AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第10页

The Association of Learning Health System Practicing Hospitals and other Health Information Interested Hospitals with Patient-Generated Health Data Uptake. 学习健康系统实践医院协会和其他有患者生成健康数据的健康信息感兴趣的医院。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Ibukun E Fowe, Neal T Wallace, Jeffrey Kaye

{"title":"The Association of Learning Health System Practicing Hospitals and other Health Information Interested Hospitals with Patient-Generated Health Data Uptake.","authors":"Ibukun E Fowe, Neal T Wallace, Jeffrey Kaye","doi":"","DOIUrl":"","url":null,"abstract":"Patient generated health data (PGHD) has been described as a necessary addition to provider-generated information for improving care processes in US hospitals. This study evaluated the distribution of Health Information Interested (HII) US hospitals that are more likely to capture or use PGHD. The literature suggests that HII hospitals are more likely to capture and use PGHD. Cross-sectional analysis of the 2018 American Hospital Association's (AHA) health-IT-supplement and other supporting datasets showed that HII hospitals collectively and majority of HII hospital subcategories evaluated were associated with increased PGHD capture and use. The full Learning Health System (LHS) hospital subcategory had the highest association and hospitals in the meaningful use stage three compliant (MU3) and PCORI funded subcategory also had higher rates of PGHD capture or use when in combination with LHS hospitals. Hence, being LHS appears to be the strongest practice and policy lever to increase PGHD capture and use.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283141/pdf/2055.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9711835","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

Characterizing Disparities in the Treatment of Intimate Partner Violence. 描述亲密伴侣暴力治疗中的差异。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Çerağ Oğuztüzün, Mehmet Koyutürk, Günnur Karakurt

{"title":"Characterizing Disparities in the Treatment of Intimate Partner Violence.","authors":"Çerağ Oğuztüzün, Mehmet Koyutürk, Günnur Karakurt","doi":"","DOIUrl":"","url":null,"abstract":"Exposure to Intimate Partner Violence (IPV) has lasting adverse effects on the physical, behavioral, cognitive, and emotional health of survivors. To this end, it is critical to understand the effectiveness of IPV treatment strategies in reducing IPV and its debilitating effects. Meta-analyses designed to comprehensively describe the effectiveness of treatments offer unique advantages. However, the heterogeneity within and between studies poses challenges in interpreting findings. Meta-analyses are therefore unlikely to identify the factors that underlie disparities in treatment efficacy. To characterize the effect of demographic and social factors on treatment effectiveness, we develop a comprehensive computational and statistical framework that uses Meta-regression to characterize the effect of demographic and social variables on treatment outcomes. The innovations in our methodology include (i) standardization of outcome variables to enable meaningful comparisons among studies, and (ii) two parallel meta-regression pipelines to reliably handle missing data.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283094/pdf/2326.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9710340","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

Enrichment of a Data Lake to Support Population Health Outcomes Studies Using Social Determinants Linked EHR Data. 利用社会决定因素相关的电子病历数据丰富数据湖以支持人口健康结果研究。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Md Kamruz Zaman Rana, Xing Song, Humayera Islam, Tanmoy Paul, Khuder Alaboud, Lemuel R Waitman, Abu S M Mosa

{"title":"Enrichment of a Data Lake to Support Population Health Outcomes Studies Using Social Determinants Linked EHR Data.","authors":"Md Kamruz Zaman Rana, Xing Song, Humayera Islam, Tanmoy Paul, Khuder Alaboud, Lemuel R Waitman, Abu S M Mosa","doi":"","DOIUrl":"","url":null,"abstract":"The integration of electronic health records (EHRs) with social determinants of health (SDoH) is crucial for population health outcome research, but it requires the collection of identifiable information and poses security risks. This study presents a framework for facilitating de-identified clinical data with privacy-preserved geocoded linked SDoH data in a Data Lake. A reidentification risk detection algorithm was also developed to evaluate the transmission risk of the data. The utility of this framework was demonstrated through one population health outcomes research analyzing the correlation between socioeconomic status and the risk of having chronic conditions. The results of this study inform the development of evidence-based interventions and support the use of this framework in understanding the complex relationships between SDoH and health outcomes. This framework reduces computational and administrative workload and security risks for researchers and preserves data privacy and enables rapid and reliable research on SDoH-connected clinical data for research institutes.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283101/pdf/2450.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10089108","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

