AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第2页

Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction. 利用多源数据解决药物敏感性预测中药物基因组学数据集的不一致性。

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

Xiaodi Li, Trisha Das, Kritib Bhattarai, Sivaraman Rajaganapathy, Vincent C Buchner, Yanshan Wang, Chang Su, Lichao Sun, Liewei Wang, James R Cerhan, Nansu Zong

{"title":"Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction.","authors":"Xiaodi Li, Trisha Das, Kritib Bhattarai, Sivaraman Rajaganapathy, Vincent C Buchner, Yanshan Wang, Chang Su, Lichao Sun, Liewei Wang, James R Cerhan, Nansu Zong","doi":"","DOIUrl":"","url":null,"abstract":"Researchers have developed pharmacogenomics datasets for various purposes, such as biomarker identification, yet drug response prediction models often underperform due to dataset inconsistencies. These variations arise from inter-tumoral heterogeneity, experimental conditions, and cell subtype complexity, limiting model generalizability. To address this, we propose a computational model based on Aggregated Learning (AL) to enhance drug response prediction by learning from inconsistencies across multiple datasets. Our model minimizes discrepancies by training on overlapping inconsistent data points from three pharmacogenomic datasets-CCLE, GDSC2, and gCSI. Compared to four baseline methods-Selecting Better (SB), Result Average (RA), Combining Data (CD), and Model Average (MA)-our approach achieved superior performance with lower Mean Absolute Error (MAE) scores: 0.090 (CCLE-GDSC), 0.096 (CCLE-gCSI), and 0.081 (GDSC-gCSI). These results demonstrate that addressing inconsistencies enhances prediction accuracy and generalizability, making our model a promising solution for robust drug response predictions.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"744-753"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272967","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

Relation Extraction with Instance-Adapted Predicate Descriptions. 基于实例适应谓词描述的关系提取。

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

Yuhang Jiang, Ramakanth Kavuluru

引用次数: 0

A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub. 通用数据协调框架：支持RADx数据中心中可复制和可扩展的数据集成。

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

Jimmy K Yu, Marcos Martínez-Romero, Matthew Horridge, Mete U Akdogan, Mark A Musen

{"title":"A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub.","authors":"Jimmy K Yu, Marcos Martínez-Romero, Matthew Horridge, Mete U Akdogan, Mark A Musen","doi":"","DOIUrl":"","url":null,"abstract":"In the age of big data, it is important for primary research data to follow the FAIR principles of findability, accessibility, interoperability, and reusability. Data harmonization enhances interoperability and reusability by aligning heterogeneous data under standardized representations, benefiting both repository curators responsible for upholding data quality standards and consumers who require unified datasets. However, data harmonization is difficult in practice, requiring significant domain and technical expertise. We present a software framework to facilitate principled and reproducible harmonization protocols. Our framework implements a novel strategy of building harmonization transformations from parameterizable primitive operations, such as the assignment of numerical values to user-specified categories, with automated bookkeeping for executed transformations. We establish our data representation model and harmonization strategy and then report a proof-of-concept application in the context of the RADx Data Hub. Our framework enables data practitioners to execute transparent and reproducible harmonization protocols that align closely with their research goals.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1482-1491"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273073","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

When AI Writes Back: Ethical Considerations by Physicians on AI-Drafted Patient Message Replies. 当人工智能回信时：医生对人工智能起草的患者信息回复的伦理考虑。

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

Di Hu, Yawen Guo, Ha Na Cho, Emilie Chow, Dana B Mukamel, Dara Sorkin, Andrew Reikes, Danielle Perret, Deepti Pandita, Kai Zheng

{"title":"When AI Writes Back: Ethical Considerations by Physicians on AI-Drafted Patient Message Replies.","authors":"Di Hu, Yawen Guo, Ha Na Cho, Emilie Chow, Dana B Mukamel, Dara Sorkin, Andrew Reikes, Danielle Perret, Deepti Pandita, Kai Zheng","doi":"","DOIUrl":"","url":null,"abstract":"The increasing burden of responding to large volumes of patient messages has become a key factor contributing to physician burnout. Generative AI (GenAI) shows great promise to alleviate this burden by automatically drafting patient message replies. The ethical implications of this use have however not been fully explored. To address this knowledge gap, we conducted a qualitative interview study with 21 physicians who participated in a GenAI pilot program. We found that notable ethical considerations expressed by the physician participants included oversight as ethical safeguard, transparency and patient consent of AI use, patient misunderstanding of AI's role, and patient privacy and data security as prerequisites. Additionally, our findings suggest that the physicians believe the ethical responsibility of using GenAI in this context primarily lies with users, not with the technology. These findings may provide useful insights into guiding the future implementation of GenAI in clinical practice.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"481-489"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273079","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

