AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第9页

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

A Generalized Tool to Assess Algorithmic Fairness in Disease Phenotype Definitions. 一种评估疾病表型定义算法公平性的通用工具。

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

Jacob S Zelko, Justin Manjourides

引用次数: 0

Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation. 生成式人工智能尚未准备好用于下背部疼痛患者的临床患者教育，即使有检索增强生成。

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

Yi-Fei Zhao, Allyn Bove, David Thompson, James Hill, Yi Xu, Yufan Ren, Andrea Hassman, Leming Zhou, Yanshan Wang

{"title":"Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation.","authors":"Yi-Fei Zhao, Allyn Bove, David Thompson, James Hill, Yi Xu, Yufan Ren, Andrea Hassman, Leming Zhou, Yanshan Wang","doi":"","DOIUrl":"","url":null,"abstract":"Low back pain (LBP) is a leading cause of disability globally. Following the onset of LBP and subsequent treatment, adequate patient education is crucial for improving functionality and long-term outcomes. Despite advancements in patient education strategies, significant gaps persist in delivering personalized, evidence-based information to patients with LBP. Recent advancements in large language models (LLMs) and generative artificial intelligence (GenAI) have demonstrated the potential to enhance patient education. However, their application and efficacy in delivering educational content to patients with LBP remain underexplored and warrant further investigation. In this study, we introduce a novel approach utilizing LLMs with Retrieval-Augmented Generation (RAG) and few-shot learning to generate tailored educational materials for patients with LBP. Physical therapists manually evaluated our model responses for redundancy, accuracy, and completeness using a Likert scale. In addition, the readability of the generated education materials is assessed using the Flesch Reading Ease score. The findings demonstrate that RAG-based LLMs outperform traditional LLMs, providing more accurate, complete, and readable patient education materials with less redundancy. Having said that, our analysis reveals that the generated materials are not yet ready for use in clinical practice. This study underscores the potential of AI-driven models utilizing RAG to improve patient education for LBP; however, significant challenges remain in ensuring the clinical relevance and granularity of content generated by these models.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"644-653"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276848","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

Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare. 将健康的社会决定因素整合到知识图谱中：评估医疗保健中的预测偏差和公平性。

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

Tianqi Shang, Weiqing He, Tianlong Chen, Ying Ding, Huanmei Wu, Kaixiong Zhou, Li Shen

{"title":"Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare.","authors":"Tianqi Shang, Weiqing He, Tianlong Chen, Ying Ding, Huanmei Wu, Kaixiong Zhou, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formulation for graph embeddings, focusing on invariance with respect to sensitive SDoH information. Via employing a heterogeneous-GCN model for drug-disease link prediction, we detect biases related to various SDoH factors. To mitigate these biases, we propose a post-processing method that strategically reweights edges connected to SDoHs, balancing their influence on graph representations. This approach represents one of the first comprehensive investigations into fairness issues within biomedical knowledge graphs incorporating SDoH. Our work not only highlights the importance of considering SDoH in medical informatics but also provides a concrete method for reducing SDoH-related biases in link prediction tasks, paving the way for more equitable healthcare recommendations. Our code is available at https://github.com/hwq0726/SDoH-KG.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"481-490"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276853","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

Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth. 预测青少年脑震荡后心理健康后遗症的机器学习模型比较

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

Jin Peng, Jiayuan Chen, Changchang Yin, Ping Zhang, Jingzhen Yang

引用次数: 0

Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases. 机器学习在糖尿病和心脏病临床支持中的不同模型性能和稳定性。

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

Ioannis Bilionis, Ricardo C Berrios, Luis Fernandez-Luque, Carlos Castillo

{"title":"Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases.","authors":"Ioannis Bilionis, Ricardo C Berrios, Luis Fernandez-Luque, Carlos Castillo","doi":"","DOIUrl":"","url":null,"abstract":"Machine Learning (ML) algorithms are vital for supporting clinical decision-making in biomedical informatics. However, their predictive performance can vary across demographic groups, often due to the underrepresentation of historically marginalized populations in training datasets. The investigation reveals widespread sex- and age-related inequities in chronic disease datasets and their derived ML models. Thus, a novel analytical framework is introduced, combining systematic arbitrariness with traditional metrics like accuracy and data complexity. The analysis of data from over 25,000 individuals with chronic diseases revealed mild sex-related disparities, favoring predictive accuracy for males, and significant age-related differences, with better accuracy for younger patients. Notably, older patients showed inconsistent predictive accuracy across seven datasets, linked to higher data complexity and lower model performance. This highlights that representativeness in training data alone does not guarantee equitable outcomes, and model arbitrariness must be addressed before deploying models in clinical settings.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"95-104"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276864","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

