AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第4页

Sepsis Prediction Models are Trained on Labels that Diverge from Clinician-Recommended Treatment Times. 脓毒症预测模型是在偏离临床医生推荐治疗时间的标签上训练的。

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

Gary E Weissman, Rebecca A Hubbard, Blanca E Himes, Kelly L Goodman-O'Leary, Michael O Harhay, Jennifer C Ginestra, Rachel Kohn, Andrew J Admon, Stephanie Parks Taylor, Scott D Halpern

{"title":"Sepsis Prediction Models are Trained on Labels that Diverge from Clinician-Recommended Treatment Times.","authors":"Gary E Weissman, Rebecca A Hubbard, Blanca E Himes, Kelly L Goodman-O'Leary, Michael O Harhay, Jennifer C Ginestra, Rachel Kohn, Andrew J Admon, Stephanie Parks Taylor, Scott D Halpern","doi":"","DOIUrl":"","url":null,"abstract":"Many sepsis prediction models use the Sepsis-3 definition or its variants as a training label. However, among the few sepsis models ever deployed in practice, there is scant evidence that they offer clinically meaningful decision support at the bedside. As a potential mechanism to explain this limitation, we hypothesized that clinician-recommended treatment times for sepsis would diverge from onset time defined by Sepsis-3. We conducted an electronic survey that was completed by 153 clinicians at three large and geographically diverse medical centers using vignettes derived from eight real cases of sepsis. After reviewing these vignettes, participants suggested antibiotic treatment to start an average of 7.0 hours (95% confidence interval 5.3 to 8.8) before the Sepsis-3 definition onset. Thus, predicting Sepsis-3 onset as a treatment prompt could lead to inappropriate and delayed treatment recommendations. Building predictive decision support systems that identify outcomes aligned with bedside decisions would increase their clinical utility.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1215-1224"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144733","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 Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data. 使用真实世界跨省初级保健数据的加拿大成人联合糖尿病预测

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

Guojun Tang, Jason E Black, Tyler S Williamson, Steve H Drew

{"title":"Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.","authors":"Guojun Tang, Jason E Black, Tyler S Williamson, Steve H Drew","doi":"","DOIUrl":"","url":null,"abstract":"Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models can provide early identification of patients with high risk for diabetes, potentially leading to more effective therapeutic strategies and reduced healthcare costs. However, regulation restrictions create barriers to developing centralized predictive models. This paper addresses the challenges by introducing a federated learning approach, which amalgamates predictive models without centralized data storage and processing, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) without cross-province patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. Experimental results show that the federated MLP model presents a similar or higher performance compared to the model trained with the centralized approach. However, the federated logistic regression model showed inferior performance compared to its centralized peer.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1099-1108"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144645","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 acute kidney injury subtypes based on serum electrolyte data in ICU via K-medoids clustering. 基于ICU患者血清电解质数据的K-medoids聚类识别急性肾损伤亚型。

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

Wentie Liu, Tongyue Shi, Haowei Xu, Huiying Zhao, Jianguo Hao, Guilan Kong

引用次数: 0

Impact of Automated Transfer of Semi-Automated Segmentation and Structured Report Rule Requirements on Cardiac MRI Report Quality, Standardization, and Efficiency. 半自动分割和结构化报告规则要求的自动传递对心脏MRI报告质量、标准化和效率的影响。

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

Diane Rizkallah, Neil L Greenberg, Rishabh Khurana, Vadivelan Palanisamy, Ben Alencherry, Carl Ammoury, Yezan Salam, Lisa Lamovsky, Haitham Fares, Robert Geschke, Richard Grimm, Christopher Nguyen, David Chen, Deborah H Kwon

{"title":"Impact of Automated Transfer of Semi-Automated Segmentation and Structured Report Rule Requirements on Cardiac MRI Report Quality, Standardization, and Efficiency.","authors":"Diane Rizkallah, Neil L Greenberg, Rishabh Khurana, Vadivelan Palanisamy, Ben Alencherry, Carl Ammoury, Yezan Salam, Lisa Lamovsky, Haitham Fares, Robert Geschke, Richard Grimm, Christopher Nguyen, David Chen, Deborah H Kwon","doi":"","DOIUrl":"","url":null,"abstract":"Clinical reporting of cardiac magnetic resonance (CMR) imaging exams is commonly performed with a dictation approach which requires great care to capture both consistent and comprehensive data. We sought to transform the reporting process by utilizing a structured report framework for reporting standardization, by incorporating automated transfer of data semi-automated segmentation tools for efficiency, and rule-based reporting requirements to improve quality and standardization. Interfaces between the applications used to schedule and protocol exams and to analyze the acquired images were created to bring the source information directly into the structured reporting environment. The physicians reporting CMR were surveyed to determine satisfaction and improved efficiency with the new process through self-reported reporting time. Quality improvement was assessed by examining the consistency of reported parameters with the inclusion of rule-based requirements. The designed structured reporting process with automated measurements and rule-based requirements resulted in significant improvement in report efficiency and quality.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"950-959"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144657","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

PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability. PathSAM：通过先进的分割和可解释性增强口腔癌的检测。

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

Suraj Sood, Jawad S Shah, Saeed Alqarn, Yugyung Lee

引用次数: 0

A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model. 基于句子转换器的电子健康记录到OMOP公共数据模型模式映射的自然语言处理方法。