Detection of Suicidal Behavior and Self-harm Among Children Presenting to Emergency Departments: A Tree-based Classification Approach. 检测急诊科就诊儿童的自杀行为和自残行为：基于树的分类方法

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Juliet B Edgcomb, Chi-Hong Tseng, Mengtong Pan, Alexandra Klomhaus, Bonnie Zima

{"title":"Detection of Suicidal Behavior and Self-harm Among Children Presenting to Emergency Departments: A Tree-based Classification Approach.","authors":"Juliet B Edgcomb, Chi-Hong Tseng, Mengtong Pan, Alexandra Klomhaus, Bonnie Zima","doi":"","DOIUrl":"","url":null,"abstract":"Suicide is the second leading cause of death of U.S. children over 10 years old. Application of statistical learning to structured EHR data may improve detection of children with suicidal behavior and self-harm. Classification trees (CART) were developed and cross-validated using mental health-related emergency department (MH-ED) visits (2015-2019) of children 10-17 years (N=600) across two sites. Performance was compared with the CDC Surveillance Case Definition ICD-10-CM code list. Gold-standard was child psychiatrist chart review. Visits were suicide-related among 284/600 (47.3%) children. ICD-10-CM detected cases with sensitivity 70.7 (95%CI 67.0-74.3), specificity 99.0 (98.8-100), and 85/284 (29.9%) false negatives. CART detected cases with sensitivity 85.1 (64.7-100) and specificity 94.9 (89.2-100). Strongest predictors were suicide-related code, MH- and suicide-related chief complaints, site, area deprivation index, and depression. Diagnostic codes miss nearly one-third of children with suicidal behavior and self-harm. Advances in EHR-based phenotyping have the potential to improve detection of childhood-onset suicidality.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283119/pdf/2295.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10089106","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

Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE. 利用 STREAMLINE 探索通过多模态脑成像进行认知结果预测的自动化机器学习。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Xinkai Wang, Yanbo Feng, Boning Tong, Jingxuan Bao, Marylyn D Ritchie, Andrew J Saykin, Jason H Moore, Ryan Urbanowicz, Li Shen

引用次数: 0

Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection. 避免标签选择情况下有偏差的临床机器学习模型性能评估

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Conor K Corbin, Michael Baiocchi, Jonathan H Chen

{"title":"Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection.","authors":"Conor K Corbin, Michael Baiocchi, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading. In this study we describe three classes of label selection and simulate five causally distinct scenarios to assess how particular selection mechanisms bias a suite of commonly reported binary machine learning model performance metrics. Simulations reveal that when selection is affected by observed features, naive estimates of model discrimination may be misleading. When selection is affected by labels, naive estimates of calibration fail to reflect reality. We borrow traditional weighting estimators from causal inference literature and find that when selection probabilities are properly specified, they recover full population estimates. We then tackle the real-world task of monitoring the performance of deployed machine learning models whose interactions with clinicians feed-back and affect the selection mechanism of the labels. We train three machine learning models to flag low-yield laboratory diagnostics, and simulate their intended consequence of reducing wasteful laboratory utilization. We find that naive estimates of AUROC on the observed population undershoot actual performance by up to 20%. Such a disparity could be large enough to lead to the wrongful termination of a successful clinical decision support tool. We propose an altered deployment procedure, one that combines injected randomization with traditional weighted estimates, and find it recovers true model performance.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283136/pdf/2405.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9703649","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

Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations. 将环境二氧化氮污染测量与电子健康记录数据联系起来研究哮喘恶化。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Alana Schreibman, Sherrie Xie, Rebecca A Hubbard, Blanca E Himes

