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

Journal Club Engagement and Its Impact on Capstone Performance: A Study in a Health and Bioinformatics Master's Program. 期刊俱乐部的参与及其对顶点绩效的影响：健康与生物信息学硕士课程的研究。

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

Suhila Sawesi, Mohamed Rashrash, Guenter Tusch

{"title":"Journal Club Engagement and Its Impact on Capstone Performance: A Study in a Health and Bioinformatics Master's Program.","authors":"Suhila Sawesi, Mohamed Rashrash, Guenter Tusch","doi":"","DOIUrl":"","url":null,"abstract":"Introduction: In the evolving field of health informatics, the American Medical Informatics Association (AMIA) highlights the need for professionals skilled in current research. Journal clubs bridge academic learning with practical application, addressing challenges like limited literature review time and fostering critical analysis. Aim: This study evaluates the impact of an interdisciplinary journal club on 33 Master's students in Health and Bioinformatics program at Grand Valley State University. Thirteen students participated, analyzing contemporary literature and applying findings to real-world problems. Results: Significant improvements were observed in key capstone assessments among journal club participants: Capstone Overall Percentage (mean difference 15.23 points, p < 0.05), Project Proposal (mean difference 13.62 points, p < 0.05), and Research Topic Presentations (mean difference 27.30 points, p < 0.05). Conclusion: These findings support integrating journal clubs into curricula to enhance evidence-based practice, interdisciplinary collaboration, and practical application of knowledge, aligning with AMIA's vision of continuous professional development.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"987-996"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144431","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

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

Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care. 加强抗生素管理：一种预测住院患者抗生素耐药性的机器学习方法。

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

Fateme Nateghi Haredasht, Manoj V Maddali, Stephen P Ma, Amy Chang, Grace Y E Kim, Niaz Banaei, Stanley Deresinski, Mary K Goldstein, Steven M Asch, Jonathan H Chen

{"title":"Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care.","authors":"Fateme Nateghi Haredasht, Manoj V Maddali, Stephen P Ma, Amy Chang, Grace Y E Kim, Niaz Banaei, Stanley Deresinski, Mary K Goldstein, Steven M Asch, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"Antibiotics have been crucial in advancing medical treatments, but the growing threat of antibiotic resistance challenges these achievements and emphasizes the need for innovative stewardship strategies. In this study, we developed machine learning models, 'personalized antibiograms', to predict antibiotic resistance across five key antibiotics using Stanford's electronic health record data of 49,872 urine, blood, and respiratory infections. We aimed to ascertain the efficacy of these models in predicting antibiotic susceptibility and identify the clinical factors most indicative of resistance. Employing LightGBM, we incorporated demographics, prior resistance, prescriptions, and comorbidities as features. The models demonstrated notable discriminative ability, with AUROCs between 0.74 and 0.78, and highlighted prior resistance and prescriptions as significant predictive factors. The high specificity demonstrates machine learning models' potential to inform antibiotic de-escalation, aiding stewardship without risking safety. By leveraging machine learning with relevant clinical features, we show that it is feasible to improve empirical antibiotic prescribing.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"857-864"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144583","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

Enhancing Wearable Sensor Data Classification Through Novel Modified- Recurrent Plot-Based Image Representation and Mixup Augmentation. 基于改进循环图像表示和混合增强的可穿戴传感器数据分类。

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

Yidong Zhu, Nadia Aimandi, Md Mahmudur Rahman, Mohammad Arif Ul Alam

{"title":"Enhancing Wearable Sensor Data Classification Through Novel Modified- Recurrent Plot-Based Image Representation and Mixup Augmentation.","authors":"Yidong Zhu, Nadia Aimandi, Md Mahmudur Rahman, Mohammad Arif Ul Alam","doi":"","DOIUrl":"","url":null,"abstract":"Deep learning advancements have revolutionized scalable classification in many domains including computer vision, healthcare and Natural Language Processing (NLP). However, when it comes to classification and domain adaptation based on wearables, it suffers from persistent underperformance, largely due to the scarcity of pre-trained deep learning models that are abundantly available for computer vision and NLP. This is primarily because wearable sensor data need sensor-specific preprocessing, architectural modification, and extensive data collection. We present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. We employ an efficient Fourier Transform-based frequency domain angular difference estimation scheme in conjunction with existing temporal recurrent plots. We validated proposed method in two different domains: accelerometer-based activity-recognition and real-time glucose level prediction from wearable sensors. Our findings demonstrated the method we developed not only improves accuracy at recognizing activity but also makes a big leap in glucose level prediction.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1340-1349"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144607","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

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

Paulina Sockolow

引用次数: 0

A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records. 从电子健康记录中发现和量化系统性红斑狼疮病因异质性的数据驱动方法。

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

Marco Barbero Mota, John M Still, Jorge L Gamboa, Eric V Strobl, Charles M Stein, Vivian K Kawai, Thomas A Lasko