Technology and Human Support Systems in Decentralized Studies: A Participant-Centered Case Study in Cystic Fibrosis. 分散研究中的技术和人类支持系统：一项以参与者为中心的囊性纤维化病例研究。

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

Ayana Sarrieddine, Claire Lai, Oliver Bear Don't Walk, Nick F H Reid, Gregory Sawicki, Ariel Berlinski, Margaret Rosenfeld, Andrea L Hartzler

{"title":"Technology and Human Support Systems in Decentralized Studies: A Participant-Centered Case Study in Cystic Fibrosis.","authors":"Ayana Sarrieddine, Claire Lai, Oliver Bear Don't Walk, Nick F H Reid, Gregory Sawicki, Ariel Berlinski, Margaret Rosenfeld, Andrea L Hartzler","doi":"","DOIUrl":"","url":null,"abstract":"As the integration of informatics into clinical research reshapes the landscape of decentralized studies, optimizing participant experience remains a key challenge. Although prior research has established foundations for decentralized study design, a more comprehensive understanding of participant perspectives is essential to ensure remote methods for data collection meet participant needs. This study contributes to a growing literature in participant-centered decentralized studies through an analysis of OUTREACH, a 3-month home spirometry study among individuals with cystic fibrosis. Through a qualitative analysis of 46 participant exit interviews, we identified three overarching categories that influenced participant experience: motivators, technological infrastructure, and human coordination. Our findings emphasize the value of reliable technology and comprehensive interpersonal support systems. These findings shed light upon the importance of sociotechnical elements for optimizing participant experience, which may enhance the quality of clinical study data through meaningful participant engagement.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1130-1139"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273135","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 Missing IS-A Relations in SNOMED CT with Fine-Tuned Pre-trained Language Models and Non-lattice Subgraphs. 用微调预训练语言模型和非格子图识别SNOMED CT中缺失的IS-A关系。

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

Xubing Hao, Rashmie Abeysinghe, Jay Shi, Guo-Qiang Zhang, Licong Cui

{"title":"Identifying Missing IS-A Relations in SNOMED CT with Fine-Tuned Pre-trained Language Models and Non-lattice Subgraphs.","authors":"Xubing Hao, Rashmie Abeysinghe, Jay Shi, Guo-Qiang Zhang, Licong Cui","doi":"","DOIUrl":"","url":null,"abstract":"Ensuring the completeness of IS-A relations in SNOMED CT is crucial for maintaining its accuracy in clinical applications. In this study, we propose a hybrid approach leveraging non-lattice subgraphs and pre-trained language models (PLMs) to identify missing IS-A relations in SNOMED CT. We fine-tuned four BERT-based models: BERT, DistillBERT, DeBERTa, and BioClinicalBERT, and four generative large language models (LLMs): BioMistral, Llama3, Gemma2, and Phi-4. Missing IS-A relations were identified through consensus predictions by all eight models. De-BERTa achieved the best performance (precision: 0.96, recall: 0.97, F1-score: 0.965) for IS-A relation prediction. Our approach identified 678 potential missing IS-A relations in SNOMED CT (March 2023 US Edition), of which 100 randomly selected cases were manually reviewed by a domain expert, confirming 93 as valid (93% precision). These results demonstrate the effectiveness of fine-tuned PLMs in detecting missing IS-A relations within non-lattice subgraphs, offering a promising avenue for improving SNOMED CT's quality.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"433-442"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273142","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

STop Clock for Automated Tracking (STAT) during Time-Critical Medical Work: Evaluating the Accuracy and Usability of an AI-Driven Automated Stop Clock. 在时间紧迫的医疗工作中用于自动跟踪（STAT）的计时器：评估人工智能驱动的自动计时器的准确性和可用性。

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

Katherine A Zellner, Sifan Yuan, Emily R Ernst, Dylan W Arkowitz, Aaron H Mun, Mary S Kim, Ivan Marsic, Randall S Burd, Aleksandra Sarcevic

