AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第3页

Augmenting Missing Individual Social Determinants of Health with Area-Level Social Deprivation Index for Gene-Environment Interaction: Application to Opioid Use Disorder Prediction. 用区域社会剥夺指数增强缺失的个体健康社会决定因素，用于基因-环境相互作用：在阿片类药物使用障碍预测中的应用。

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

Yaxi Yang, Youwen Liu, Jihoon Kim

{"title":"Augmenting Missing Individual Social Determinants of Health with Area-Level Social Deprivation Index for Gene-Environment Interaction: Application to Opioid Use Disorder Prediction.","authors":"Yaxi Yang, Youwen Liu, Jihoon Kim","doi":"","DOIUrl":"","url":null,"abstract":"Detecting gene-environment (G×E) interactions is challenging when individual-level social determinants of health survey responses (iSR) are highly missing. We hypothesize that an area-level Social Deprivation Index (aSDI), derived from ZIP3 codes with <1% missingness, can augment or substitute for iSR variables, which exhibit ∼62% missingness in the All of Us Research Program. We developed a generalized linear mixed-effects model framework incorporating ZIP3-level random effects, assessed concordance between iSR and aSDI through correlation and quintile analyses, and evaluated G×E interactions between polygenic risk scores and aSDI. We applied this framework to prediction of opioid use disorder (OUD) in All of Us (N = 123,325 opioid-exposed adults). Composite quintile concordance between iSR and aSDI was moderate (r = 0.52). Models using aSDI alone (AUC = 0.822) performed comparably to iSR-only models (AUC = 0.825). Leveraging aSDI to address missing iSR expanded the analytic sample from ∼43,000 to ∼123,000 and enabled detection of significant gene-poverty and gene-income interactions. The full model integrating clinical, genetic, individual, and area-level social features achieved AUC = 0.835.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"546-553"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280053","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

Spatio-Temporal Modeling for Multi-County Opioid Overdose Surveillance: A Unified Graph Convolutional Framework. 多县阿片类药物过量监测的时空建模：一个统一的图卷积框架。

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

Dohyo Jeong, Daniel R Harris

引用次数: 0

Generative Transformers for Pharmacovigilance Signal Detection using Electronic Health Records. 基于电子健康记录的药物警戒信号检测生成变压器。

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

YiFan Wu, Ian De Boer, Trevor Cohen

{"title":"Generative Transformers for Pharmacovigilance Signal Detection using Electronic Health Records.","authors":"YiFan Wu, Ian De Boer, Trevor Cohen","doi":"","DOIUrl":"","url":null,"abstract":"Adverse drug reactions (ADRs) present substantial challenges to patient safety and to healthcare systems, often leading to hospitalizations and economic hardship. To mitigate these burdens, post-market surveillance is used to monitor ADRs after the conclusion of clinical trials. The commonly employed methods to estimate the strength of associations between drugs and ADRs from surveillance data include disproportionality metrics. However, these methods are limited in their ability to model temporal relationships indicating causality. Furthermore, they perform poorly when applied to comprehensive data from electronic health records (EHRs), failing to realize such data's potential to mitigate under-reporting and bias in ADR surveillance system data. To address these limitations, we propose novel methods using generative pre-trained transformers (GPT) for enhanced ADR signal detection in EHR data. On evaluations on data from two healthcare systems, the GPT models improved overall AUROC representing an absolute gain of 6-16 % over established baseline methods. This study highlights the potential of transformer-based models to advance pharmacovigilance by integrating comprehensive clinical data.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"516-525"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280181","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

Robust AI-ECG for Predicting Left Ventricular Systolic Dysfunction in Pediatric Congenital Heart Disease. 鲁棒AI-ECG预测小儿先天性心脏病左心室收缩功能障碍

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

Yuting Yang, Lorenzo Peracchio, Joshua Mayourian, John K Triedman, Timothy Miller, William G La Cava

{"title":"Robust AI-ECG for Predicting Left Ventricular Systolic Dysfunction in Pediatric Congenital Heart Disease.","authors":"Yuting Yang, Lorenzo Peracchio, Joshua Mayourian, John K Triedman, Timothy Miller, William G La Cava","doi":"","DOIUrl":"","url":null,"abstract":"Artificial intelligence-enhanced electrocardiogram (AI-ECG) has shown promise as an inexpensive, ubiquitous, and non-invasive screening tool to detect left ventricular systolic dysfunction in pediatric congenital heart disease. However, current approaches rely heavily on large-scale labeled datasets, which poses a major obstacle to the democratization of AI in hospitals where only limited pediatric ECG data are available. In this work, we propose a robust training framework to improve AI-ECG performance under low-resource conditions. Specifically, we introduce an on-manifold adversarial perturbation strategy for pediatric ECGs to generate synthetic samples that better reflect real-world signal variations. Building on this, we develop an uncertainty-aware adversarial training algorithm that is architecture-agnostic and enhances model robustness. Internal and external evaluation on real-world pediatric (n=178,495) and adult (n=100,000) datasets demonstrates that our method enables low-cost and reliable detection of left ventricular systolic dysfunction, highlighting its potential for deployment in resource-limited clinical settings.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"536-545"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280146","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

Assessing Multimodal AI for Visual Information Extraction of Pharmacology. 评估多模态人工智能在药理学视觉信息提取中的应用。

