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AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献

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Building Patient-Facing Technology: A REDCap-Based Approach. 构建面向患者的技术：基于redcap的方法。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01
Yuheng Shi, Eric Yang, Katie Gahn, Heidi Mason, Yun Jiang, Yang Gong
{"title":"Building Patient-Facing Technology: A REDCap-Based Approach.","authors":"Yuheng Shi, Eric Yang, Katie Gahn, Heidi Mason, Yun Jiang, Yang Gong","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This work describes the architecture design of a patient-facing technology (PFT) based on the Research Electronic Data Capture (REDCap) platform and other tools to support cancer patients in self-tracking and managing medication concerns and symptoms during transitions of care. The design is guided by the Chronic Care Model (CCM) and User-Centered Design (UCD) principles for a personalized application to inform, engage, and empower patients. We describe the evolutional details of four major versions, which represent milestones of our PFT, highlighting how specific objectives were achieved and the barriers encountered. Additionally, patient representatives were involved in the evaluation of prototypes, and potential improvements to the application of the REDCap platform were discussed. REDCap has demonstrated great potential to serve beyond its traditional role as a survey distribution and management tool. This work is intended to provide developers with insights into future PFT architecture development and sustainable research strategies.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"501-510"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276843","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 Dietary Supplements Related Effects from Social Media by ChatGPT. 通过ChatGPT识别社交媒体对膳食补充剂的相关影响。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01
Ying Liu, Yu Hou, Jeremy Yeung, Tou Thao, Meijia Song, Rubina Rizvi, Jiang Bian, Rui Zhang
{"title":"Identifying Dietary Supplements Related Effects from Social Media by ChatGPT.","authors":"Ying Liu, Yu Hou, Jeremy Yeung, Tou Thao, Meijia Song, Rubina Rizvi, Jiang Bian, Rui Zhang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This study advances relationship identification in social media by analyzing dietary supplement-related tweets aiming to expand the drug-supplement interactions dataset iDisk. We collected 90,000+ tweets (2007-2022) and annotated 1,000 for nuanced relationships and entities. Using a BioBERT model and ChatGPT-generated prompts, we conducted entity type and relationship identification. The BioBERT model achieved an F1 score of 0.90 for relationship prediction, while ChatGPT prompts reached 0.99. Entity type recognition proved more challenging, with high semantic similarity between types impacting accuracy. Our methodology significantly enhances relationship identification from social media data, particularly for dietary supplements usage, offering promising methods for improved post-market surveillance and public health monitoring. This work demonstrates the potential of combining traditional NLP models with large language models for complex text analysis tasks in healthcare.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"322-331"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276849","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
Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models. 基于大语言模型的基因型数据的知识驱动特征选择与工程。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01
Joseph Lee, Shu Yang, Jae Young Baik, Xiaoxi Liu, Zhen Tan, Dawei Li, Zixuan Wen, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Li Shen
{"title":"Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models.","authors":"Joseph Lee, Shu Yang, Jae Young Baik, Xiaoxi Liu, Zhen Tan, Dawei Li, Zixuan Wen, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally, data-driven approaches are used for this task, yet the high dimensional nature of genotype data makes the analysis and prediction difficult. Motivated by the biomedical knowledge encoded in pre-trained LLMs and the emerging applications for genetics, we set to examine the ability of LLMs in feature selection and engineering for tabular genotype data, with a novel knowledge-driven framework. We develop FREEFORM, Free-flow Reasoning and Ensembling for Enhanced Feature Output and Robust Modeling, designed with chain-of-thought and ensembling principles, to select and engineer features with the intrinsic knowledge of LLMs. Evaluated on two distinct genotype-phenotype datasets, genetic ancestry and hereditary hearing loss, we find this framework outperforms several data-driven methods, particularly on low-data regimes. FREEFORM is available as open-source framework at GitHub: https://github.com/PennShenLab/FREEFORM.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"250-259"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276855","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
Toward Automated Clinical Transcriptions. 迈向自动化临床转录。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01
