future research on LLM specialization in the medical domain.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"614-623"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276868","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