AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第6页

DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making. DynamiCare：用于交互式开放式医疗决策的动态多智能体框架。

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

Tianqi Shang, Weiqing He, Charles Zheng, Lingyao Li, Li Shen, Bingxin Zhao

{"title":"DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making.","authors":"Tianqi Shang, Weiqing He, Charles Zheng, Lingyao Li, Li Shen, Bingxin Zhao","doi":"","DOIUrl":"","url":null,"abstract":"The rise of Large Language Models (LLMs) has enabled the development of specialized AI agents with domain-specific reasoning and interaction capabilities, particularly in healthcare. While recent frameworks simulate medical decision-making, they largely focus on single-turn tasks where a doctor agent receives full case information upfront-diverging from the real-world diagnostic process, which is inherently uncertain, interactive, and iterative. In this paper, we introduce MIMIC-Patient, a structured dataset built from the MIMIC-III electronic health records (EHRs), designed to support dynamic, patient-level simulations. Building on this, we propose DynamiCare, a novel dynamic multi-agent framework that models clinical diagnosis as a multi-round, interactive loop, where a team of specialist agents iteratively queries the patient system, integrates new information, and dynamically adapts its composition and strategy. We demonstrate the feasibility and effectiveness of Dynami-Care through extensive experiments, establishing the first benchmark for dynamic clinical decision-making with LLM-powered agents. Our code is available at https://github.com/THEGREATICE/DynamiCare.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"408-417"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280216","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

Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training. 基于hypergraph的预训练增强ESUS患者房颤预测。

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

Yuzhang Xie, Yuhua Wu, Ruiyu Wang, Fadi Nahab, Xiao Hu, Carl Yang

{"title":"Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training.","authors":"Yuzhang Xie, Yuhua Wu, Ruiyu Wang, Fadi Nahab, Xiao Hu, Carl Yang","doi":"","DOIUrl":"","url":null,"abstract":"Atrial fibrillation (AF) is a major complication following embolic stroke of undetermined source (ESUS), elevating the risk of recurrent stroke and mortality. Early identification is clinically important, yet existing tools face limitations in accuracy, scalability, and cost. Machine learning (ML) offers promise but is hindered by small ESUS cohorts and high-dimensional medical features. To address these challenges, we introduce supervised and unsupervised hypergraph-based pre-training strategies to improve AF prediction in ESUS patients. We first pre-train hypergraph-based patient embedding models on a large stroke cohort (7,780 patients) to capture salient features and higher-order interactions. The resulting embeddings are transferred to a smaller ESUS cohort (510 patients), reducing feature dimensionality while preserving clinically meaningful information, enabling effective prediction with lightweight models. Experiments show that both pre-training approaches outperform traditional models trained on raw data, improving accuracy and robustness. This framework offers a scalable and efficient solution for AF risk prediction after stroke.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"526-535"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280211","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

End-to-end extraction of temporal information from psychiatric discharge summaries. 从精神科出院总结中提取端到端时间信息。

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

Spencer Thomas, Gaby Dinh, WonJin Yoon, Boyu Ren, Guergana Savova, Mei-Hua Hall, Timothy A Miller

引用次数: 0

Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations. 基于多模式ehr的儿童哮喘发作预测。

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

Zhengkang Fan, Jinqian Pan, Mengxian Lyu, Renjie Liang, Chengkun Sun, Yonghui Wu, David Fedele, Jennifer Fishe, Jie Xu