引用次数: 0

Leveraging Large Language Models for Cancer Vaccine Adjuvant Name Extraction from Biomedical Literature. 利用大型语言模型从生物医学文献中提取癌症疫苗佐剂名称。

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

Hasin Rehana, Jie Zheng, Feng-Yu Yeh, Benu Bansal, Nur Bengisu Çam, Christianah Jemiyo, Brett McGregor, Arzucan Özgür, Yongqun He, Junguk Hur

{"title":"Leveraging Large Language Models for Cancer Vaccine Adjuvant Name Extraction from Biomedical Literature.","authors":"Hasin Rehana, Jie Zheng, Feng-Yu Yeh, Benu Bansal, Nur Bengisu Çam, Christianah Jemiyo, Brett McGregor, Arzucan Özgür, Yongqun He, Junguk Hur","doi":"","DOIUrl":"","url":null,"abstract":"This study explores cancer vaccine adjuvant name recognition using Large Language Models (LLMs), specifically Generative Pretrained Transformers (GPT), Large Language Model Meta AI (Llama), and Gemma. The models were tested in zero- and few-shot learning paradigms using AdjuvareDB and Vaccine Adjuvant Compendium (VAC) datasets. Prompts were designed to extract adjuvant names and assess the impact of contextual details. Notably, Llama-3.2 3B achieved a Recall of up to 68.7% (72.5% with manual validation) on the VAC dataset with four-shots, although its Precision and F1-score were lower. In contrast, GPT-4o, with additional contextual interventions, achieved a Precision of 65.9%, Recall of 79.7%, and F1-score of 69.8% on the AdjuvareDB dataset. Gemma-2 9B also demonstrated moderate few-shot gains, peaking at 63.6% F1-score. These LLMs outperformed BioBERT, a model widely used for biomedical text mining, highlighting the potential of general-purpose LLMs for automatic vaccine adjuvant name extraction and contributing to advancements in vaccine research.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1071-1080"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272865","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

My Kidney T.R.E.K. - Thinking, Reflecting, and Empowering Kidney Transplant Patients, through technology. 我的肾脏T.R.E.K. -思考，反思，并授权肾移植患者，通过技术。

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

Julia C Dunbar, Wanda Pratt, Lily Jeffs, Chelsea Ng, Sanaa Sayed, Jodi Smith, Ari H Pollack

引用次数: 0

Data-Driven Evidence-Based Patient-Centered Optimal Initiation Time for Dialysis Treatment. 数据驱动的循证患者为中心的透析治疗最佳起始时间。

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

Eva K Lee, Di Liu, Jeffrey Hoffman

{"title":"Data-Driven Evidence-Based Patient-Centered Optimal Initiation Time for Dialysis Treatment.","authors":"Eva K Lee, Di Liu, Jeffrey Hoffman","doi":"","DOIUrl":"","url":null,"abstract":"In this study, we propose a novel decision-making framework based on natural-language processing, machine learning and stochastic modeling for the purpose of providing a data-driven perspective to optimize the initiation time for dialysis treatment. When to start dialysis treatment is an important decision for end-stage chronic kidney disease (CKD) care. In the absence of a national guideline, determining the best time to start dialysis remains a challenge. Our decision support framework for optimizing the initiation time includes (a) a comprehensive, efficient \"pipeline\" for extracting, de-identifying, and standardizing EHR data; (b) an informatics toolkit that couples natural language processing and event mapping, clustering and machine learning to uncover the disease prognosis and treatment effects, and deduce the symptoms and utility rewards for each disease-action stage; (c) a first-of-its-kind, personalized, dialysis-timing stochastic model to determine the optimal initiation time; and (d) a clinical practice guideline (CPG) for systematic testing and implementation in the clinical setting. We evaluate the results using utility rewards for each decision process and published financial costs for each CKD disease and treatment stage. Compared to current clinical policy, the optimal initiation-time policies offer a potential 20.0% to 54.7% mortality reduction, an increase of 6.4% to 14.7% in overall utility reward and a reduction of 9.6% to 16.7% in overall cost. Working with nephrologists and a CKD/ESRD care team, a CPG was developed and tested in the clinic. Initial usage shows promising results. Follow-on clinical trials will gauge the overall effectiveness and impact on patient care of our approach. The new CPG has the potential to become a national standard.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"683-692"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272947","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