AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第9页

Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning. 利用电子健康记录数据和因果机器学习模拟一项评估选择性血清素再摄取抑制剂对抗微生物药物耐药性感染发展影响的目标试验。

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

Sarah E Ser, Urszula A Snigurska, Scott A Cohen, Inyoung Jun, Ragnhildur I Bjarnadottir, Robert J Lucero, Simone Marini, Jiang Bian, Mattia Prosperi

{"title":"Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning.","authors":"Sarah E Ser, Urszula A Snigurska, Scott A Cohen, Inyoung Jun, Ragnhildur I Bjarnadottir, Robert J Lucero, Simone Marini, Jiang Bian, Mattia Prosperi","doi":"","DOIUrl":"","url":null,"abstract":"Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly prescribed to treat depression, anxiety, and other psychiatric disorders, is increasing. Previous in vitro studies have shown that bacteria can become resistant to antibiotics when exposed to SSRIs. In this study, we emulated a target trial to estimate the effect of SSRI usage on the incidence of antibiotic-resistant infection. Our study population consisted of patients with mood, anxiety, or stress-related disorders, and a record of previous antimicrobial susceptibility testing or diagnosis of bacterial infection. Univariable, multivariable survival regression, and causal survival forest analyses all showed that patients treated with SSRIs had a higher risk of developing an antibiotic-resistant infection than those not treated with SSRIs. This study confirms the in vitro findings and may provide insights for future studies exploring the relationship of treatment with SSRIs and subsequent antibiotic-resistant infection.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"997-1004"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144576","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

Behavior Shifts in Patient Portal Usage During and After Policy Changes Around Test Result Delivery and Notification. 在测试结果传递和通知政策改变期间和之后，患者门户使用的行为变化。

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

Uday Suresh, Bryan D Steitz, S Trent Rosenbloom, Kevin N Griffith, Jessica S Ancker

引用次数: 0

Integrating AI into Clinical Workflows: A Simulation Study on Implementing AI-aided Same-day Diagnostic Testing Following an Abnormal Screening Mammogram. 将人工智能融入临床工作流程：在异常筛查乳房x线照片后实施人工智能辅助当日诊断测试的模拟研究。

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

Yannan Lin, Anne C Hoyt, Vladimir G Manuel, Moira Inkelas, Cleo K Maehara, Mehmet Ulvi Saygi Ayvaci, Mehmet Eren Ahsen, William Hsu

{"title":"Integrating AI into Clinical Workflows: A Simulation Study on Implementing AI-aided Same-day Diagnostic Testing Following an Abnormal Screening Mammogram.","authors":"Yannan Lin, Anne C Hoyt, Vladimir G Manuel, Moira Inkelas, Cleo K Maehara, Mehmet Ulvi Saygi Ayvaci, Mehmet Eren Ahsen, William Hsu","doi":"","DOIUrl":"","url":null,"abstract":"Artificial intelligence (AI) shows promise in clinical tasks, yet its integration into workflows remains underexplored. This study proposes an AI-aided same-day diagnostic imaging workup to reduce recall rates following abnormal screening mammograms and alleviate patient anxiety while waiting for the diagnostic examinations. Using discrete simulation, we found minimal disruption to the workflow (a 4% reduction in daily patient volume or a 2% increase in operating time) under specific conditions: operation from 9 am to 12 pm with all radiologists managing all patient types (screenings, diagnostics, and biopsies). Costs specific to the AI-aided same-day diagnostic workup include AI software expenses and potential losses from unused pre-reserved slots for same-day diagnostic workups. These simulation findings can inform the implementation of an AI-aided same-day diagnostic workup, with future research focusing on its potential benefits, including improved patient satisfaction, reduced anxiety, lower recall rates, and shorter time to cancer diagnoses and treatment.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"713-722"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144678","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

Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology. 人工智能辅助生物医学文献知识综合支持精准肿瘤学决策。

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

Ting He, Kory Kreimeyer, Mimi Najjar, Jonathan Spiker, Maria Fatteh, Valsamo Anagnostou, Taxiarchis Botsis

