AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第5页

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

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

Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program. 使用我们所有人研究项目的基于深度学习的抑郁症和哮喘事件时间分析。

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

Xueting Wang, Lucila Ohno-Machado, Jose L Gomez, Wen Gu, Rongyi Sun, Jihoon Kim

引用次数: 0

Development and Usability Testing of a Web-Based Research Guide for Health Solutions Grant Writing. 基于网络的健康解决方案研究指南的开发和可用性测试。

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

Maheswari Eluru, Aishwarya S Potturu, Matthew Scotch, Lisa Allen, Nancy Osgood, Ana Tello, Adela Grando

{"title":"Development and Usability Testing of a Web-Based Research Guide for Health Solutions Grant Writing.","authors":"Maheswari Eluru, Aishwarya S Potturu, Matthew Scotch, Lisa Allen, Nancy Osgood, Ana Tello, Adela Grando","doi":"","DOIUrl":"","url":null,"abstract":"Young scientists, including postdocs and assistant professors, need access to grant writing resources for training and proposal development. To assist in this, we developed a web-based research guide providing centralized access to curated tools throughout the research funding process- finding funding, preparing proposals, managing awards, etc. Using consumer informatics principles, we enhanced the research grant repository's effectiveness, with lessons learned and insights generalizable to other institutions. Six faculty members completed nine tasks to explore the guide's ten sections. Participants found the guide highly usable, with an excellent System Usability Scale (SUS) score of 89.2. Suggestions included improving navigation, content organization and providing education on award management processes. Liked features were the chronological organization of information, samples from successful grants, pre-populated templates, and mechanisms for ongoing feedback. These findings underscore the importance of usability in developing resources that effectively support faculty in grant writing and proposal development.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"378-387"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144452","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

Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection. 增强基因表达谱的元学习增强肺癌检测。

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

Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Cuncong Zhong, Zijun Yao

{"title":"Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection.","authors":"Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Cuncong Zhong, Zijun Yao","doi":"","DOIUrl":"","url":null,"abstract":"Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodologies such as deep neural networks for sophisticated analysis. To address this \"small data\" dilemma, Meta-Learning has been introduced as a solution to enhance the optimization of machine learning models by utilizing similar datasets, thereby facilitating a quicker adaptation to target datasets without the requirement of sufficient samples. In this study, we present a meta-learning-based approach for predicting lung cancer from gene expression profiles. We apply this framework to well-established deep learning methodologies and employ four distinct datasets for the meta-learning tasks, where one as the target dataset and the rest as source datasets. Our approach is evaluated against both traditional and deep learning methodologies, and the results show the superior performance of meta-learning on augmented source data compared to the baselines trained on single datasets. Moreover, we conduct the comparative analysis between meta-learning and transfer learning methodologies to highlight the efficiency of the proposed approach in addressing the challenges associated with limited sample sizes. Finally, we incorporate the explainability study to illustrate the distinctiveness of decisions made by meta-learning.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"828-837"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144606","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

Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning. ICU中风患者更好的血压控制：一种深度强化学习与监督指导的自适应输液速率调节方法。

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

Kun-Yi Chen, Adnan I Qureshi, William I Baskett, Chi-Ren Shyu

{"title":"Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.","authors":"Kun-Yi Chen, Adnan I Qureshi, William I Baskett, Chi-Ren Shyu","doi":"","DOIUrl":"","url":null,"abstract":"Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (ICUs), where higher BPV correlates with increased mortality rates. Current interventions lack effective methods for controlling BPV across consecutive time windows. To addressing this gap, we propose an offline deep reinforcement learning approach with supervised guidance to regulate systolic BPV in the following consecutive time windows by optimizing intravenous nicardipine infusion rates for intracerebral hemorrhage patients. Using clinically inspired reward functions, our method aims to tailor antihypertensive medication management within the critical 24-hour recovery window. Compared to human performance, our best method showed 57.52% and 126.01% improvements over the human baseline for maintaining BP within the desired range for the next time window and across two consecutive time windows. This research promises streamlined antihypertensive medication dosing, offering potential just-in-time adaptive interventions through automated pumps during stroke patients' ICU stays.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"271-280"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144627","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

Neural Granger Causal Discovery for Derangements in ICU-Acquired Acute Kidney Injury Patients. 重症监护下获得性急性肾损伤患者神经紊乱的Granger因果发现。

