AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第4页

The Maturity of Maturity Models in Health Care and Informatics. 卫生保健与信息学成熟度模型的成熟度。

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

Adam Wilcox, David A Dorr, Boyd Knosp, Robin Champieux, William Barnett, Nick Anderson, Justin Starren, Jodyn Platt, Ashish Vaidyanathan, Peter J Embí

引用次数: 0

Knowledge Graph Augmented Large Language Models for Disease Prediction. 知识图谱增强的疾病预测大语言模型。

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

Ruiyu Wang, Tuan Vinh, Ran Xu, Yuyin Zhou, Jiaying Lu, Francisco Pasquel, Mohammed K Ali, Carl Yang

{"title":"Knowledge Graph Augmented Large Language Models for Disease Prediction.","authors":"Ruiyu Wang, Tuan Vinh, Ran Xu, Yuyin Zhou, Jiaying Lu, Francisco Pasquel, Mohammed K Ali, Carl Yang","doi":"","DOIUrl":"","url":null,"abstract":"Electronic health records (EHRs) support strong clinical prediction but often provide coarse, post hoc explanations that are hard to use for patient-level decisions. We propose a knowledge-graph (KG)-guided chain-of-thought (CoT) framework for visit-level disease prediction on MIMIC-III. We map ICD-9 codes to PrimeKG, mine disease-relevant nodes and paths, and use these paths to scaffold temporally consistent CoT explanations, retaining only samples whose conclusions match observed outcomes. We then fine-tune lightweight LLaMA-3.1-Instruct-8B and Gemma-7B models on two small cohorts (400 and 1,000 index visits) across ten PrimeKG-mapped diseases. Our models outper-form strong classical baselines, reaching AUROC of 0.66-0.70 and macro-AUPR of 0.40-0.47. Without additional training, the models transfer zero-shot to the CRADLE cohort, improving accuracy from 0.40-0.51 to 0.72-0.77. Blinded clinicians consistently prefer KG-guided CoT for clarity, relevance, and correctness. Code is available at https://github.com/JonathanWry/KG-guided-LLM-pipeline.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"496-505"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280172","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

Quantifying the Independent and Combined Contributions of Clinical, Genetic, Proteomic, and Metabolomic Signals to ASCVD Prediction. 量化临床、遗传、蛋白质组学和代谢组学信号对ASCVD预测的独立和联合贡献。

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

Yonghyun Nam, Dokyoon Kim

{"title":"Quantifying the Independent and Combined Contributions of Clinical, Genetic, Proteomic, and Metabolomic Signals to ASCVD Prediction.","authors":"Yonghyun Nam, Dokyoon Kim","doi":"","DOIUrl":"","url":null,"abstract":"Atherosclerotic cardiovascular disease (ASCVD) risk prediction based on clinical factors and polygenic risk scores (PRS) does not fully capture dynamic biological processes related to near-term risk. Using UK Biobank participants with matched genetics, plasma proteomics, metabolomics, and clinical data (n=15,787; 1,731 incident ASCVD events), we evaluated the independent and combined contributions of clinical, genetic, proteomic, and metabolomic signals within a unified modeling framework. Modality-specific risk scores were constructed for PRS, proteomics (ProRS), metabolomics (MetRS), and clinical factors, then integrated using Cox models. Proteomics provided the strongest independent predictive contribution, PRS contributed orthogonal inherited risk information, and metabolomics provided intermediate prognostic value. Multi-modal integration improved discrimination compared with single-modality models, and joint ProRS-PRS stratification identified biologically and clinically distinct risk subgroups. These findings support multi-modal risk modeling, particularly proteomics-integrated approaches, for improving ASCVD risk stratification and precision prevention.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"343-352"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280202","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

Adaptive Integration of Incomplete Multimodal 3D Neuroimaging for Alzheimer's Prediction and Biomarker Discovery. 不完全多模态三维神经成像自适应集成用于阿尔茨海默病预测和生物标志物发现。

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

Jenna L Ballard, Li Shen, Qi Long

{"title":"Adaptive Integration of Incomplete Multimodal 3D Neuroimaging for Alzheimer's Prediction and Biomarker Discovery.","authors":"Jenna L Ballard, Li Shen, Qi Long","doi":"","DOIUrl":"","url":null,"abstract":"Alzheimer's disease (AD) diagnosis requires analysis of diverse data types to capture the heterogeneous factors underlying its development and progression. Magnetic resonance imaging (MRI) and positron emission tomography (PET) noninvasively measure brain structure and neuronal activity, respectively, and can serve as early indicators of AD onset and future progression. We propose V3D-MMoE, an interpretable framework to adaptively integrate incomplete multimodal 3D neuroimaging for AD diagnosis prediction and biomarker discovery. It goes beyond prior approaches by leveraging (1) a sparse mixture-of-experts formulation to account for variation in the importance of different modality combinations, (2) modality alignment to enhance cross-modal learning, and (3) cross-encoders to dynamically handle missing modalities. When applied to MRI and PET scans to predict two-year AD diagnosis, V3D-MMoE outperformed state-of-the-art multimodal 3D neuroimaging methods. Interpretability analyses revealed subject-specific MRI and PET biomarkers consistent with the known biology of AD. Ablation experiments demonstrated the benefit of leveraging multimodal neuroimaging.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"67-76"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280061","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

Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment. 从多模态训练到单模态部署：在训练期间利用非结构化数据来优化仅结构化数据的部署。

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

Zigui Wang, Minghui Sun, Jiang Shu, Matthew M Engelhard, Lauren Franz, Benjamin A Goldstein

{"title":"Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment.","authors":"Zigui Wang, Minghui Sun, Jiang Shu, Matthew M Engelhard, Lauren Franz, Benjamin A Goldstein","doi":"","DOIUrl":"","url":null,"abstract":"Unstructured Electronic Health Record (EHR) data-such as clinical notes-contain clinical contextual observations that are not directly reflected in structured data fields. This additional information can substantially improve model learning. However, due to their unstructured nature, these data are often unavailable or impractical to use when deploying a model. We introduce a multimodal learning framework that leverages unstructured EHR data during training while producing a model that can be deployed using only structured EHR data. Using a cohort of 3,466 children evaluated for late talking, we generated note embeddings with BioClinicalBERT and encoded structured embeddings from demographics and medical codes. A note-based teacher model and a structured-only student model were jointly trained using contrastive learning and contrastive knowledge distillation loss, producing a strong classifier (AUROC = 0.985). Our proposed model reached AUROC of 0.705-outperforming the structured-only baseline of 0.656. These results demonstrate that incorporating unstructured data during training enhances the model's capacity to identify task-relevant information within structured EHR data, enabling a deployable structured- only phenotype model.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"486-495"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280226","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

Exploring Anti-Aging Literature via ConvexTopics and Large Language Models. 通过凸主题和大语言模型探索抗衰老文献。

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

Lana E Yeganova, Won G Kim, Shubo Tian, Natalie Xie, Donald C Comeau, W John Wilbur, Zhiyong Lu

{"title":"Exploring Anti-Aging Literature via ConvexTopics and Large Language Models.","authors":"Lana E Yeganova, Won G Kim, Shubo Tian, Natalie Xie, Donald C Comeau, W John Wilbur, Zhiyong Lu","doi":"","DOIUrl":"","url":null,"abstract":"The rapid expansion of biomedical publications creates challenges for organizing knowledge and detecting emerging trends, underscoring the need for scalable and interpretable methods. Common clustering and topic modeling approaches such as K-means or LDA remain sensitive to initialization and prone to local optima, limiting reproducibility and evaluation. We propose a reformulation of a convex-optimization-based clustering algorithm that produces stable, fine-grained topics by selecting exemplars from the data and guaranteeing a global optimum. Applied to ~12,000 PubMed articles on aging and longevity, our method uncovers topics validated by medical experts. It yields interpretable topics spanning from molecular mechanisms to dietary supplements, physical activity, and gut microbiota. The method performs favorably, and most importantly, its reproducibility and interpretability distinguish it from common clustering approaches, including K-means, LDA, and BERTopic. This work provides a basis for developing scalable, web-accessible tools for knowledge discovery.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"554-563"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280214","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

TumorOriginPredictor: A Clinically Validated and Cloud-Deployed AI Platform for Tissue-of-Origin Identification in Cancer of Unknown Primary Using Somatic Mutation Profiles. TumorOriginPredictor：一个临床验证和云部署的人工智能平台，用于使用体细胞突变谱识别未知原发癌症的组织起源。

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

Saicharan Vellanki, Paraic A Kenny

{"title":"TumorOriginPredictor: A Clinically Validated and Cloud-Deployed AI Platform for Tissue-of-Origin Identification in Cancer of Unknown Primary Using Somatic Mutation Profiles.","authors":"Saicharan Vellanki, Paraic A Kenny","doi":"","DOIUrl":"","url":null,"abstract":"Cancer of Unknown Primary (CUP) remains one of the deadliest diagnostic challenges in oncology, accounting for 3-5% of all cancer diagnoses in the United States. Tumor origin is essential for guiding treatment; however, histological methods often fail in metastatic cases due to an inconclusive origin, leading to toxic and ineffective empirical therapies. Somatic mutations offer a promising alternative for accurate diagnosis via genomic alterations. We developed TumorOriginPredictor, a machine learning platform trained on 10,945 MSK-IMPACT mutation profiles. Five models generate the top three predicted tumor origins with rank-ordered probabilities, ensuring interpretability and clinical trust. Evaluated as a single platform, TumorOriginPredictor achieved over 72% top-3 accuracy across 12 cancer types and exceeded 80% for prevalent cancers. Clinically validated with 770 real-world profiles and deployed on Azure cloud, it provides real-time predictions, promoting individualized treatment by eliminating empirical therapies, reducing costs and time, and improving survival through a scalable, trustworthy AI platform.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"478-485"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279935","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 Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials. 确定非小细胞肺癌免疫治疗试验ehr可预测资格标准的多模型LLM共识框架