{"title":"Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology.","authors":"Ting He, Kory Kreimeyer, Mimi Najjar, Jonathan Spiker, Maria Fatteh, Valsamo Anagnostou, Taxiarchis Botsis","doi":"","DOIUrl":"","url":null,"abstract":"The delivery of effective targeted therapies requires comprehensive analyses of the molecular profiling of tumors and matching with clinical phenotypes in the context of existing knowledge described in biomedical literature, registries, and knowledge bases. We evaluated the performance of natural language processing (NLP) approaches in supporting knowledge retrieval and synthesis from the biomedical literature. We tested PubTator 3.0, Bidirectional Encoder Representations from Transformers (BERT), and Large Language Models (LLMs) and evaluated their ability to support named entity recognition (NER) and relation extraction (RE) from biomedical texts. PubTator 3.0 and the BioBERT model performed best in the NER task (best F1-score 0.93 and 0.89, respectively), while BioBERT outperformed all other solutions in the RE task (best F1-score 0.79) and a specific use case it was applied to by recognizing nearly all entity mentions and most of the relations. Our findings support the use of AI-assisted approaches in facilitating precision oncology decision-making.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"513-522"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144698","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

LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction. 基于llms的基于EHR的少针疾病预测：一种结合预测代理推理和关键代理指导的新方法。

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

Hejie Cui, Zhuocheng Shen, Jieyu Zhang, Hui Shao, Lianhui Qin, Joyce C Ho, Carl Yang

{"title":"LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction.","authors":"Hejie Cui, Zhuocheng Shen, Jieyu Zhang, Hui Shao, Lianhui Qin, Joyce C Ho, Carl Yang","doi":"","DOIUrl":"","url":null,"abstract":"Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and challenging to obtain. In this study, we investigate the feasibility of applying Large Language Models (LLMs) to convert structured patient visit data (e.g., diagnoses, labs, prescriptions) into natural language narratives. We evaluate the zero-shot and few-shot performance of LLMs using various EHR-prediction-oriented prompting strategies. Furthermore, we propose a novel approach that utilizes LLM agents with different roles: a predictor agent that makes predictions and generates reasoning processes and a critic agent that analyzes incorrect predictions and provides guidance for improving the reasoning of the predictor agent. Our results demonstrate that with the proposed approach, LLMs can achieve decent few-shot performance compared to traditional supervised learning methods in EHR-based disease predictions, suggesting its potential for health-oriented applications.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"319-328"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144602","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

Boosting Social Determinants of Health Extraction with Semantic Knowledge Augmented Large Language Model. 基于语义知识增强的大型语言模型促进健康提取的社会决定因素。

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

Lei Gong, Jaren Bresnick, Aidong Zhang, Cathy Wu, Kishlay Jha

{"title":"Boosting Social Determinants of Health Extraction with Semantic Knowledge Augmented Large Language Model.","authors":"Lei Gong, Jaren Bresnick, Aidong Zhang, Cathy Wu, Kishlay Jha","doi":"","DOIUrl":"","url":null,"abstract":"Social determinants of health (SDoH) significantly impacts health outcomes and contributes to perpetuating health disparities across healthcare applications. However, automatic extraction of SDoH information from Electronic Health Records (EHRs) is challenging due to the unstructured nature of clinical narratives that contain SDoH related information. Recent advances in Large Language Models (LLMs) have shown great promise for automated SDoH extraction. However, their performance suffers for the imbalanced SDoH categories due to the data scarcity issues. To address this, we propose an innovative approach that augments LLMs with semantic knowledge obtained from the Unified Medical Language Systems (UMLS). This strategy enriches the feature representations of imbalanced SDoH classes, leading to accurate SDoH extraction. More specifically, our proposed data augmentation strategy generates semantically enriched clinical narratives at the LLM pre-finetuning stage. This approach enables the LLM to better adapt to the target data and leads to a good initialization for the finetuning stage. Through extensive experiments using publicly available MIMIC-SDoH data, the proposed approach demonstrates significant improvement in results for the SDoH extraction, especially for the imbalanced classes.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"453-462"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144628","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

Identifying Stakeholder Requirements for the Development of an Electronic Care Transitions Tool to Improve Health Outcomes for Patients with Multiple Chronic Conditions. 确定利益相关者对电子护理过渡工具开发的需求，以改善多种慢性病患者的健康结果。

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

Hongyi Wu, Christian J Tejeda, Joanne Roman Jones, Allison B McCoy, Pamela M Garabedian, Lipika Samal, Patricia C Dykes