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

Israel O Dilan-Pantojas, Phu T Duong, Kevin Lopes, Richard D Boyce

{"title":"Assessing Multimodal AI for Visual Information Extraction of Pharmacology.","authors":"Israel O Dilan-Pantojas, Phu T Duong, Kevin Lopes, Richard D Boyce","doi":"","DOIUrl":"","url":null,"abstract":"While Americans are using herbal dietary supplements (natural products) more than ever, the consumption of natural products with prescription drugs can lead to harmful interactions. Pharmacovigilance of natural products depends on careful expert review and interpretation of a wide variety of evidence. In prior work, we demonstrated the value of knowledge graph (NP-KG) for assisting with natural product safety investigations. However, scaling the NP-KG from 33 natural products to the thousands on the market requires computer-assisted data extraction, particularly from visual elements (figures or tables) of pharmacology literature. We evaluated the accuracy and resilience of 3 open-weights and 7 closed-weights multimodal models by performing visual information extraction from select tables and images. The best performing models could accurately extract 90% of tabular data and 45% of data reported figures with a modified relative error rate of 0.05. Image resolution and information density were primary hindrances to better extraction performance.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"353-362"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280050","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

Large Models for Small Tables: Adapting Tabular Foundation Models to EHR Data. 小表格的大模型：适应电子病历数据的表格基础模型。

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

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

{"title":"Large Models for Small Tables: Adapting Tabular Foundation Models to EHR Data.","authors":"Rui Zhu, Xiaopu Zhou, Ivy Liang, Stephen W Scherer, Ke Xu, Lucila Ohno-Machado","doi":"","DOIUrl":"","url":null,"abstract":"Electronic Health Records (EHR) contain abundant structured tabular data, yet developing accurate predictive models on small clinical datasets remains challenging. Foundation models have transformed natural language and imaging tasks by leveraging pretraining on large-scale data, and they are increasingly explored in healthcare as a route toward generalist medical AI. However, this paradigm remains largely unexplored for structured EHR data. In this study, we explore the adaptation of a tabular foundation model to EHR predictive tasks. We fine-tune TabPFN, a Transformer-based model pre-trained on diverse synthetic tabular datasets, to capture complex patterns in clinical data that traditional models might miss. Our results demonstrate that the fine-tuned foundation model consistently out-performs conventional methods, achieving higher performance across tabular clinical prediction tasks and suggesting that large models for small tables can advance healthcare AI by combining rich prior knowledge with domain-specific fine-tuning.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"604-613"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280194","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

In silico design of an epitope-based vaccine for Rocio virus using phage display and E. coli expression system. 利用噬菌体展示和大肠杆菌表达系统设计罗西奥病毒表位疫苗。

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

Taarun Ganesh, Jarin Taslem Mourosi

引用次数: 0

ScreeningPaL: LLM-NLP Enabled Early Autism Detection Method from Caregiver's Free-Text Input. ScreeningPaL：基于看护者自由文本输入的LLM-NLP早期自闭症检测方法

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

Sumaiya Afroz Mila, Jeba Maliha, Md Rafiul Kabir, Ankan Ghosh, Sandip Ray

{"title":"ScreeningPaL: LLM-NLP Enabled Early Autism Detection Method from Caregiver's Free-Text Input.","authors":"Sumaiya Afroz Mila, Jeba Maliha, Md Rafiul Kabir, Ankan Ghosh, Sandip Ray","doi":"","DOIUrl":"","url":null,"abstract":"Identifying autism traits and detecting the spectrum of disorder early can substantially improve quality of life. We present a text-driven approach for early autism risk detection that analyzes caregiver-reported behavioral descriptions using advanced natural language processing techniques. Synthetic free-text generated from validated screening items is used to train multiple language models, which are then evaluated on an external benchmark dataset (TASD) to assess generalization under domain shift. Fine-tuned transformer models achieve the highest performance, reaching 90% accuracy, outperforming GPT and Gemini models, and conventional NLP baselines. Augmenting training datasets with noisy, realistic text further improves model performance, specifically recall in traditional pipelines, demonstrating the potential of noise-aware data augmentation for free-text screening. This methodology enables translational and low-cost early assessment without requiring structured questionnaires or speech samples. This approach provides early cues that may assist specialist evaluation, promote accessible and proactive developmental health monitoring.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"313-322"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280182","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

Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction. 基于可穿戴加速度计的健康预测中llm和时间序列基础模型的表示嵌入评估。

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

Soomin You, Tian Gu

{"title":"Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.","authors":"Soomin You, Tian Gu","doi":"","DOIUrl":"","url":null,"abstract":"Wearable accelerometer data capture rich behavioral signals relevant for personalized health, yet the comparative evidence on modern representation-learning approaches remains limited. Using accelerometer data from the National Health and Nutrition Examination Survey (NHANES), we evaluated three representation families for predicting multiple clinical outcomes: simple entropy-based features, pretrained large-language-model (LLM) embeddings, and time-series foundation model embeddings. Outcomes included overweight status, lipid biomarkers, glucose, arthritis, and cancers. Across all endpoints, entropy-based features consistently performed comparably to, and often slightly better than, embedding approaches. LLM-derived embeddings offered only marginal improvements (ΔAUC≈0.01-0.05), and time-series foundation model embeddings provided minimal value across varying sequence lengths. Prompt-based LLM reasoning performed worst (AUC≈0.56-0.65), demonstrating limited ability to infer quantitative physiological states from structured text. These results highlight the strength of simple variability features and underscore the need for domain-aligned pretraining in future time-series foundation models for health sensing.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"564-573"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280150","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 Knowledge Graph Approach To Discovering Drug Combination Therapies Across The Phenome. 跨现象组发现药物联合疗法的知识图谱方法。

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

Jianfeng Ke, Tingjian Ge, Rachel D Melamed

引用次数: 0