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

Abdul Muqeeth, Yu Huang, Jiang Bian, Hao Liu, Yan Zhuang

{"title":"A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.","authors":"Abdul Muqeeth, Yu Huang, Jiang Bian, Hao Liu, Yan Zhuang","doi":"","DOIUrl":"","url":null,"abstract":"Clinical trial participation in oncology remains low, with complex and granular eligibility criteria limiting timely accrual and excluding many patients who could otherwise benefit from novel therapies. We propose a structured framework that utilizes a multi-model large language model consensus pipeline to convert free-text eligibility from Phase III PD-1/PD-L1 non-small cell lung cancer trials into standardized umbrella concepts and to classify each concept into tiers of Electronic Health Record predictability. Applying this framework, we consolidated hundreds of heterogeneous eligibility traits into a manageable set of clinical concepts and found that roughly half are both clinically important and plausibly inferable from routine structured and unstructured EHR data, while the remainder either add little value to prediction or depend on specialized testing. This taxonomy provides a prioritized \"predictable target\" list for future NLP and machine-learning models and a practical blueprint for designing more computable, pragmatic eligibility criteria and EHR-driven prescreening tools.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"333-342"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280095","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

Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation. 基础模型引导的合成EHR释放：性能增强与隐私保护。

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

Rui Zhu, Xiaopu Zhou, Ivy Liang, Stephen W Scherer, Ke Xu, Lucila Ohno-Machado

{"title":"Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.","authors":"Rui Zhu, Xiaopu Zhou, Ivy Liang, Stephen W Scherer, Ke Xu, Lucila Ohno-Machado","doi":"","DOIUrl":"","url":null,"abstract":"Machine learning on electronic health records (EHR) is constrained by limited data, strict privacy regulations, and distribution shifts across sites and time. Synthetic data offer a potential solution, but existing tabular generators often struggle in the small sample size in EHR regime. We propose MEDPFN, a distribution-aware synthetic EHR generator built on top of the TabPFN tabular foundation model. MEDPFN uses TabPFN as a learned compatibility score over EHR and adds a Mahalanobis regularizer that anchors generated samples to realistic feature statistics, stabilizing generation from small reference cohorts. On six public tabular EHR datasets, we treat the original data only as private context, train standard classifiers solely on MEDPFN-generated samples, and evaluate on held-out real patients. Across datasets and models, MEDPFN-based training matches or exceeds real-data baselines and outperforms several state-of-the-art synthetic generators, enabling high-utility, privacy-friendly EHR data release.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"614-622"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280145","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

Advancing Clinical Trial Efficiency and Data Accuracy through Direct EHR-to-EDC Integration. 通过直接ehr - edc集成提高临床试验效率和数据准确性。

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

Maryam Y Garza, Muayad Hamidi, Iddo Peleg, Sapir Balas, Byeong Yeob Choi, Meredith N Zozus

{"title":"Advancing Clinical Trial Efficiency and Data Accuracy through Direct EHR-to-EDC Integration.","authors":"Maryam Y Garza, Muayad Hamidi, Iddo Peleg, Sapir Balas, Byeong Yeob Choi, Meredith N Zozus","doi":"","DOIUrl":"","url":null,"abstract":"For study sponsors, clinical trial efficiency and data accuracy are non-negotiable drivers of success. Despite broad digitization in healthcare, redundant data entry persists, and the current reliance on manual transcription from electronic health records (EHRs) into electronic data capture (EDC) systems creates unnecessary complexity- leading to higher costs, site burden, and data quality risks. With the growing maturity of interoperability standards and frameworks, direct EHR-to-EDC integration offers a transformative opportunity to automate data transfer, improve accuracy, and streamline operations. This paper evaluates the impact of direct EHR-to-EDC integration on data quality compared to traditional workflows. Our findings in a multi-study, multi-site assessment demonstrate that manual transcription resulted in an error rate of 8.23% (95% CI: 7.79-8.70), whereas no transcription errors were detected in the integrated EHR-to-EDC workflow (95% CI: 0.00-0.03). These results underscore the potential of interoperability-based automation to improve data reliability and operational efficiency in clinical research.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"147-152"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280056","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