AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第6页

Subtyping Gastrointestinal Surgical Outcomes from Real World Data: A Comprehensive Analysis of UK Biobank. 从真实世界数据中对胃肠道手术结果进行细分：英国生物库综合分析。

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

Uri Kartoun, Kingsley Njoku, Tesfaye Yadete, Sivan Ravid, Eileen Koski, William Ogallo, Joao Bettencourt-Silva, Natasha Mulligan, Jianying Hu, Julia Liu, Thaddeus Stappenbeck, Vibha Anand

{"title":"Subtyping Gastrointestinal Surgical Outcomes from Real World Data: A Comprehensive Analysis of UK Biobank.","authors":"Uri Kartoun, Kingsley Njoku, Tesfaye Yadete, Sivan Ravid, Eileen Koski, William Ogallo, Joao Bettencourt-Silva, Natasha Mulligan, Jianying Hu, Julia Liu, Thaddeus Stappenbeck, Vibha Anand","doi":"","DOIUrl":"","url":null,"abstract":"Chronic gastrointestinal (GI) conditions, such as inflammatory bowel diseases (IBD), offer a promising opportunity to create classification systems that can enhance the accuracy of predicting the most effective therapies and prognosis for each patient. Here, we present a novel methodology to explore disease subtypes using our open-sourced BiomedSciAI toolkit. Applying methods available in this toolkit on the UK Biobank, including subpopulation-based feature selection and multi-dimensional subset scanning, we aimed to discover unique subgroups from GI surgery cohorts. Of a 12,073-patient cohort, a subgroup of 440 IBD patients was discovered with an increased risk of a subsequent GI surgery (OR: 2.21, 95% CI [1.81-2.69]). We iteratively demonstrate the discovery process using an additional cohort (with a narrower definition of GI surgery). Our results show that the iterative process can refine the subgroup discovery process and generate novel hypotheses to investigate determinants of treatment response.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"426-435"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467621","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

The SMART Text2FHIR Pipeline. SMART Text2FHIR 管道。

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

Timothy A Miller, Andrew J McMurry, James Jones, Daniel Gottlieb, Kenneth D Mandl

{"title":"The SMART Text2FHIR Pipeline.","authors":"Timothy A Miller, Andrew J McMurry, James Jones, Daniel Gottlieb, Kenneth D Mandl","doi":"","DOIUrl":"","url":null,"abstract":"Objective: To implement an open source, free, and easily deployable high throughput natural language processing module to extract concepts from clinician notes and map them to Fast Healthcare Interoperability Resources (FHIR). Materials and Methods: Using a popular open-source NLP tool (Apache cTAKES), we create FHIR resources that use modifier extensions to represent negation and NLP sourcing, and another extension to represent provenance of extracted concepts. Results: The SMART Text2FHIR Pipeline is an open-source tool, released through standard package managers, and publicly available container images that implement the mappings, enabling ready conversion of clinical text to FHIR. Discussion: With the increased data liquidity because of new interoperability regulations, NLP processes that can output FHIR can enable a common language for transporting structured and unstructured data. This framework can be valuable for critical public health or clinical research use cases. Conclusion: Future work should include mapping more categories of NLP-extracted information into FHIR resources and mappings from additional open-source NLP tools.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"514-520"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467631","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

Multimodal Pediatric Lymphoma Detection using PET and MRI. 利用正电子发射计算机断层显像和核磁共振成像进行小儿淋巴瘤多模态检测

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

Hongzhi Wang, Amirhossein Sarrami, Joy Tzung-Yu Wu, Lucia Baratto, Arjun Sharma, Ken C L Wong, Shashi Bhushan Singh, Heike E Daldrup-Link, Tanveer Syeda-Mahmood

引用次数: 0

Characterizing Autism Spectrum Disorder and Predicting Suicide Risk for Pediatric Psychiatric Emergency Services Encounters. 确定自闭症谱系障碍的特征并预测儿科精神科急诊就诊者的自杀风险。

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

Katherine A Brown, Kathleen R Donise, Mary Kathryn Cancilliere, Dilum P Aluthge, Elizabeth S Chen

引用次数: 0

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data. 通过聚类多变量临床时间序列数据识别创伤性脑损伤的生理状态。

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

Hamid Ghaderi, Brandon Foreman, Amin Nayebi, Sindhu Tipirneni, Chandan K Reddy, Vignesh Subbian