{"title":"Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations.","authors":"Zhengkang Fan, Jinqian Pan, Mengxian Lyu, Renjie Liang, Chengkun Sun, Yonghui Wu, David Fedele, Jennifer Fishe, Jie Xu","doi":"","DOIUrl":"","url":null,"abstract":"Pediatric asthma exacerbations are a frequent cause of emergency department (ED) visits and hospitalizations, yet accurate risk prediction remains limited and no consensus risk scores exist. Using UF Health electronic health records (EHRs) from 2011-2023, we evaluated two computable phenotypes (i.e., CAPriCORN and COMPAC) to predict exacerbations over 6-, 12-, and 24-month horizons. Exacerbations were defined using a validated composite of diagnosis codes from ED, inpatient, or outpatient encounters combined with systemic corticosteroids prescriptions. Several commonly used machine learning (ML) models were trained with stratified five-fold cross-validation, Bayesian hyper-parameter optimization, and Youden's J thresholding. XGBoost achieved the best performance, with SHapley Additive exPlanations (SHAP) highlighting note-derived symptom terms and rescue-medication use as dominant predictors. Future work will focus on external validation and assessment of generalizability. This interpretable, text-integrated framework may support child-specific risk stratification and inform EHR-based decision support for timely pediatric asthma management.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"125-134"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280162","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

KRAS Clinical Trial Finder: A Patient-Centered Platform for Improving Access to Clinical Trial Information. KRAS临床试验查找器：一个以患者为中心的改善临床试验信息获取的平台。

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

Chris C Conneran, Terri Conneran, Bonnie H M Chen, Danny T Y Wu

{"title":"KRAS Clinical Trial Finder: A Patient-Centered Platform for Improving Access to Clinical Trial Information.","authors":"Chris C Conneran, Terri Conneran, Bonnie H M Chen, Danny T Y Wu","doi":"","DOIUrl":"","url":null,"abstract":"KRAS mutations are linked to aggressive cancers, yet many patients struggle to locate trials that match their tumor subtype or circumstances. This study presents the design and user-centered evaluation of the KRAS Clinical Trial Finder, a patient-centered platform that improves access to KRAS-specific clinical trial information. A Python pipeline was developed to extract and transform KRAS-related trials from ClinicalTrials.gov into a relational database, which was presented through an interactive R Shiny dashboard offering plain-language summaries and distance calculations. Twenty-eight KRAS Kickers patients and care partners completed four usability tasks, evaluated with the Single Ease Question (SEQ) and System Usability Scale (SUS). Results demonstrated excellent usability (mean SEQ 6.44/7; SUS 91.4) and rapid task completion (mean 66.5 seconds), with 95% expressing willingness to reuse the system. Qualitative analysis identified five improvement domains: visibility, personalization, education, empowerment, and accessibility. Future directions include EHR integration, AI-enabled trial matching, enhanced education, and multilingual support.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"117-124"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280167","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

Prompt injection of OpenAI custom GPTs leaks informatics secrets. 提示注入OpenAI自定义gpt会泄露信息机密。

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

Van Q Truong, Marylyn D Ritchie

引用次数: 0

RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation. RadAnnotate：高效可靠的放射学报告注释的大型语言模型。

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

Saisha Pradeep Shetty, Roger Eric Goldman, Vladimir Filkov

{"title":"RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation.","authors":"Saisha Pradeep Shetty, Roger Eric Goldman, Vladimir Filkov","doi":"","DOIUrl":"","url":null,"abstract":"Radiology report annotation is essential for clinical NLP, yet manual labeling is slow and costly. We present RadAnnotate, an LLM-based framework that studies retrieval-augmented synthetic reports and confidence-based selective automation to reduce expert effort for labeling in RadGraph. We study RadGraph-style entity labeling (graph nodes) and leave relation extraction (edges) to future work. First, we train entity-specific classifiers on gold-standard reports and characterize their strengths and failure modes across anatomy and observation categories, with uncertain observations hardest to learn. Second, we generate RAG-guided synthetic reports and show that synthetic-only models remain within 1-2 F1 points of gold-trained models, and that synthetic augmentation is especially helpful for uncertain observations in a low-resource setting, improving F1 from 0.61 to 0.70. Finally, by learning entity-specific confidence thresholds, RadAnnotate can automatically annotate 55-90% of reports at 0.86-0.92 entity match score while routing low-confidence cases for expert review.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"418-427"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280137","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 Early Prediction of Amyotrophic Lateral Sclerosis Empowered by Machine Learning and Clinical Big Data. 基于机器学习和临床大数据的肌萎缩侧索硬化症早期预测

