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Early Disease Prediction Using a Text-Numerical Hybrid Model Using Large-Scale Clinical Real-World Data. 使用大规模临床真实世界数据的文本-数字混合模型进行早期疾病预测。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Ayaka Oka, Tatsuya Yamaguchi, Masaki Ishihara, Takayuki Baba, Tatsuya Sato, Kazuki Iwamoto, Ryo Iwamura, Shigetaka Toma, Kaho Ogura, Masahiro Kimura, Hokuto Morohoshi, Akio Nakamura
{"title":"Early Disease Prediction Using a Text-Numerical Hybrid Model Using Large-Scale Clinical Real-World Data.","authors":"Ayaka Oka, Tatsuya Yamaguchi, Masaki Ishihara, Takayuki Baba, Tatsuya Sato, Kazuki Iwamoto, Ryo Iwamura, Shigetaka Toma, Kaho Ogura, Masahiro Kimura, Hokuto Morohoshi, Akio Nakamura","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>To assist physicians in predicting diseases, most natural language processing (NLP) models have focused on progress notes in electronic medical records with full descriptions from the initial stage of patient diagnosis to the final stage of discharge. However, accurately predicting diseases in the early stage using initial notes is challenging due to limited information. To address this, a text-numerical hybrid method is developed to improve disease prediction accuracy. The method identifies \"Reliably predicted diseases (RPD)\" that can be robustly predicted in the NLP and Random Forest models even if there are missing values in the numerical data or the amount of text data is small. Results show that, among the predicted disease groups of the two models, diseases matching the RPD are preferentially adopted and integrated. Precision@10 reveals that our developed method has a relatively higher accuracy of 67.0% than the traditional NLP model.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"885-893"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144554","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
MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering. MKRAG：医学知识检索增强生成医学问答。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Yucheng Shi, Shaochen Xu, Tianze Yang, Zhengliang Liu, Tianming Liu, Xiang Li, Ninghao Liu
{"title":"MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering.","authors":"Yucheng Shi, Shaochen Xu, Tianze Yang, Zhengliang Liu, Tianming Liu, Xiang Li, Ninghao Liu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks such as medical question answering (QA). In addition, LLMs tend to function as \"black-boxes\", making it challenging to modify their behavior. To address the problem, our work employs a transparent process of retrieval augmented generation (RAG), aiming to improve LLM responses without the need for fine-tuning or retraining. Specifically, we propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then inject them into the LLM's query prompt. Focusing on medical QA, we evaluate the impact of different retrieval models and the number of facts on LLM performance using the MedQA-SMILE dataset. Notably, our retrieval-augmented Vicuna-7B model exhibited an accuracy improvement from 44.46% to 48.54%. This work underscores the potential of RAG to enhance LLM performance, offering a practical approach to mitigate the challenges posed by black-box LLMs.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1011-1020"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144558","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
Examining Oral Anti-Cancer Medication Continuation Using Questionnaires, Prescription Refills, and Structured Electronic Health Records. 使用问卷调查、处方补充和结构化电子健康记录检查口服抗癌药物的延续。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Congning Ni, Qingyuan Song, Jeremy L Warner, Qingxia Chen, Lijun Song, S Trent Rosenbloom, Bradley A Malin, Zhijun Yin
{"title":"Examining Oral Anti-Cancer Medication Continuation Using Questionnaires, Prescription Refills, and Structured Electronic Health Records.","authors":"Congning Ni, Qingyuan Song, Jeremy L Warner, Qingxia Chen, Lijun Song, S Trent Rosenbloom, Bradley A Malin, Zhijun Yin","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Medication persistence is essential for the efficacy of treatment and patient health outcomes. This study investigates the discontinuation of oral anticancer medications (capecitabine, ibrutinib, or sunitinib) in a cohort that is well-characterized by medication discontinuation survey questionnaires, prescription refill data, and structured electronic health records (EHRs). We categorized discontinuation reasons based on a survey of patients taking medication, revealing that 38% of 257 patients completed therapy, while discontinuation was due primarily to no response to therapy and/or progression of disease leading to discontinuation (33%) and side effects/complication (9%). Survival analysis showed variable medication persistence, with capecitabine persistence decreasing significantly over time, to 0.08 in two years. A logistic regression model demonstrated strong capability (with an AUC of 0.835) to identify patients at risk for medication discontinuation. Our study shows the complexities of medication persistence and emphasizes the importance of understanding medication discontinuation patterns and leveraging predictive analytics to inform future research and clinical monitoring in the treatment of cancer.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"865-874"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144567","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
EHR Phenotyping Methods for Measuring Treatment Adherence Among People Living With HIV in All of Us: Towards Disparities and Inequalities in HIV Care Continuum. EHR表现型方法用于测量我们所有人的艾滋病毒感染者的治疗依从性：对艾滋病毒护理连续体的差异和不平等。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Yuanzhen Yue, Ashok Khanal, Tianchu Lyu, Sharon Weissman, Chen Liang