{"title":"Identifying Stakeholder Requirements for the Development of an Electronic Care Transitions Tool to Improve Health Outcomes for Patients with Multiple Chronic Conditions.","authors":"Hongyi Wu, Christian J Tejeda, Joanne Roman Jones, Allison B McCoy, Pamela M Garabedian, Lipika Samal, Patricia C Dykes","doi":"","DOIUrl":"","url":null,"abstract":"The transition from hospital to home can be a vulnerable and challenging period for patients, especially those living with multiple chronic conditions (MCC), as evidenced by their disproportionately high rates of readmission.1 Low health literacy, complexity of a new medication schedule, and \"post-hospital syndrome\" can all contribute to suboptimal adherence to discharge instructions.2 Timely and adequate support during transitional care has the potential to prevent adverse events and avoidable hospital readmissions. The use of mobile technology has been shown to improve health outcomes among those living with chronic illness by promoting self-management and adherence behavior.3 However, current digital interventions focus on the long-term management of a single chronic illness, failing to target the pivotal transition from hospital to home and to address the complex care needs required by those living with MCC. In this study, we describe the stakeholder requirement-gathering process used to inform the design of an EHR-integrated electronic tool to effectively address common care transition challenges for patients with MCC.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1255-1264"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144653","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 Visual Identification of Under-resourced Dermatological Diagnoses with Classifier-Steered Background Masking. 基于分类器导向背景掩蔽的资源不足皮肤病诊断的鲁棒视觉识别。

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

Miguel Dominguez, Julie Ryan Wolf, Paritosh Prasad, Wendemagegn Enbiale, Michael Gottlieb, Carl T Berdahl, Art Papier

{"title":"Robust Visual Identification of Under-resourced Dermatological Diagnoses with Classifier-Steered Background Masking.","authors":"Miguel Dominguez, Julie Ryan Wolf, Paritosh Prasad, Wendemagegn Enbiale, Michael Gottlieb, Carl T Berdahl, Art Papier","doi":"","DOIUrl":"","url":null,"abstract":"Collecting images of rare dermatological diseases for machine learning detection applications is a costly, laborious task. It is difficult to collect enough images of these diagnoses to avoid the risk of low accuracy \"in the wild\". One of the sources of bias in these networks is irrelevant background pixel data. These pixels necessarily have no clinical significance, yet Deep Neural Networks will make weak correlations based on that information. To reduce their ability to do this, we introduce a masking augmentation algorithm, InfoMax-Cutout. It employs unsupervised Information Maximization losses to mask out background pixels. InfoMax-Cutout increased accuracy on classifying 319 diagnoses by 0.76%. These features generalized to an unseen diagnosis task (Fitzpatrick 17k), improving accuracy over a baseline by 43.3% and reducing Gini inequality by 20.9%. This approach of learning to separate out background pixels can increase accuracy in detecting diseases in Lower and Middle Income Countries.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"368-377"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144724","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

OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier. 章鱼：基于磁盘，多平台，移动友好的宏基因组分类器。

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

Simone Marini, Alexander Barquero, Anisha Ashok Wadhwani, Jiang Bian, Jaime Ruiz, Christina Boucher, Mattia Prosperi

{"title":"OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier.","authors":"Simone Marini, Alexander Barquero, Anisha Ashok Wadhwani, Jiang Bian, Jaime Ruiz, Christina Boucher, Mattia Prosperi","doi":"","DOIUrl":"","url":null,"abstract":"Portable genomic sequencers such as Oxford Nanopore's MinION enable real-time applications in clinical and environmental health. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g., when cloud processing is unreachable due to absence of Internet connection, or only low-end computing devices can be carried on site. Here we present a platform-friendly software for portable metagenomic analysis of Nanopore data, the Oligomer-based Classifier of Taxonomic Operational and Pan-genome Units via Singletons (OCTOPUS). OCTOPUS is written in Java, reimplements several features of the popular Kraken2 and KrakenUniq software, with original components for improving metagenomics classification on incomplete/sampled reference databases, making it ideal for running on smartphones or tablets. OCTOPUS obtains sensitivity and precision comparable to Kraken2, while dramatically decreasing (4- to 16-fold) the false positive rate, and yielding high correlation on real-word data. OCTOPUS is available along with customized databases at https://github.com/DataIntellSystLab/OCTOPUS and https://github.com/Ruiz-HCI-Lab/OctopusMobile.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"798-807"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144640","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

Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records. 分布式电子健康记录中非随机缺失变量的联邦多重代入。

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

Yi Lian, Xiaoqian Jiang, Qi Long

引用次数: 0