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

Leveraging A Clinical Dashboard and Process Mappings to Improve Treatment Access and Outcomes for Women Veterans with Urinary Incontinence. 利用临床仪表板和流程映射改善患有尿失禁的女性退伍军人的治疗途径和效果。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Grace Gao, Camille P Vaughan, Alayne D Markland, Kayla Reinicke, Neeraja Annavaram, Zachary Burningham

{"title":"Leveraging A Clinical Dashboard and Process Mappings to Improve Treatment Access and Outcomes for Women Veterans with Urinary Incontinence.","authors":"Grace Gao, Camille P Vaughan, Alayne D Markland, Kayla Reinicke, Neeraja Annavaram, Zachary Burningham","doi":"","DOIUrl":"","url":null,"abstract":"In support of the Improving Primary Care Understanding of Resources and Screening for Urinary Incontinence to Enhance Treatment initiative with the Veterans Health Administration, we developed a clinical dashboard to support primary care providers in identifying underdiagnosed, undertreated women Veterans with urinary incontinence. This paper describes our dashboard development and evaluation. We employed a user-centered design in determining dashboard requirements, interface design, and functionality. We invited early users at three pilot sites to formal usability reviews. We quantified the dashboard usability using the System Usability Scale and administered surveys and interviews for insights on performance. We employed process maps to uncover processes of end-users' dashboard engagements within local environments. User evaluations demonstrated the dashboard as a helpful instrument in identifying women Veterans with good to excellent usability performance. User feedback offers a user-driven pathway to develop our dashboard that supports clinicians to better care for women Veterans with urinary incontinence.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"359-368"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467517","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

Leveraging Unlabeled Clinical Data to Boost Performance of Risk Stratification Models for Suspected Acute Coronary Syndrome. 利用未标记的临床数据提高疑似急性冠状动脉综合征风险分层模型的性能。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Yutong Wu, David Conlan, Siegfried Perez, Anthony Nguyen

引用次数: 0

Outliers in diagnosis ratios: A clue toward possibly absent data. 诊断比率中的异常值：可能缺失数据的线索

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Dmitry Morozyuk, Mark G Weiner

引用次数: 0

Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records. 利用高通量机器学习模型检测电子健康记录中的敏感数据。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Kai Zhang, Xiaoqian Jiang

{"title":"Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records.","authors":"Kai Zhang, Xiaoqian Jiang","doi":"","DOIUrl":"","url":null,"abstract":"In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based sensitive variable identification systems that work on one database fail on another. To address this issue, our paper explores the use of machine learning algorithms to identify sensitive variables in structured data, thus facilitating the de-identification process. We made a key observation that the distributions of metadata of PHI fields and non-PHI fields are very different. Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data. We trained the model on a variety of large EHR databases from different data sources and found that our algorithm achieves 99% accuracy when detecting PHI-related fields for unseen datasets. The implications of our study are significant and can benefit industries that handle sensitive data.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"814-823"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467614","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

An Automated Strategy to Calculate Medication Regimen Complexity. 自动计算用药方案复杂性的策略。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Yuzhi Lu, Ariel R Green, Rosalphie Quiles, Casey Overby Taylor

引用次数: 0

Using Case Mix Index within Diagnosis-Related Groups to Evaluate Variation in Hospitalization Costs at a Large Academic Medical Center. 利用诊断相关组内的病例混合指数评估一家大型学术医疗中心的住院费用差异。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Selina Pi, Jonathan Masterson, Stephen P Ma, Conor K Corbin, Arnold Milstein, Jonathan H Chen

{"title":"Using Case Mix Index within Diagnosis-Related Groups to Evaluate Variation in Hospitalization Costs at a Large Academic Medical Center.","authors":"Selina Pi, Jonathan Masterson, Stephen P Ma, Conor K Corbin, Arnold Milstein, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"In analyzing direct hospitalization cost and clinical data from an academic medical center, commonly used metrics such as diagnosis-related group (DRG) weight explain approximately 37% of cost variability, but a substantial amount of variation remains unaccounted for by case mix index (CMI) alone. Using CMI as a benchmark, we isolate and target individual DRGs with higher than expected average costs for specific quality improvement efforts. While DRGs summarize hospitalization care after discharge, a predictive model using only information known before admission explained up to 60% of cost variability for two DRGs with a high excess cost burden. This level of variability likely reflects underlying patient factors that are not modifiable (e.g., age and prior comorbidities) and therefore less useful for health systems to target for intervention. However, the remaining unexplained variation can be inspected in further studies to discover operational factors that health systems can target to improve quality and value for their patients. Since DRG weights represent the expected resource consumption for a specific hospitalization type relative to the average hospitalization, the data-driven approach we demonstrate can be utilized by any health institution to quantify excess costs and potential savings among DRGs.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"1201-1208"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139466289","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

Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets. 了解医学文本到 SQL 模型和数据集的通用性。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Richard Tarbell, Kim-Kwang Raymond Choo, Glenn Dietrich, Anthony Rios

{"title":"Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets.","authors":"Richard Tarbell, Kim-Kwang Raymond Choo, Glenn Dietrich, Anthony Rios","doi":"","DOIUrl":"","url":null,"abstract":"Electronic medical records (EMRs) are stored in relational databases. It can be challenging to access the required information if the user is unfamiliar with the database schema or general database fundamentals. Hence, researchers have explored text-to-SQL generation methods that provide healthcare professionals direct access to EMR data without needing a database expert. However, currently available datasets have been essentially \"solved\" with state-of-the-art models achieving accuracy greater than or near 90%. In this paper, we show that there is still a long way to go before solving text-to-SQL generation in the medical domain. To show this, we create new splits of the existing medical text-to- SQL dataset MIMICSQL that better measure the generalizability of the resulting models. We evaluate state-of-the-art language models on our new split showing substantial drops in performance with accuracy dropping from up to 92% to 28%, thus showing substantial room for improvement. Moreover, we introduce a novel data augmentation approach to improve the generalizability of the language models. Overall, this paper is the first step towards developing more robust text-to-SQL models in the medical domain.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"669-678"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139465614","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

Computational Phenotyping of OMOP CDM Normalized EHR for Prenatal and Postpartum Episodes: An Informatics Framework and Clinical Implementation on All of Us. 产前和产后发作的 OMOP CDM 归一化电子病历的计算表型：我们所有人的信息学框架和临床实施。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Tianchu Lyu, Chen Liang

{"title":"Computational Phenotyping of OMOP CDM Normalized EHR for Prenatal and Postpartum Episodes: An Informatics Framework and Clinical Implementation on All of Us.","authors":"Tianchu Lyu, Chen Liang","doi":"","DOIUrl":"","url":null,"abstract":"The use of Electronic Health Records (EHR) in pregnancy care and obstetrics-gynecology (OB/GYN) research has increased in recent years. In pregnancy, timing is important because clinical characteristics, risks, and patient management are different in each stage of pregnancy. However, the difficulty of accurately differentiating pregnancy episodes and temporal information of clinical events presents unique challenges for EHR phenotyping. In this work, we introduced the concept of time relativity and proposed a comprehensive framework of computational phenotyping for prenatal and postpartum episodes based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We implemented it on the All of Us national EHR database and identified 6,280 pregnancies with accurate start and end dates among 5,399 female patients. With the ability to identify different episodes in pregnancy care, this framework provides new opportunities for phenotyping complex clinical events and gestational morbidities for pregnant women, thus improving maternal and infant health.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"1096-1104"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785883/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467400","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

Creating Augmented Reality Holograms for Polytrauma Patients Using 3D Slicer and Holomedicine Medical Image Platform. 利用 3D 切片机和全息医学图像平台为多发性创伤患者创建增强现实全息图像。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Wei-Shao Sun, Chun-Chuan Sun, Lorenzo Porta, Ting-Kai Yang, Shih-Hao Su, Shih-Hung Liu, Tsung-Hsin Chou, Shyr-Chyr Chen, Joshua Ho, Chien-Chang Lee

引用次数: 0

Development of a Knowledge Graph Embeddings Model for Pain. 开发疼痛知识图谱嵌入模型

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2024-01-11 eCollection Date: 2023-01-01

Jaya Chaturvedi, Tao Wang, Sumithra Velupillai, Robert Stewart, Angus Roberts

{"title":"Development of a Knowledge Graph Embeddings Model for Pain.","authors":"Jaya Chaturvedi, Tao Wang, Sumithra Velupillai, Robert Stewart, Angus Roberts","doi":"","DOIUrl":"","url":null,"abstract":"Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"299-308"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467441","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