Predicting survival time for critically ill patients with heart failure using conformalized survival analysis. 应用符合化生存分析预测危重心衰患者的生存时间。

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

Xiaomeng Wang, Zhimei Ren, Jiancheng Ye

{"title":"Predicting survival time for critically ill patients with heart failure using conformalized survival analysis.","authors":"Xiaomeng Wang, Zhimei Ren, Jiancheng Ye","doi":"","DOIUrl":"","url":null,"abstract":"Heart failure (HF) is a significant public health challenge, especially among critically ill patients in intensive care units (ICUs). Predicting survival outcomes for these patients with calibrated uncertainty is both challenging and essential for guiding subsequent treatments. This study introduces conformalized survival analysis (CSA) as a novel method for predicting survival times in critically ill HF patients. CSA enhances each predicted survival time with a statistically rigorous lower bound, providing valuable uncertainty quantification. Using the MIMIC-IV dataset, we demonstrate that CSA effectively delivers calibrated uncertainty quantification for survival predictions, in contrast to parametric models like the Cox or Accelerated Failure Time models. Through the application of CSA to a large, real-world dataset, this study underscores its potential to improve decision-making in critical care, offering a more precise and reliable tool for prognosis in a setting where accurate predictions and calibrated uncertainty can profoundly impact patient outcomes.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"576-597"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276880","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 Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports. 面向PubMed病例报告相对时间线提取的大语言模型框架。

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

Jing Wang, Jeremy C Weiss

{"title":"A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports.","authors":"Jing Wang, Jeremy C Weiss","doi":"","DOIUrl":"","url":null,"abstract":"Timing of clinical events is central to characterization of patient trajectories, enabling analyses such as process tracing, forecasting, and causal reasoning. However, structured electronic health records capture few data elements critical to these tasks, while clinical reports lack temporal localization of events in structured form. We present a system that transforms case reports into textual time series-structured pairs of textual events and timestamps. We contrast manual and large language model (LLM) annotations (n=320 and n=390 respectively) of ten randomly-sampled PubMed open-access (PMOA) case reports (N=152,974) and assess inter-LLM agreement (n=3,103 N=93). We find that the LLM models have moderate event recall (O1-preview: 0.80) but high temporal concordance among identified events (O1-preview: 0.95). By establishing the task, annotation, and assessment systems, and by demonstrating high concordance, this work may serve as a benchmark for leveraging the PMOA corpus for temporal analytics. Code is available at:https://github.com/jcweiss2/LLM-Timeline-PMOA/.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"598-606"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276832","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

EntroLLM: Leveraging Entropy and Large Language Model Embeddings for Enhanced Risk Prediction with Wearable Device Data. 利用熵和大语言模型嵌入增强可穿戴设备数据的风险预测。

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

Xueqing Huang, Tian Gu

{"title":"EntroLLM: Leveraging Entropy and Large Language Model Embeddings for Enhanced Risk Prediction with Wearable Device Data.","authors":"Xueqing Huang, Tian Gu","doi":"","DOIUrl":"","url":null,"abstract":"Wearable devices collect complex structured data with high-dimensional and time-series features that are challenging for traditional models to handle efficiently. We propose EntroLLM, a new method that combines entropy measures and the low-dimensional representation (embedding) generated from large language models (LLMs) to enhance risk prediction using wearable device data. In EntroLLM, the entropy quantifies the variability of a subject's physical activity patterns, while the LLM embedding approximates the latent temporal structure. We evaluate the feasibility and performance of EntroLLM using NHANES data to predict overweight status using demographics and physical activity collected from wearable devices. Results show that combining entropy with GPT-based embedding improves model performance compared to baseline models and other embedding techniques, leading to an average increase in AUC from 0.56 to 0.64. EntroLLM showcases the potential of combining entropy and LLM-based embedding and offers a promising approach to wearable device data analysis for predicting health outcomes.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"225-234"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276870","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