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

Askar Safipour Afshar, Jefferey Statland, Xing Song

{"title":"Towards Early Prediction of Amyotrophic Lateral Sclerosis Empowered by Machine Learning and Clinical Big Data.","authors":"Askar Safipour Afshar, Jefferey Statland, Xing Song","doi":"","DOIUrl":"","url":null,"abstract":"Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by substantial symptom heterogeneity and overlap with other neurological conditions, often delaying diagnosis. This study developed a consensus-based feature selection framework to identify a stable and parsimonious minimal feature set for early ALS prediction using large-scale observational data. Using multi-year medical claims and multi-site EHRs, we identified 1,716 ALS cases with matched controls. The approach integrated variability across sample, task, and model dimensions to isolate features predictive up to 18 months before diagnosis. Predictive models using LASSO regression and GBT were evaluated with AUROC and classification metrics. The resulting nine-feature set achieved AUROC values above 0.85 across time windows. The GBT model was further evaluated in musculoskeletal, nervous system, and limb or bulbar subgroups, demonstrating reliable discrimination and preserved sensitivity and specificity. These findings highlight the potential of stable minimal feature sets to support earlier ALS identification.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"623-632"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279946","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

LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories. LabSage：用于临床实验室增强检索-增强生成的结构-语义解耦。

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

Hang Zhang, Yuelyu Ji, Chenyu Li, Hooman Rashidi, Sarah Wheeler, Yanshan Wang

{"title":"LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.","authors":"Hang Zhang, Yuelyu Ji, Chenyu Li, Hooman Rashidi, Sarah Wheeler, Yanshan Wang","doi":"","DOIUrl":"","url":null,"abstract":"Modern clinical laboratory operates within a stringent regulatory ecosystem requiring precise adherence to Standard Operating Procedures (SOPs). However, standard Retrieval-Augmented Generation (RAG) approaches frequently fail in this domain due to context fragmentation, where fixed-size segmentation arbitrarily severs critical procedural dependencies. To address this, we introduce LabSage, a domain-adapted framework implementing structural-semantic decoupling. This hierarchical architecture indexes compact search units for optimal retrieval precision while dynamically retrieving expanded context units to preserve procedural completeness during inference. Evaluated on authentic laboratory queries using Qwen-2.5-7B as backbone, LabSage achieved an Answer Accuracy of 0.780 and Context Recall of 0.909, outperforming standard RAG by 8.3% and 5.7%, respectively. These findings demonstrate that decoupling vector search from reasoning context is a critical architectural advancement in regulated medical domains to mitigate safety-critical omissions and ensure compliance.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"584-593"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280148","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 Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography. 门控和非门控计算机断层扫描冠状动脉钙评分的跨域泛化框架。

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, Moneera N Haque, Steve W Leung, Caroline N Leach, Seth Parker, Stephen B Hobbs, Mary E Oates, Vincent L Sorrell, W Brent Seales, V K Cody Bumgardner

{"title":"A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography.","authors":"Mahmut S Gokmen, Moneera N Haque, Steve W Leung, Caroline N Leach, Seth Parker, Stephen B Hobbs, Mary E Oates, Vincent L Sorrell, W Brent Seales, V K Cody Bumgardner","doi":"","DOIUrl":"","url":null,"abstract":"Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce an automated framework for CAC detection and lesion-specific Agatston scoring that works across gated and non-gated CT scans. At its core is CARD-ViT, a self-supervised Vision Transformer trained exclusively on gated CT data using DINO. Without any non-gated training data, our framework achieves 0.707 accuracy and Cohen's κ of 0.528 on the publicly available Stanford non-gated dataset, matching models trained directly on non-gated scans. On gated test sets, the framework achieves 0.910 accuracy with Cohen's κ scores of 0.871 and 0.874 across independent datasets, demonstrating risk stratification. Results demonstrate the feasibility of cross-domain CAC scoring from gated to non-gated domains, supporting scalable cardiovascular screening in routine chest imaging without additional scans or annotations.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"163-172"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280024","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