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

Haowei Xu, Wentie Liu, Tongyue Shi, Guilan Kong

{"title":"Neural Granger Causal Discovery for Derangements in ICU-Acquired Acute Kidney Injury Patients.","authors":"Haowei Xu, Wentie Liu, Tongyue Shi, Guilan Kong","doi":"","DOIUrl":"","url":null,"abstract":"Nowadays, healthcare systems increasingly utilize automated surveillance of electronic medical record (EMR) data to detect adverse events with specific patterns. Despite these technological advances, the early identification of adverse events remains challenging due to the absence of clearly defined prodromal sequences that could signal the onset of such events. Achieving clinically meaningful and interpretable prediction outcomes necessitates a framework that is capable of (i) deducing temporal relationships among various time series features within EMR data (e.g., laboratory test results, vital signs), and (ii) identifying specific patterns that herald the occurrence of an adverse event (e.g., acute kidney injury (AKI)). This study employs a time series forecasting approach to undertake neural Granger causal analysis, and further enhance it by integrating a personalized PageRank algorithm to analyze the critical causal derangements among ICU-acquired AKIpatients. An experimental analysis based on the proposed methodology was conducted using a dataset from MIMIC-IV.Finally, a Granger causality (GC) graph, which revealed several interpretable GC chains that could be used to predict the occurrence ofAKI in ICU settings, was generated. The GC graph and GC chains identified in this study have the potential to aid ICU physicians in providing timely interventions and may help improve patient outcomes.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1265-1274"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144636","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

Harnessing the Power of Large Language Models (LLMs) to Unravel the Influence of Genes and Medications on Biological Processes of Wound Healing. 利用大语言模型（LLMs）的力量来揭示基因和药物对伤口愈合生物过程的影响。

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

Jayati H Jui, Milos Hauskrecht

{"title":"Harnessing the Power of Large Language Models (LLMs) to Unravel the Influence of Genes and Medications on Biological Processes of Wound Healing.","authors":"Jayati H Jui, Milos Hauskrecht","doi":"","DOIUrl":"","url":null,"abstract":"Recent advancements in Large Language Models (LLMs) have ushered in a new era for knowledge extraction in the domains of biological and clinical natural language processing (NLP). In this research, we present a novel approach to understanding the regulatory effects of genes and medications on biological processes central to wound healing. Utilizing the capabilities of Generative Pre-trained Transformer (GPT) models by OpenAI, specifically GPT-3.5 and GPT-4, we developed a comprehensive pipeline for the identification and grounding of biological processes and the extraction of such regulatory relations. The performances of both GPTs were rigorously evaluated against a manually annotated corpus of 104 PubMed titles, focusing on their ability to accurately identify and ground biological process concepts and extract relevant regulatory relationships from the text. Our findings demonstrate that GPT-4, in particular, exhibits superior performance in all the tasks, showcasing its potential to facilitate significant advancements in biomedical research without requiring model fine-tuning.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"571-580"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144649","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

An Interactive Web Application for School-Based Physical Fitness Testing in California: Geospatial Analysis and Custom Mapping. 加州校本体能测试的互动网路应用程式：地理空间分析与自订绘图。

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

Yawen Guo, Kaiyuan Hu, Di Hu, Kai Zheng, Dan M Cooper

{"title":"An Interactive Web Application for School-Based Physical Fitness Testing in California: Geospatial Analysis and Custom Mapping.","authors":"Yawen Guo, Kaiyuan Hu, Di Hu, Kai Zheng, Dan M Cooper","doi":"","DOIUrl":"","url":null,"abstract":"Physical activity is crucial for children's healthy growth and development. In the US, most states have physical education standards. California implemented the mandated School-based Physical Fitness Testing (SB-PFT) over two decades ago. Despite the substantial effort in collecting the SB-PFT data, its research reuse has been limited due to the lack of readily accessible analytical tools. We developed a web application utilizing GeoServer, ArcGIS, and AWS to visualize the SB-PFT data. Education administrators and policymakers can leverage this user-friendly platform to gain insights into children's physical fitness trend, and identify schools and districts with successful programs to gauge the success of new physical education programs. The application also includes a custom mapping tool that allows users to compare external datasets with SB-PFT. We conclude that by incorporating advanced analytical capabilities through an informatics-based user-facing tool, this platform has great potential to encourage a broader engagement in enhancing children's physical fitness.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"463-472"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144694","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

BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records. 电子健康记录临床语言模型中的后门攻击。

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

Weimin Lyu, Zexin Bi, Fusheng Wang, Chao Chen

引用次数: 0