AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第9页

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

APEA: A Type 1 Diabetes Self-Management Ambient-AI Assistance Tool that Bridges Trajectory Prediction, Interactive Explanation, and Just-in-Time Adaptive Intervention Action. APEA: 1型糖尿病自我管理环境-人工智能辅助工具，桥梁轨迹预测，互动解释和及时的适应性干预行动。

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

Kun-Yi Chen, Erin M Tallon, Chi-Ren Shyu

{"title":"APEA: A Type 1 Diabetes Self-Management Ambient-AI Assistance Tool that Bridges Trajectory Prediction, Interactive Explanation, and Just-in-Time Adaptive Intervention Action.","authors":"Kun-Yi Chen, Erin M Tallon, Chi-Ren Shyu","doi":"","DOIUrl":"","url":null,"abstract":"Helping patients self-managing diseases like type 1 diabetes (T1D) requires informatics tools delivering real-time predictions with explainable, actionable guidance. However, many healthcare AI solutions lack actionable recommendations and user-friendly explanations, limiting clinical impacts. We introduce APEA, a pediatric T1D self-management Ambient-AI assistance tool, integrating glucose multi-trajectory-scenarios Prediction, interactive, context-aware large language model Explanations, and just-in-time adaptive intervention policy optimization for Actionable real-time suggestions through reinforcement learning. Using T1DEXIP dataset (262 pediatric T1D patients, multi-center), our results showed improved glucose control outcomes: 45% over human management, 69% over infusion-pump management. Although constrained by small sample size and severe class imbalance, APEA addresses healthcare AI implementation gaps by bridging what might happen, what can be done about it, and why it makes clinical sense. APEA offers a transferable framework for other chronic conditions that demand continuous, personalized, just-in-time adaptive interventions.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"257-266"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12921402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273119","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

Navigating Variability in Prostate RT Planning: Real-Time Insights for Human-Centered CDS Design. 前列腺放射治疗计划中的导航变异性：以人为中心的CDS设计的实时洞察。

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

Meagan Foster, Elizabeth Byrd, Elizabeth Kwong, Anirudh Karunaker, Brian M Anderson, Michael C Repka, Ross McGurk, Shiva K Das, Lawrence B Marks, Lukasz Mazur

{"title":"Navigating Variability in Prostate RT Planning: Real-Time Insights for Human-Centered CDS Design.","authors":"Meagan Foster, Elizabeth Byrd, Elizabeth Kwong, Anirudh Karunaker, Brian M Anderson, Michael C Repka, Ross McGurk, Shiva K Das, Lawrence B Marks, Lukasz Mazur","doi":"","DOIUrl":"","url":null,"abstract":"Clinical variability in prostate radiation therapy (RT) planning is well documented, but little is known about how radiation oncologists experience and adapt to the factors that drive it. This study explores variability as a human-centered design challenge, with the goalofinformingclinicaldecision support (CDS) design through real-timeinsight into planning decisions. We conducted observation sessions with the think aloud method followed by semi-structured interviews with five radiation oncologists while they contoured prostate cases. Using the Systems Engineering Initiative for Patient Safety (SEIPS) framework, we thematically analyzed the contributors to variability across tasks, technology, and organizational conditions. Results suggest that variability arises not only from anatomical or guidelineambiguity, butalso fromindividual interpretations of inputs, variation in contouring decisions, andadaptive strategies such as reliance on prior experience and estimation under uncertainty. Findings support the design of context-sensitive CDS tools that reflect real-world clinical reasoning while preserving clinical flexibility.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"369-375"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273002","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

Harmonizing Medicare Claims Data with OMOP: A Validated ETL Pipeline. 协调医疗保险索赔数据与OMOP：一个有效的ETL管道。

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

Yao An Lee, Ying Lu, Jiang Bian, Jingchuan Serena Guo, Xing He

引用次数: 0

Mining Social Media Data for Influenza Vaccine Effectiveness Using a Large Language Model and Chain-of-Thought Prompting. 使用大型语言模型和思维链提示挖掘流感疫苗有效性的社交媒体数据。

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

Dongfang Xu, Guillermo López García, Karen O'Connor, Haily Holston, Ari Z Klein, Ivan Flores Amaro, Matthew Scotch, Graciela Gonzalez-Hernandez

{"title":"Mining Social Media Data for Influenza Vaccine Effectiveness Using a Large Language Model and Chain-of-Thought Prompting.","authors":"Dongfang Xu, Guillermo López García, Karen O'Connor, Haily Holston, Ari Z Klein, Ivan Flores Amaro, Matthew Scotch, Graciela Gonzalez-Hernandez","doi":"","DOIUrl":"","url":null,"abstract":"Influenza vaccine effectiveness (VE) estimation plays a critical role in public health decision-making by quantifying the real-world impact of vaccination campaigns and guiding policy adjustments. Current approaches to VE estimation are constrained by limited population representation, selection bias, and delayed reporting. To address some of these gaps, we propose leveraging large language models (LLMs) with few-shot chain-of-thought (CoT) prompting to mine social media data for real-time influenza VE estimation. We annotated over 4,000 tweets from the 2020-2021 flu season using structured guidelines, achieving high inter-annotator agreement. Our best prompting strategy achieves F1 scores above 87% for identifying influenza vaccination status and test outcomes, outperforming traditional supervised fine-tuning methods by large margins. These findings indicate that LLM-based prompting approaches effectively identify relevant social media information for influenza VE estimation, offering a valuable real-time surveillance tool that complements traditional epidemiological methods.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1404-1413"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273038","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

Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models. 使用推理大语言模型从初始精神病评估笔记中解释自杀表型。

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

Zehan Li, Wanjing Wang, Lokesh Shahani, Rodrigo M Vieira, Salih Selek, Jair Soares, Hongfang Liu, Ming Huang

{"title":"Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models.","authors":"Zehan Li, Wanjing Wang, Lokesh Shahani, Rodrigo M Vieira, Salih Selek, Jair Soares, Hongfang Liu, Ming Huang","doi":"","DOIUrl":"","url":null,"abstract":"Clinical phenotyping is the process of extracting patient's observable symptoms and traits to better understand their disease condition. Suicide phenotyping focuses more on behavioral and cognitive characteristics, such as suicide ideation, attempt, and self-injury, to identify suicide risks and improve interventions. In this study, we leveraged the latest reasoning models, namely 4o, o1, and o3-mini, to perform note-level multi-label classification and reasoning generation tasks using previously annotated psychiatric evaluation notes from a safety-net psychiatric inpatient hospital in Harris County, Texas. Compared with the previously finetuned GPT-3.5 model, the out-of-box reasoning models prompted with in-context learning achieved comparable and better performance, with the highest accuracy of 0.94 and F1 of 0.90. We implemented novel clinical justification generation from these models on the traditional classification tasks. This finding marked a promising direction for performing clinical phenotyping that is interpretable and actionable using smaller, efficient reasoning models.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"774-784"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273075","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

A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data. 基于电子健康记录和ZIP-Level数据的阿片类药物使用障碍治疗选择模型

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

Leigh Anne Tang, Kristopher A Kast, Colin G Walsh

{"title":"A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data.","authors":"Leigh Anne Tang, Kristopher A Kast, Colin G Walsh","doi":"","DOIUrl":"","url":null,"abstract":"Background: Buprenorphine and methadone are effective medications for opioid use disorder (OUD) but remain underused, particularly when specialists are not leading care decisions. Objective: We developed a predictive model to guide treatment selection for OUD. Methods: Models predicted the probability of treatment response for each medication, which we defined as the absence of an adverse outcome during hospitalization and within 90 days of hospital discharge. Models considered electronic health record (EHR) and ZIP-level data. We constructed generalized linear regression, random forest, gradient boosted machines, and deep learning models and tested different combinations of EHR and ZIP-level data using early and late fusion methods. Results: EHR-only models performed better than ZIP-only models did. ZIP-level data did not significantly improve the performance of EHR-only models. Models consistently recommended buprenorphine over methadone. Conclusion: Future work should explore different approaches to modeling OUD treatment response and capturing relevant social and external factors.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1239-1248"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273121","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

Refining Substance Use Classification: An Ontological Framework for Enhancing Large-Scale Data Collection. 细化物质使用分类：加强大规模数据收集的本体论框架。

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

Chi-Hua Lu, Kenneth E Leonard, Werner Ceusters

引用次数: 0

Variogram Modeling of Spatially Variant Early Response to Therapy in Advanced Non-Small Cell Lung Cancer. 晚期非小细胞肺癌早期治疗反应空间变异的变异图建模。

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

Faisal Yaseen, Daniel S Hippe, Parth V Soni, Shouyi Wang, Chunyan Duan, John H Gennari, Stephen R Bowen

引用次数: 0

Evaluating the Effectiveness of Complementary and Integrative Health Therapies in Preventing Postpartum Depression: A Target Trial Emulation Study. 评价补充和综合健康疗法预防产后抑郁症的有效性：一项目标试验模拟研究。

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

Huixue Zhou, Yiye Zhang, Zhenxing Xu, Chang Su, Kelvin Lim, Andrea Johnson, Nili Solomonov, Fei Wang, Rui Zhang

{"title":"Evaluating the Effectiveness of Complementary and Integrative Health Therapies in Preventing Postpartum Depression: A Target Trial Emulation Study.","authors":"Huixue Zhou, Yiye Zhang, Zhenxing Xu, Chang Su, Kelvin Lim, Andrea Johnson, Nili Solomonov, Fei Wang, Rui Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Subject: This study aims to evaluate the effectiveness of Complementary and Integrative Health (CIH) therapies in reducing the incidence and severity of Postpartum Depression (PPD) using real-world data and target trial emulation. Methods: Using electronic health records (EHR) from a large healthcare system, we emulated target trials for CIH approaches including acupuncture, chiropractic, aromatherapy, and omega-3 fatty acids. CIH usage was identified and extracted from clinical notes using natural language processing (NLP) techniques. Logistic regression-based propensity score matching was employed to address confounding factors. The primary outcome was the incidence of PPD within 12 months postpartum, defined by diagnostic codes or antidepressant initiation. Secondary outcomes included changes in PHQ-9 scores and subgroup analyses by treatment type. Results: For the primary outcome, none of the treatments significantly reduced PPD risk intervals (CIs). However, omega-3 fatty acids and chiropractic care significantly reduced PHQ-9 scores in the treatment groups (omega-3 fatty acids: p<0.001, chiropractic care: p = 0.021), with no comparable improvements in controls. Aromatherapy showed mixed results, with reduced severe depression in the treatment group but increased severity in controls. Acupuncture had no significant effect (p > 0.05). These findings suggest that omega-3 fatty acids and chiropractic care may alleviate PPD symptoms, while the effects of aromatherapy, acupuncture and chiropractic remain inconclusive and warrant further investigation. Conclusion: This study provides approach to evaluating CIH interventions in real-world settings. These findings underscore the importance of integrating non-traditional treatment options into clinical practice to improve outcomes for individuals affected by PPD.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1557-1566"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273120","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