AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第6页

Shifting Information Needs in Clinical Practice: The Evolving Role of Generative AI in Addressing Clinician Demands for Context-Specific Knowledge. 临床实践中不断变化的信息需求：生成式人工智能在解决临床医生对特定情境知识需求方面的演变作用。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Sachleen K Tuteja, Elise L Boventer, Abdulaziz Alkattan, Noemie Elhadad, Sarah C Rossetti

{"title":"Shifting Information Needs in Clinical Practice: The Evolving Role of Generative AI in Addressing Clinician Demands for Context-Specific Knowledge.","authors":"Sachleen K Tuteja, Elise L Boventer, Abdulaziz Alkattan, Noemie Elhadad, Sarah C Rossetti","doi":"","DOIUrl":"","url":null,"abstract":"This study explores clinicians' evolving information needs and evaluates the potential of Generative Artificial Intelligence (Gen AI) to address these gaps by reassessing and extending the Currie et al. (2003) taxonomy. Despite advancements in electronic health records (EHRs), unresolved information needs persist, impacting clinical efficiency and patient care. A cross-sectional survey conducted at Columbia University Irving Medical Center (CUIMC) analyzed clinician-generated Gen AI prompts, comparing them against the 2003 taxonomy. Findings reveal that while 80% of prompts align with existing categories, 20% represent emerging needs, including AI-driven workflow optimization and fairness-related inquiries. These findings highlight the necessity of adapting clinical decision support frameworks to integrate AI-driven solutions, ensuring that modern tools meet evolving clinician needs. By formally extending the Currie et al. taxonomy, this study provides a foundational framework for leveraging Gen AI to bridge long-standing information gaps and enhance patient outcomes in an increasingly complex healthcare environment.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1277-1284"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273076","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

Automating Adjudication of Cardiovascular Events Using Large Language Models. 使用大型语言模型自动判定心血管事件。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Sonish Sivarajkumar, Kimia Ameri, Chuqin Li, Yanshan Wang, Min Jiang

{"title":"Automating Adjudication of Cardiovascular Events Using Large Language Models.","authors":"Sonish Sivarajkumar, Kimia Ameri, Chuqin Li, Yanshan Wang, Min Jiang","doi":"","DOIUrl":"","url":null,"abstract":"Cardiovascular event adjudication is essential in clinical trials but relies on manual chart review that is slow, variable, and expensive. We present a two-stage framework that automates adjudication of cardiovascular deaths using large language models (LLMs). First, a few-shot LLM extracts structured evidence (event, span, negation, date) from unstructured clinical documents. Second, a Tree-of-Thoughts adjudicator aligns its reasoning with clinical endpoint committee (CEC) guidelines to classify deaths as cardiovascular or non-cardiovascular and produce an auditable rationale. On Lilly clinical-trial data, extraction achieved precision 0.96, recall 0.71 (F1 0.82), and adjudication attained 0.68 accuracy (GPT-4 ToT), outperforming a summarizer-plus-adjudicator baseline. We introduce CLEART, a rubric-based automated score that quantifies rationale quality across clarity, consistency, detail, guideline adherence, relevance, and timeline accuracy (overall 0.67), highlighting temporal reasoning and relevance as key areas for improvement. This approach can reduce adjudication time and variability while increasing transparency.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1219-1228"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273197","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

Leveraging Cluster Causal Diagrams for Determining Causal Effects in Medicine. 利用聚类因果图确定医学中的因果效应。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Tara V Anand, George Hripcsak

引用次数: 0

Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations. 从临床对话内容中自动检测有偏见的社会信号。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Feng Chen, Manas Satish Bedmutha, Ray-Yuan Chung, Janice Sabin, Wanda Pratt, Brian R Wood, Nadir Weibel, Andrea L Hartzler, Trevor Cohen

{"title":"Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations.","authors":"Feng Chen, Manas Satish Bedmutha, Ray-Yuan Chung, Janice Sabin, Wanda Pratt, Brian R Wood, Nadir Weibel, Andrea L Hartzler, Trevor Cohen","doi":"","DOIUrl":"","url":null,"abstract":"Implicit bias can impede patient-provider interactions and lead to inequities in care. Raising awareness is key to reducing such bias, but its manifestations in the social dynamics of patient-provider communication are difficult to detect. In this study, we used automated speech recognition (ASR) and natural language processing (NLP) to identify social signals in patient-provider interactions. We built an automated pipeline to predict social signals from audio recordings of 782 primary care visits that achieved 90.1% average accuracy across codes, and exhibited fairness in its predictions for white and non-white patients. Applying this pipeline, we identified statistically significant differences in provider communication behavior toward white versus non-white patients. In particular, providers expressed more patient-centered behaviors towards white patients including more warmth, engagement, and attentiveness. Our study underscores the potential of automated tools in identifying subtle communication signals that may be linked with bias and impact healthcare quality and equity.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"252-261"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144811","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

Cryptogenic Stroke and Migraine: Using Probabilistic Independence and Machine Learning to Uncover Latent Sources of Disease from the Electronic Health Record. 隐源性中风和偏头痛：利用概率独立性和机器学习从电子健康记录中发现潜在的疾病来源。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Joshua W Betts, John M Still, Thomas A Lasko