{"title":"A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records.","authors":"Marco Barbero Mota, John M Still, Jorge L Gamboa, Eric V Strobl, Charles M Stein, Vivian K Kawai, Thomas A Lasko","doi":"","DOIUrl":"","url":null,"abstract":"Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sources from multimodal imperfect EHR data. These sources represent exogenous variables in the data generation process causal graph that estimate latent root causes of the presence of SLE in the health record. We objectively evaluated the sources against the original variables from which they were discovered by training supervised models to discriminate SLE from negative health records using a reduced set of labelled instances. We found 19 predictive sources with high clinical validity and whose EHR signatures define independent factors of SLE heterogeneity. Using the sources as input patient data representation enables models to provide with rich explanations that better capture the clinical reasons why a particular record is (not) an SLE case. Providers may be willing to trade patient-level interpretability for discrimination especially in challenging cases.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"172-181"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144685","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

Molecularly-Guided Cancer Clinical Trial Matching using FHIR and HL7 Clinical Quality Language: A Proof of Concept. 使用FHIR和HL7临床质量语言的分子引导癌症临床试验匹配：概念验证。

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

Robert H Dolin, Waddah Arafat, Bret S E Heale, Edna Shenvi, Srikar Chamala

{"title":"Molecularly-Guided Cancer Clinical Trial Matching using FHIR and HL7 Clinical Quality Language: A Proof of Concept.","authors":"Robert H Dolin, Waddah Arafat, Bret S E Heale, Edna Shenvi, Srikar Chamala","doi":"","DOIUrl":"","url":null,"abstract":"Introduction: Clinical trials play a crucial role in precision cancer care. Patients generally learn of trials from their physician, and physician recognition of potential matches can be enhanced through decision support tools. But automated trial matching remains challenging, particularly for molecular eligibility criteria. Objective: We assessed the feasibility of FHIR Genomics plus CQL to enable trial matching, particularly for molecular criteria. Methods: We developed a prototype that included (1) encoded trial criteria in CQL; (2) synthetic patient clinical and genomic data; (3) trial eligibility computation. Results: We found that even complex molecular eligibility criteria can be represented in CQL given that the semantics of a criterion are formalized in base FHIR specifications. The proof of concept \"CQL for Clinical Trials Matching\" is available at [https://elimu.io/downloads/]. Discussion and Conclusions: Proof of concept work suggests FHIR and CQL as viable options for enhancing clinical trial matching.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"359-367"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144633","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

Translating Evidence-Based Guidelines Into Clinical Decision Support Tools to Improve Identification and Management of Familial Hypercholesterolemia. 将循证指南转化为临床决策支持工具，以改善家族性高胆固醇血症的识别和管理。

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

Timothy C Shuey, Tyler J Schubert, Katrina Romagnoli, Dylan Cawley, Laney K Jones, Samuel S Gidding, Marc S Williams

{"title":"Translating Evidence-Based Guidelines Into Clinical Decision Support Tools to Improve Identification and Management of Familial Hypercholesterolemia.","authors":"Timothy C Shuey, Tyler J Schubert, Katrina Romagnoli, Dylan Cawley, Laney K Jones, Samuel S Gidding, Marc S Williams","doi":"","DOIUrl":"","url":null,"abstract":"Evidence-based clinical guidelines serve to support clinical decision making, but implementing such guidelines into practice remains a challenge. Familial hypercholesterolemia (FH) is a high impact clinical condition that exemplifies this disconnect. Using implementation science methods, we designed clinical decision support tools embedded into the electronic health record, including a FH-focused electronic health record Smart Set and clinic note template, to improve the care of adult and pediatric patients at high-risk of FH. End-user feedback gathered through direct observations, semi-structured interviews, and deliberative engagement sessions was used to inform the development of the tools before and after pilot-testing. Clinicians desired comprehensive, guidelines-based tools that promoted collaborative care. During pilot testing, end-users provided insights into technical issues encountered with the tool's first iteration and suggested regular check-in sessions to monitor issues moving forward. This methodology can be used to surmount challenges that prevent the uptake of evidence-based guidelines into practice.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1030-1039"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099389/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144758","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

Student Behavior Analysis using YOLOv5 and OpenPose in Smart Classroom Environment. 基于YOLOv5和OpenPose的智能课堂环境下学生行为分析

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

Xiang Li, Yucheng Ji, Jiayi Yang, Mingyong Li

{"title":"Student Behavior Analysis using YOLOv5 and OpenPose in Smart Classroom Environment.","authors":"Xiang Li, Yucheng Ji, Jiayi Yang, Mingyong Li","doi":"","DOIUrl":"","url":null,"abstract":"In the classroom, artificial intelligence techniques help automate student behavior analysis, and teachers are able to understand students' class status more effectively. We developed an intelligent method for classroom behavior analysis by building a CQStu datasets and annotating 6,687 images through active learning. OpenPose was used to detect the key points of the student's body, and the key points of the key parts of the body were utilized to generate representative points of the student, and the idea of coordinates was used to assign the student's position. Using YOLOV5 to recognize students' classroom behaviors and count the number of times, our experimental results show that the average classroom behavior recognition accuracy is 84.23%, and the overall location accuracy is about 79.6%. In addition, we introduced a nonlinear weighting factor to evaluate the effectiveness of teaching and constructed corresponding classroom behavior weights based on different classroom scenarios. A method for student classroom behavior identification and analysis is provided, and a framework for future intelligent classroom teaching evaluation methods is established, providing objective data support for student performance analysis.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"674-683"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144807","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