Mitchell A Klusty, W Vaiden Logan, Samuel E Armstrong, Aaron D Mullen, Caroline N Leach, Ken Calvert, Jeff Talbert, V K Cody Bumgardner
{"title":"Toward Automated Clinical Transcriptions.","authors":"Mitchell A Klusty, W Vaiden Logan, Samuel E Armstrong, Aaron D Mullen, Caroline N Leach, Ken Calvert, Jeff Talbert, V K Cody Bumgardner","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Administrative documentation is a major driver of rising healthcare costs and is linked to adverse outcomes, including physician burnout and diminished quality of care. This paper introduces a secure system that applies recent advancements in speech-to-text transcription and speaker-labeling (diarization) to patient-provider conversations. This system is optimized to produce accurate transcriptions and highlight potential errors to promote rapid human verification, further reducing the necessary manual effort. Applied to over 40 hours of simulated conversations, this system offers a promising foundation for automating clinical transcriptions.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"235-241"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276903","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
Unraveling Complex Temporal Patterns in EHRs via Robust Irregular Tensor Factorization. 基于鲁棒不规则张量分解的电子病历复杂时间模式研究。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2025-06-10 eCollection Date: 2025-01-01
Ren Yifei, Linghui Zeng, Jian Lou, Li Xiong, Joyce C Ho, Xiaoqian Jiang, Sivasubramanium V Bhavani
{"title":"Unraveling Complex Temporal Patterns in EHRs via Robust Irregular Tensor Factorization.","authors":"Ren Yifei, Linghui Zeng, Jian Lou, Li Xiong, Joyce C Ho, Xiaoqian Jiang, Sivasubramanium V Bhavani","doi":"","DOIUrl":"","url":null,"abstract":"<p><p><i>Electronic health records (EHRs) contain diverse patient data with varying visit frequencies. While irregular tensor factorization techniques such as PARAFAC2 have been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and complex temporal patterns and struggle with missing entries. In this paper, we propose</i> REPAR<i>, an</i> R<i>NN R</i>E<i>gularized Robust</i> PAR<i>AFAC2 method to model complex temporal dependencies and enhance robustness in the presence of missing data. Our approach employs Recurrent Neural Networks (RNNs) for temporal regularization and a low-rank constraint for robustness, enabling precise patient subgroup identification and improved clinical decision-making in noisy EHR data. We design a hybrid optimization framework that handles multiple regularizations and various data types. REPAR is evaluated on 3 real-world EHR datasets, demonstrating improved reconstruction and robustness under missing data. Two case studies further showcase REPAR's ability to extract meaningful dynamic phenotypes and enhance phenotype predictability from noisy temporal EHRs.</i></p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"451-460"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276905","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
Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records. 在电子健康记录中实施产后抑郁症的机器学习风险预测模型。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-10-21 eCollection Date: 2024-01-01
Yiye Zhang, Rochelle Joly, Ashley N Beecy, Samen Principe, Sujit Satpathy, Anatoly Gore, Tom Reilly, Mitchel Lang, Nagi Sathi, Carlos Uy, Matt Adams, Mark Israel
{"title":"Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.","authors":"Yiye Zhang, Rochelle Joly, Ashley N Beecy, Samen Principe, Sujit Satpathy, Anatoly Gore, Tom Reilly, Mitchel Lang, Nagi Sathi, Carlos Uy, Matt Adams, Mark Israel","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This study describes the deployment process of an AI-driven clinical decision support (CDS) system to support postpartum depression (PPD) prevention, diagnosis and management. Central to this CDS is an L2-regularized logistic regression model trained on electronic health record (EHR) data at an academic medical center, and subsequently refined through a broader dataset from a consortium to ensure its generalizability and fairness. The deployment architecture leveraged Microsoft Azure to facilitate a scalable, secure, and efficient operational framework. We used Fast Healthcare Interoperability Resources (FHIR) for data extraction and ingestion between the two systems. Continuous Integration/Continuous Deployment pipelines automated the deployment and ongoing maintenance, ensuring the system's adaptability to evolving clinical data. Along the technical preparation, we focused on a seamless integration of the CDS within the clinical workflow, presenting risk assessment directly within the clinician schedule and providing options for subsequent actions. The developed CDS is expected to drive a PPD clinical pathway to enable efficient PPD risk management.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"1057-1066"},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513963","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
