AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第8页

Measuring Accuracy of ConsultBot, Hybrid AI Tool, in Interpreting Blood Gas Results. 混合人工智能工具ConsultBot在血气结果解释中的测量精度

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

Praveen Meka, Christine Tsien Silvers, Bharath Gunapati, Qing Liu

{"title":"Measuring Accuracy of ConsultBot, Hybrid AI Tool, in Interpreting Blood Gas Results.","authors":"Praveen Meka, Christine Tsien Silvers, Bharath Gunapati, Qing Liu","doi":"","DOIUrl":"","url":null,"abstract":"Importance: Arterial and venous blood gases are complex tests used in inpatient settings. While interpretation of blood gas results is algorithmic, it is time consuming and error prone when done manually. Our study measures the accuracy of ConsultBot, in automated interpretation of blood gases. Objective: To determine proportional accuracy of ConsultBot, using rule-based logic combined with a large language model (LLM), in interpreting blood gases. Design: ConsultBot was tested in an IRB-approved single-arm trial using a dataset of 101 blood gas results and compared with a predetermined answer key. The tool's performance was assessed using proportional accuracy, sensitivity, and Cohen's Kappa. Results: ConsultBot achieved a 98% (99/101)[CI 93%-100%] proportional accuracy across the study dataset. Sensitivity was 98%[CI 93%-99%]. Cohen's Kappa of 0.97 suggests high degree of agreement between ConsultBot's interpretations and answer key. Conclusions: ConsultBot's evaluation yielded promising results, demonstrating potential in clinical-decision support for blood gas interpretations.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"881-888"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272996","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

No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism. 不再是黑盒：用时间-特征交叉注意机制揭开临床预测模型的神秘面纱。

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

Yubo Li, Xinyu Yao, Rema Padman

引用次数: 0

ProtoBERT-LoRA: Parameter-Efficient Prototypical Finetuning for Immunotherapy Study Identification. ProtoBERT-LoRA：用于免疫治疗研究鉴定的参数高效原型微调。

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

Shijia Zhang, Xiyu Ding, Kai Ding, Jacob Zhang, Kevin Galinsky, Mengrui Wang, Ryan P Mayers, Zheyu Wang, Hadi Kharrazi

{"title":"ProtoBERT-LoRA: Parameter-Efficient Prototypical Finetuning for Immunotherapy Study Identification.","authors":"Shijia Zhang, Xiyu Ding, Kai Ding, Jacob Zhang, Kevin Galinsky, Mengrui Wang, Ryan P Mayers, Zheyu Wang, Hadi Kharrazi","doi":"","DOIUrl":"","url":null,"abstract":"Identifying immune checkpoint inhibitor (ICI) studies in genomic repositories like Gene Expression Omnibus (GEO) is vital for cancer research yet remains challenging due to semantic ambiguity, extreme class imbalance, and limited labeled data in low-resource settings. We present ProtoBERT-LoRA, a hybrid framework that combines PubMedBERT with prototypical networks and Low-Rank Adaptation (LoRA) for efficient fine-tuning. The model enforces class-separable embeddings via episodic prototype training while preserving biomedical domain knowledge. Our dataset was divided as: Training (20 positive, 20 negative), Prototype Set (10 positive, 10 negative), Validation (20 positive, 200 negative), and Test (71 positive, 765 negative). Evaluated on test dataset, ProtoBERT-LoRA achieved F1-score of 0.624 (precision: 0.481, recall: 0.887), outperforming the rule-based system, machine learning baselines and finetuned PubMedBERT. Application to 44,287 unlabeled studies reduced manual review efforts by 82%. Ablation studies confirmed that combining prototypes with LoRA improved performance by 29% over stand-alone LoRA.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1539-1548"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273130","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

Intimate Partner Homicide Among Women of Childbearing Age: Identifying Multilevel Risk Factors with Machine Learning. 育龄妇女的亲密伴侣凶杀：用机器学习识别多层次的风险因素。

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

Snigdha Peddireddy, Shifan Yan, Sangmi Kim, Ran Xiao

引用次数: 0

Re-designing inpatient nursing notes shared with families: an opportunity to enhance family-centered care delivery. 重新设计与家庭共享的住院护理记录：加强以家庭为中心的护理服务的机会。

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

Halley Ruppel, Amina Khan, Rose Mintor, Jessica Nguyen, Meghan McNamara, Brooke Luo, Michelle Kelly, Kenrick Cato, Elizabeth B Froh

引用次数: 0

An Information Extraction Approach to Detecting Novelty of Biomedical Publications. 一种生物医学出版物新颖性检测的信息提取方法。