{"title":"EHR Phenotyping Methods for Measuring Treatment Adherence Among People Living With HIV in All of Us: Towards Disparities and Inequalities in HIV Care Continuum.","authors":"Yuanzhen Yue, Ashok Khanal, Tianchu Lyu, Sharon Weissman, Chen Liang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>HIV treatment adherence is among the most important determinants of HIV outcomes. However, only 50% of people living with HIV in the US were retained in care. Measuring HIV treatment adherence in the clinical settings is feasible but when it comes to the growing number of multi-site Electronic Health Records (EHR), there has been a dearth of research for adequate informatics methods to handle EHR. We sought to address this gap by developing a cluster of metrics for measuring HIV treatment adherence via EHR phenotyping methods. Our methods were developed and tested in the All of Us research program. We also performed preliminary analyses to explore disparities in HIV treatment adherence and demographic factors contributing to poor adherence. This study paves the way for systematic data mining and analyses for the HIV care continuum, disparities, and inequality research on All of Us and other EHR normalized with the OMOP Common Data Model.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1294-1302"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144574","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 Semantic and Structure Modeling of Diseases for Diagnosis Prediction. 面向疾病诊断预测的语义和结构建模。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Hang Lv, Zehai Chen, Yacong Yang, Shuyao Pan, Bo Xiong, Yanchao Tan, Carl Yang
{"title":"Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.","authors":"Hang Lv, Zehai Chen, Yacong Yang, Shuyao Pan, Bo Xiong, Yanchao Tan, Carl Yang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to forecast the potential diagnosis that patients may receive. However, existing studies usually ignore the fine-grained semantic and structure information in EHRs (e.g., the hierarchical relations between diseases and ICD-9 codes), which fails to provide accurate disease representation towards effective diagnosis prediction. To this end, we propose to enhance diagnosis prediction through LabCare, a framework with improved semantic and structure modeling of diseases in EHR data. LabCare can simultaneously capture rich semantic and structural relations among diseases and ICD-9 codes, which is achieved by innovatively integrating language models and box embeddings. Extensive experiments on two EHR datasets show that LabCare surpasses competitors, consistently achieving a 4.29% average improvement in Recall and NDCG metrics.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"758-767"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144603","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
Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data. 生成式人工智能在护理流程数据上演示了困难推理。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Courtney J Diamond, Jennifer Thate, Jennifer B Withall, Rachel Y Lee, Kenrick Cato, Sarah C Rossetti
{"title":"Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data.","authors":"Courtney J Diamond, Jennifer Thate, Jennifer B Withall, Rachel Y Lee, Kenrick Cato, Sarah C Rossetti","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Excessive documentation burden is linked to clinician burnout, thus motivating efforts to reduce burden. Generative artificial intelligence (AI) poses opportunities for burden reduction but requires rigorous assessment. We evaluated the ability of a large language model (LLM) (OpenAI's GPT-4) to interpret various intervention-response relationships presented on nursing flowsheets, assessing performance using MUC-5 evaluation metrics, and compared its assessments to those of nurse expert evaluators. ChatGPT correctly assessed 3 of 14 clinical scenarios, and partially correctly assessed 6 of 14, frequently omitting data from its reasoning. Nurse expert evaluators correctly assessed all relationships and provided additional language reflective of standard nursing practice beyond the intervention-response relationships evidenced in nursing flowsheets. Future work should ensure the training data used for electronic health record (EHR)-integrated LLMs includes all types of narrative nursing documentation that reflect nurses' clinical reasoning, and verification of LLM-based information summarization does not burden end-users.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"349-358"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144647","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
Policy Library Redundancy Analysis Using K-means Clustering. 基于k -均值聚类的策略库冗余分析。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Michael D Wendorf, Christopher I Macintosh
{"title":"Policy Library Redundancy Analysis Using K-means Clustering.","authors":"Michael D Wendorf, Christopher I Macintosh","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This capstone project investigates the application of artificial intelligence (AI) techniques, specifically sentence embedding and k-means clustering using large language models, to address the challenge of policy library redundancy within a healthcare setting. The project aimed to demonstrate the viability of using AI-assisted tools in policy library management, targeting a 5% reduction in the overall policy library at a large academic healthcare system. By collaborating with the accreditation team and developing a Python-script prototype, the study showed that AI-assisted methods could significantly enhance efficiency and reduce labor in policy library management. Results indicate a potential 4% reduction in library size, underscoring the method's effectiveness and the opportunity for further optimization. This research contributes to the emerging field of AI in healthcare administration, offering a scalable model for improving policy library management processes in various healthcare contexts.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1225-1234"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144665","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