引用次数: 0

Global Relevance of Online Health Information Sources: A Case Study of Experiences and Perceptions of Nigerians. 在线卫生信息源的全球相关性：尼日利亚人的经验和看法的案例研究。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Ommo Clark, Karuna P Joshi, Tera L Reynolds

引用次数: 0

Development of a Flexible Chain of Thought Framework for Automated Routing of Patient Portal Messages. 一个灵活的思想链框架的开发，用于患者门户消息的自动路由。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Michael Gao, Kartik Pejavara, Suresh Balu, Ricardo Henao

{"title":"Development of a Flexible Chain of Thought Framework for Automated Routing of Patient Portal Messages.","authors":"Michael Gao, Kartik Pejavara, Suresh Balu, Ricardo Henao","doi":"","DOIUrl":"","url":null,"abstract":"The increase in utilization of patient portal messages has imposed a considerable burden on healthcare providers, contributing to an increased incidence of provider burnout. This study introduces a framework for leveraging Large Language Models (LLMs) and Chain-of-Thought (CoT) prompting in order to automatically categorize and route messages to their appropriate location. The modeling framework, which utilizes gold standard annotations from triage nurses, not only facilitates the dynamic adaptation of the model to evolving healthcare workflows and emerging edge-case scenarios, but also significantly improves the model's classification accuracy compared to traditional zero-shot methods. In addition, the framework allows for flexibility in its task and continuous improvement via annotation of exemplar messages. The model is able to accurately categorize messages in an automated fashion, which has potential to dramatically ease the burden on providers and provide faster and safer responses to patients. This framework can also be readily extended to work in a variety of clinical and documentation settings.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"443-452"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144455","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

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI. 确保MRI检测轻度认知障碍的公平性。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Boning Tong, Travyse Edwards, Shu Yang, Bojian Hou, Davoud Ataee Tarzanagh, Ryan J Urbanowicz, Jason H Moore, Marylyn D Ritchie, Christos Davatzikos, Li Shen

{"title":"Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.","authors":"Boning Tong, Travyse Edwards, Shu Yang, Bojian Hou, Davoud Ataee Tarzanagh, Ryan J Urbanowicz, Jason H Moore, Marylyn D Ritchie, Christos Davatzikos, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective treatment planning. However, existing methods are not well-suited for identifying Mild Cognitive Impairment (MCI), a critical transitional stage between normal aging and AD. This inadequacy is primarily due to label imbalance and bias from different sensitve attributes in MCI classification. To overcome these challenges, we have designed an end-to-end fairness-aware approach for label-imbalanced classification, tailored specifically for neuroimaging data. This method, built on the recently developed FACIMS framework, integrates into STREAMLINE, an automated ML environment. We evaluated our approach against nine other ML algorithms and found that it achieves comparable balanced accuracy to other methods while prioritizing fairness in classifications with five different sensitive attributes. This analysis contributes to the development of equitable and reliable ML diagnostics for MCI detection.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1119-1128"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144559","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

Publication Type Tagging using Transformer Models and Multi-Label Classification. 使用转换器模型和多标签分类的出版物类型标记。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Joe D Menke, Halil Kilicoglu, Neil R Smalheiser

{"title":"Publication Type Tagging using Transformer Models and Multi-Label Classification.","authors":"Joe D Menke, Halil Kilicoglu, Neil R Smalheiser","doi":"","DOIUrl":"","url":null,"abstract":"Indexing articles by their publication type and study design is essential for efficient search and filtering of the biomedical literature, but is understudied compared to indexing by MeSH topical terms. In this study, we leveraged the human-curated publication types and study designs in PubMed to generate a dataset of more than 1.2M articles (titles and abstracts) and used state-of-the-art Transformer-based models for automatic tagging of publication types and study designs. Specifically, we trained PubMedBERT-based models using a multi-label classification approach, and explored undersampling, feature verbalization, and contrastive learning to improve model performance. Our results show that PubMedBERT provides a strong baseline for publication type and study design indexing; undersampling, feature verbalization, and unsupervised constrastive loss have a positive impact on performance, whereas supervised contrastive learning degrades the performance. We obtained the best overall performance with 80% undersampling and feature verbalization (0.632 macro-F1, 0.969 macro-AUC). The model outperformed previous models (MultiTagger) across all metrics and the performance difference was statistically significant (p < 0.001). Despite its stronger performance, the model still has room for improvement and future work could explore features based on full-text as well as model interpretability. We make our data and code available at https://github.com/ScienceNLP-Lab/MultiTagger-v2/tree/main/AMIA.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"818-827"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144709","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

Exposing Vulnerabilities in Clinical LLMs Through Data Poisoning Attacks: Case Study in Breast Cancer. 通过数据中毒攻击暴露临床法学硕士的漏洞：乳腺癌案例研究。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Avisha Das, Amara Tariq, Felipe Batalini, Boddhisattwa Dhara, Imon Banerjee

引用次数: 0