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

Xinyu Zhou, Lovedeep Singh Dhingra, Arya Aminorroaya, Philip Adejumo, Rohan Khera

{"title":"A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.","authors":"Xinyu Zhou, Lovedeep Singh Dhingra, Arya Aminorroaya, Philip Adejumo, Rohan Khera","doi":"","DOIUrl":"","url":null,"abstract":"Mapping electronic health records (EHR) data to common data models (CDMs) enables the standardization of clinical records, enhancing interoperability and enabling large-scale, multi-centered clinical investigations. Using 2 large publicly available datasets, we developed transformer-based natural language processing models to map medication-related concepts from the EHR at a large and diverse healthcare system to standard concepts in OMOP CDM. We validated the model outputs against standard concepts manually mapped by clinicians. Our best model reached out-of-box accuracies of 96.5% in mapping the 200 most common drugs and 83.0% in mapping 200 random drugs in the EHR. For these tasks, this model outperformed a state-of-the-art large language model (SFR-Embedding-Mistral, 89.5% and 66.5% in accuracy for the two tasks), a widely used software for schema mapping (Usagi, 90.0% and 70.0% in accuracy), and direct string match (7.5% and 7.5% accuracy). Transformer-based deep learning models outperform existing approaches in the standardized mapping of EHR elements and can facilitate an end-to-end automated EHR transformation pipeline.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1332-1339"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144689","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

Secondary Use of Clinical Problem List Descriptions for Bi-Encoder Based ICD-10 Classification. 基于双编码器的ICD-10分类中临床问题清单描述的二次使用。

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

Markus Kreuzthaler, Bastian Pfeifer, Stefan Schulz

引用次数: 0

Modeling Precision Feedback Knowledge for Healthcare Professional Learning and Quality Improvement. 为医疗保健专业人员学习和质量改进建模精确反馈知识。

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

Zach Landis-Lewis, Yidan Cao, Hana Chung, Peter Boisvert, Anjana Deep Renji, Patrick Galante, Ayshwarya Jagadeesan, Farid Seifi, Allison Janda, Nirav Shah, Andrew Krumm, Allen Flynn

{"title":"Modeling Precision Feedback Knowledge for Healthcare Professional Learning and Quality Improvement.","authors":"Zach Landis-Lewis, Yidan Cao, Hana Chung, Peter Boisvert, Anjana Deep Renji, Patrick Galante, Ayshwarya Jagadeesan, Farid Seifi, Allison Janda, Nirav Shah, Andrew Krumm, Allen Flynn","doi":"","DOIUrl":"","url":null,"abstract":"Healthcare providers learn continuously, but better support for provider learning is needed as new biomedical knowledge is produced at an increasing rate alongside widespread use of EHR data for clinical performance measurement. Precision feedback is an approach to improve support for provider learning by prioritizing coaching and appreciation messages based on each message's motivational potential for a specific recipient. We developed a Precision Feedback Knowledge Base as an open resource to support precision feedback systems, containing knowledge models that hold potential as key infrastructure for learning health systems. We describe the design and development of the Precision Feedback Knowledge Base, as well as its key components, including quality measures, feedback message templates, causal pathway models, signal detectors, and prioritization algorithms. Presently, the knowledge base is implemented in a national-scale quality improvement consortium for anesthesia care, to enhance provider feedback email messages.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"628-637"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144613","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

Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset. 基于OMOP的本体驱动方法优化药物查询：基于大规模COVID-19电子病历数据集的应用

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

Xiaojin Li, Yan Huang, Licong Cui, Shiqiang Tao, Guo-Qiang Zhang

{"title":"Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset.","authors":"Xiaojin Li, Yan Huang, Licong Cui, Shiqiang Tao, Guo-Qiang Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Efficient querying for medication information in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. To address the complexity and data volume challenges involved in efficient medication information retrieval, we propose an ontology-driven medication query (ODMQ) optimization approach, leveraging the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Integrating semantic ontology structures from the OMOP CDM can help enhance query accuracy and efficiency by broadening the scope of relevant medication terms like drug names, National Drug Codes, and generics, resulting in more comprehensive query outcomes than traditional methods. ODMQ significantly reduces manual search time and enhances query capabilities. We validate ODMQ's efficacy using real-world COVID-19 EHR data, demonstrating improved query performance. Through a comprehensive manual review, ODMQ ensures that expanded search terms are relevant to user inputs. It also includes an intuitive query interface and visualizes patient history for result validation and exploration.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"693-702"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144660","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 the Seasonality of Lab Tests Among Patients with Alzheimer's Disease and Related Dementias in OneFlorida Data Trust. 在佛罗里达数据信托中评估阿尔茨海默病和相关痴呆患者实验室测试的季节性

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

Wenshan Han, Balu Bhasuran, Victorine Patricia Muse, Søren Brunak, Lifeng Lin, Karim Hanna, Yu Huang, Jiang Bian, Zhe He

{"title":"Assessing the Seasonality of Lab Tests Among Patients with Alzheimer's Disease and Related Dementias in OneFlorida Data Trust.","authors":"Wenshan Han, Balu Bhasuran, Victorine Patricia Muse, Søren Brunak, Lifeng Lin, Karim Hanna, Yu Huang, Jiang Bian, Zhe He","doi":"","DOIUrl":"","url":null,"abstract":"About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations-higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"483-492"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144699","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