Large Language Models for Efficient Medical Information Extraction. 用于高效医学信息提取的大型语言模型。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01
Navya Bhagat, Olivia Mackey, Adam Wilcox
{"title":"Large Language Models for Efficient Medical Information Extraction.","authors":"Navya Bhagat, Olivia Mackey, Adam Wilcox","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Extracting valuable insights from unstructured clinical narrative reports is a challenging yet crucial task in the healthcare domain as it allows healthcare workers to treat patients more efficiently and improves the overall standard of care. We employ ChatGPT, a Large language model (LLM), and compare its performance to manual reviewers. The review focuses on four key conditions: family history of heart disease, depression, heavy smoking, and cancer. The evaluation of a diverse sample of History and Physical (H&P) Notes, demonstrates ChatGPT's remarkable capabilities. Notably, it exhibits exemplary results in sensitivity for depression and heavy smokers and specificity for cancer. We identify areas for improvement as well, particularly in capturing nuanced semantic information related to family history of heart disease and cancer. With further investigation, ChatGPT holds substantial potential for advancements in medical information extraction.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"509-514"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141860/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201184","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
Opioid and Antimicrobial Prescription Patterns During Emergency Medicine Encounters Among Uninsured Patients. 未参保患者在急诊就医期间的阿片类药物和抗菌药物处方模式。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01
Michael A Grasso, Anantaa Kotal, Anupam Joshi
{"title":"Opioid and Antimicrobial Prescription Patterns During Emergency Medicine Encounters Among Uninsured Patients.","authors":"Michael A Grasso, Anantaa Kotal, Anupam Joshi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The purpose of this study was to characterize opioid and antimicrobial prescribing among uninsured patients seeking emergency medical care and to build predictive machine learning models. Uninsured patients were less likely to receive an opioid medication, more likely to receive non-opioid alternatives, and less likely to receive an antimicrobial prescription. The most impactful contributing factors were housing status, comorbidities, and recidivism.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"190"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201194","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 Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria. 通过自然语言处理合成电子健康记录和临床试验资格标准实现临床试验匹配自动化。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01
Victor M Murcia, Vinod Aggarwal, Nikhil Pesaladinne, Ram Thammineni, Nhan Do, Gil Alterovitz, Rafael B Fricks
{"title":"Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria.","authors":"Victor M Murcia, Vinod Aggarwal, Nikhil Pesaladinne, Ram Thammineni, Nhan Do, Gil Alterovitz, Rafael B Fricks","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"125-134"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201516","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
Detecting Multimorbidity Patterns with Association Rule Mining in Patients with Alzheimer's Disease and Related Dementias. 用关联规则挖掘法检测阿尔茨海默病及相关痴呆症患者的多病模式
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01
Razan A El Khalifa, Pui Ying Yew, Chih-Lin Chi
{"title":"Detecting Multimorbidity Patterns with Association Rule Mining in Patients with Alzheimer's Disease and Related Dementias.","authors":"Razan A El Khalifa, Pui Ying Yew, Chih-Lin Chi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Researchers estimate the number of dementia patients to triple by 2050<sup>1</sup>. Dementia seldom occurs in isolation; it's frequently accompanied by other health conditions<sup>2</sup>. The coexistence of conditions further complicates the management of dementia. In this study, we embarked on an innovative approach, applying association rule mining to analyze National Alzheimer's Coordinating Center (NACC) data. First, we completed a literature review on the utilization of association rules, heatmaps, and network analysis to detect and visualize comorbidities. Then, we conducted a secondary data analysis on the NACC data using association rule mining. This algorithm uncovers associations of comorbidities that are diagnosed together in patients who have Alzheimer's disease and related dementias (ADRD). Also, for these patients, the algorithm provides the probability of a patient developing another comorbidity given the diagnosis of an associated comorbidity. These findings can enhance treatment planning, advance research on high-association diseases, and ultimately enhance healthcare for dementia patients.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"525-534"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200313","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
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