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

Xueqing Peng, Brian Ondov, Huan He, Yan Hu, Hua Xu

{"title":"An Information Extraction Approach to Detecting Novelty of Biomedical Publications.","authors":"Xueqing Peng, Brian Ondov, Huan He, Yan Hu, Hua Xu","doi":"","DOIUrl":"","url":null,"abstract":"Scientific novelty plays a critical role in shaping research impact, yet it remains inconsistently defined and difficult to quantify. Existing approaches often reduce novelty to a single measure, failing to distinguish the specific types of contributions (such as new concepts or relationships) that drive influence. In this study, we introduce a semantic measure of novelty based on the emergence of new biomedical entities and relationships within the conclusion sections of research articles. Leveraging transformer-based named entity recognition (NER) and relation extraction (RE) tools, we identify novel findings and classify articles into four categories: No Novelty, Entity-only Novelty, Relation-only Novelty, and Entity-Relation Novelty. We evaluate this framework using citation counts and Journal Impact Factors (JIF) as proxies for research influence. Our results show that Entity-Relation Novelty articles receive the highest citation impact, with relation novelty more closely aligned with high-impact journals. These findings offer a scalable framework for assessing novelty and guiding future research evaluation.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1013-1022"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273132","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

Adaptive Constraint Relaxation in Personalized Nutrition Recommendations: An LLM-Driven Knowledge Graph Retrieval Approach. 个性化营养建议中的自适应约束放松：llm驱动的知识图谱检索方法。

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

Pengfei Zhang, Mohbat Fnu, Yutong Song, Oshani Seneviratne, Zhongqi Yang, Iman Azimi, Amir M Rahmani

{"title":"Adaptive Constraint Relaxation in Personalized Nutrition Recommendations: An LLM-Driven Knowledge Graph Retrieval Approach.","authors":"Pengfei Zhang, Mohbat Fnu, Yutong Song, Oshani Seneviratne, Zhongqi Yang, Iman Azimi, Amir M Rahmani","doi":"","DOIUrl":"","url":null,"abstract":"Personalized food recommendation systems must balance various constraints, including medical guidelines, nutritional needs, and individual preferences. However, existing methods often struggle with overly restrictive queries, frequently failing to generate recommendations when no exact match exists. To address this challenge, we propose an adaptive knowledge graph (KG) retrieval framework that integrates Large Language Models (LLMs) for intelligent constraint relaxation. Our approach dynamically prioritizes constraints, ensuring that critical dietary requirements remain intact while selectively relaxing less essential ones. By leveraging LLM-driven constraint analysis and structured relaxation strategies, our system significantly enhances recommendation coverage without compromising key dietary needs, while maintaining optimal recommendation performance. Experimental results on both the original and the extended-constraint dataset demonstrate that our method successfully retrieves recommendations in cases where previous approaches fail, achieving higher retrieval accuracy and a balanced tradeoff between flexibility and adherence to dietary constraints. The code is public available at https://github.com/zpf0117b2/adaptiveRetrieval.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1529-1538"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273098","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

Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports. 利用计算机断层成像和放射学报告进行胰腺癌的机会性筛查。

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

David Le, Ramon Correa-Medero, Amara Tariq, Bhavik Patel, Motoyo Yano, Imon Banerjee

引用次数: 0

Facilitating Clinical Information Extraction with Synthetic Data and Ontology using Large Language Models. 使用大型语言模型的综合数据和本体促进临床信息提取。

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

Yan Hu, Huan He, Qingyu Chen, Xiaoqian Jiang, Kirk Roberts, Hua Xu

{"title":"Facilitating Clinical Information Extraction with Synthetic Data and Ontology using Large Language Models.","authors":"Yan Hu, Huan He, Qingyu Chen, Xiaoqian Jiang, Kirk Roberts, Hua Xu","doi":"","DOIUrl":"","url":null,"abstract":"The rapid growth of unstructured clinical text in electronic health records necessitates robust information extraction systems, yet their development is hindered by the scarcity of high-quality annotated data. This study explores the potential of large language models to generate synthetic data for clinical named entity recognition and examines its impact on model performance. We propose a novel framework that integrates self-verified synthetic data generation with domain-specific semantic mapping using SNOMED-CT. By leveraging GPT-4o-mini for synthetic data creation and refining its quality through iterative verification and anomaly detection, we systematically evaluate the influence of synthetic data quality and quantity on fine-tuning LLaMA-3-8B. Experimental results across four datasets (MTSamples, UTP, MIMIC-III, and i2b2) demonstrate that self-verification and semantic mapping significantly enhance synthetic data utility, improving model generalizability. Our findings highlight the importance of balancing human-annotated and synthetic data, with a 1:1 ratio emerging as the optimal configuration for performance gains. This study advances clinical NLP by providing a scalable approach to mitigating annotation challenges while improving model performance.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"500-505"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273139","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

Prospective Validation of a Suicide Event Risk Model in Transgender Patients. 跨性别患者自杀事件风险模型的前瞻性验证。

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

Robert A Becker, Colin G Walsh

引用次数: 0