Antimicrobial Resistance Patterns in an Urban County: a Spatiotemporal Exploration. 城市县域抗菌素耐药模式的时空探索
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Tanvi A Ingle, Lauren N Cooper, Alaina M Beauchamp, Abdi D Wakene, Christoph U Lehmann, Richard J Medford
{"title":"Antimicrobial Resistance Patterns in an Urban County: a Spatiotemporal Exploration.","authors":"Tanvi A Ingle, Lauren N Cooper, Alaina M Beauchamp, Abdi D Wakene, Christoph U Lehmann, Richard J Medford","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The Centers for Disease Control and Prevention has raised national alarm over five Antimicrobial Resistant Organisms (AMROs) considered urgent or serious threats to public safety. Understanding the prevalence and distribution of AMROs at a local level can inform the unique infection risks facing our communities. We conducted a retrospective, spatiotemporal analysis of AMRO prevalence across Tarrant County, Texas from 2010-2019. Using spatial autocorrelation tests, we identified that across five different AMRO subtypes, the Western half of Tarrant County experienced more hot spots than the Eastern half. Our Space-Time Permutation Models identified 35 unique AMRO clusters. Using logistic regression models, we found significant associations between Area Deprivation Index, a measure of socioeconomic disparity, and most AMRO clusters. These findings underscore the importance of residency location and temporal trends when treating and preventing AMRO infections.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"541-550"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144697","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
Clinician Perceptions of Generative Artificial Intelligence Tools and Clinical Workflows: Potential Uses, Motivations for Adoption, and Sentiments on Impact. 临床医生对生成人工智能工具和临床工作流程的看法：潜在用途，采用动机和影响的情绪。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Elise L Ruan, Aziz Alkattan, Noemie Elhadad, Sarah C Rossetti
{"title":"Clinician Perceptions of Generative Artificial Intelligence Tools and Clinical Workflows: Potential Uses, Motivations for Adoption, and Sentiments on Impact.","authors":"Elise L Ruan, Aziz Alkattan, Noemie Elhadad, Sarah C Rossetti","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Successful integration of Generative Artificial Intelligence (AI) into healthcare requires understanding of health professionals' perspectives, ideally through data-driven approaches. In this study, we use a semi-structured survey and mixed methods analyses to explore clinicians' perceptions on the utility of generative AI for all types of clinical tasks, familiarity and competency with generative AI tools, and sentiments regarding the potential impact of generative AI on healthcare. Analysis of 116 clinician responses found differing perceptions regarding the usefulness of generative AI across clinical workflows, with information gathering from external sources rated highest and communication rated lowest. Clinician-generated prompt suggestions focused most often on clinician decision making and were of mixed quality, with participants more familiar with generative AI suggesting more high-quality prompts. Sentiments regarding the impact of generative AI varied, particularly regarding trustworthiness and impact on bias. Thematic analysis of open-ended comments highlighted concerns about patient care and the role of clinicians.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"960-969"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144632","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 assignment of HIV+ patients into phenogroups from demography bound phenotype attack rates. 根据人口统计学结合的表型攻击率，将HIV+患者自动分配到表型组。
AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Nick Williams
{"title":"Automating assignment of HIV+ patients into phenogroups from demography bound phenotype attack rates.","authors":"Nick Williams","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Evidence based medicine and health data for policy should update statistical data modeling methods to take advantage of at-scale data. One challenge with at-scale data is information segmentation for clinical presentation discovery and cohort assignment. We use gradient boosting machine (GBM) to segment 94,379,175,015 diagnostic clinical events attributable to 283,632,789 Centers for Medicare and Medicaid Services beneficiaries over 22 observation years. Diagnostic events were aggregated into attack rates by demography and Phenome-wide association studies (PheWas) codes. Resulting attack rates were then segmented within a user defined clinical status of interest, in this case HIV status. 1,753,647 HIV+ beneficiaries were considered. The GBM model assigned 19,651,408 PheWas attack rates from 69,133,296 ICD attack rates into phenogroups/nodes.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1235-1244"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144700","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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