{"title":"Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data.","authors":"Hamid Ghaderi, Brandon Foreman, Amin Nayebi, Sindhu Tipirneni, Chandan K Reddy, Vignesh Subbian","doi":"","DOIUrl":"","url":null,"abstract":"Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"379-388"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467495","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

Local Contrastive Learning for Medical Image Recognition. 医学图像识别的局部对比学习

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

Syed A Rizvi, Ruixiang Tang, Xiaoqian Jiang, Xiaotian Ma, Xia Hu

引用次数: 0

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint. 通过患者-标准水平公平性约束实现公平的患者-试验匹配

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

Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, Na Zou

{"title":"Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint.","authors":"Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, Na Zou","doi":"","DOIUrl":"","url":null,"abstract":"Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"884-893"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139465532","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 Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records. 在电子健康记录中识别性少数群体和性别少数群体的可计算表型。

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

Yongqiu Li, Xing He, Christopher Wheldon, Yonghui Wu, Mattia Prosperi, Elizabeth A Shenkman, Michael S Jaffee, Jingchuan Guo, Fei Wang, Yi Guo, Jiang Bian

{"title":"A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records.","authors":"Yongqiu Li, Xing He, Christopher Wheldon, Yonghui Wu, Mattia Prosperi, Elizabeth A Shenkman, Michael S Jaffee, Jingchuan Guo, Fei Wang, Yi Guo, Jiang Bian","doi":"","DOIUrl":"","url":null,"abstract":"Sexual gender minorities, including lesbian, gay, and bisexual (LGB) individuals face unique challenges due to discrimination, stigma, and marginalization, which negatively impact their well-being. Electronic health record (EHR) systems present an opportunity for LGB research, but accurately identifying LGB individuals in EHRs is challenging. Our study developed and validated a rule-based computable phenotype (CP) to identify LGB individuals and their subgroups using both structured data and unstructured clinical narratives from a large integrated health system. Validating against a sample of 537 chart-reviewed patients, our three best performing CP algorithms balancing different performance metrics, each achieved sensitivity of 1.000, PPV of 0.982, and F1-score of 0.875 in identifying LGB individuals, respectively. Applying the three best-performing CPs, our study also found that the LGB population is younger and experiences a disproportionate burden of adverse health outcomes, particularly mental health distress.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"1057-1066"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467065","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 Computational Framework to Evaluate Emergency Department Clinician Task Switching in the Electronic Health Record Using Event Logs. 利用事件日志评估电子健康记录中急诊科临床医生任务切换的计算框架。

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

Amanda J Moy, Kenrick D Cato, Eugene Y Kim, Jennifer Withall, Sarah C Rossetti

{"title":"A Computational Framework to Evaluate Emergency Department Clinician Task Switching in the Electronic Health Record Using Event Logs.","authors":"Amanda J Moy, Kenrick D Cato, Eugene Y Kim, Jennifer Withall, Sarah C Rossetti","doi":"","DOIUrl":"","url":null,"abstract":"Workflow fragmentation, defined as task switching, may be one proxy to quantify electronic health record (EHR) documentation burden in the emergency department (ED). Few measures have been operationalized to evaluate task switching at scale. Theoretically grounded in the time-based resource-sharing model (TBRSM) which conceives task switching as proportional to the cognitive load experienced, we describe the functional relationship between cognitive load and the time and effort constructs previously applied for measuring documentation burden. We present a computational framework, COMBINE, to evaluate multilevel task switching in the ED using EHR event logs. Based on this framework, we conducted a descriptive analysis on task switching among 63 full-time ED physicians from one ED site using EHR event logs extracted between April-June 2021 (n=2,068,605 events) which were matched to scheduled shifts (n=952). On average, we found a high volume of event-level (185.8±75.3/hr) and within-(6.6±1.7/chart) and between-patient chart (27.5±23.6/hr) switching per shift worked.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2023 ","pages":"1183-1192"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785917/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467069","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 Practical Approach to Optimize Computerized Provider Order Entry Systems and Reduce Clinician Burden: Pre-Post Evaluation of Vendor-Derived "Order Friction" Data. 优化计算机化医嘱输入系统和减轻临床医生负担的实用方法：对供应商提供的 "订单摩擦 "数据进行事后评估。

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

Elise L Ruan, Sarah C Rossetti, Hanson Hsu, Eugene Y Kim, Richard C Trepp

引用次数: 0