{"title":"Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations.","authors":"Alana Schreibman, Sherrie Xie, Rebecca A Hubbard, Blanca E Himes","doi":"","DOIUrl":"","url":null,"abstract":"Electronic health record (EHR)-derived data can be linked to geospatially distributed socioeconomic and environmental factors to conduct large-scale epidemiologic studies. Ambient NO2 is a known environmental risk factor for asthma. However, health exposure studies often rely on data from geographically sparse regulatory monitors that may not reflect true individual exposure. We contrasted use of interpolated NO2 regulatory monitor data with raw satellite measurements and satellite-derived ground estimates, building on previous work which has computed improved exposure estimates from remotely sensed data. Raw satellite and satellite-derived ground measurements captured spatial variation missed by interpolated ground monitor measurements. Multivariable analyses comparing these three NO2 measurement approaches (interpolated monitor, raw satellite, and satellite-derived) revealed a positive relationship between exposure and asthma exacerbations for both satellite measurements. Exposure-outcome relationships using the interpolated monitor NO2 were inconsistent with known relationships to asthma, suggesting that interpolated monitor data might yield misleading results in small region studies.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283087/pdf/2145.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9832116","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

Principal Investigators' Perceptions on Factors Associated with Successful Recruitment in Clinical Trials. 主要研究人员对临床试验成功招募相关因素的看法。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Betina Idnay, Alex Butler, Yilu Fang, Ziran Li, Junghwan Lee, Casey Ta, Cong Liu, Brenda Ruotolo, Chi Yuan, Huanyao Chen, George Hripcsak, Elaine Larson, Chunhua Weng

{"title":"Principal Investigators' Perceptions on Factors Associated with Successful Recruitment in Clinical Trials.","authors":"Betina Idnay, Alex Butler, Yilu Fang, Ziran Li, Junghwan Lee, Casey Ta, Cong Liu, Brenda Ruotolo, Chi Yuan, Huanyao Chen, George Hripcsak, Elaine Larson, Chunhua Weng","doi":"","DOIUrl":"","url":null,"abstract":"Participant recruitment continues to be a challenge to the success of randomized controlled trials, resulting in increased costs, extended trial timelines and delayed treatment availability. Literature provides evidence that study design features (e.g., trial phase, study site involvement) and trial sponsor are significantly associated with recruitment success. Principal investigators oversee the conduct of clinical trials, including recruitment. Through a cross-sectional survey and a thematic analysis of free-text responses, we assessed the perceptions of sixteen principal investigators regarding success factors for participant recruitment. Study site involvement and funding source do not necessarily make recruitment easier or more challenging from the perspective of the principal investigators. The most commonly used recruitment strategies are also the most effort inefficient (e.g., in-person recruitment, reviewing the electronic medical records for prescreening). Finally, we recommended actionable steps, such as improving staff support and leveraging informatics-driven approaches, to allow clinical researchers to enhance participant recruitment.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283115/pdf/2207.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10070909","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

TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments. TRESTLE：可重复执行语音、文本和语言实验的工具包。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Changye Li, Weizhe Xu, Trevor Cohen, Martin Michalowski, Serguei Pakhomov

{"title":"TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments.","authors":"Changye Li, Weizhe Xu, Trevor Cohen, Martin Michalowski, Serguei Pakhomov","doi":"","DOIUrl":"","url":null,"abstract":"The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals. Valuable public data repositories such as TalkBank have made it possible for researchers in the computational community to join forces and learn from each other to make significant advances in this area. However, due to variability in approaches and data selection strategies used by various researchers, results obtained by different groups have been difficult to compare directly. In this paper, we present TRESTLE (Toolkit for Reproducible Execution of Speech Text and Language Experiments), an open source platform that focuses on two datasets from the TalkBank repository with dementia detection as an illustrative domain. Successfully deployed in the hackallenge (Hackathon/Challenge) of the International Workshop on Health Intelligence at AAAI 2022, TRESTLE provides a precise digital blueprint of the data pre-processing and selection strategies that can be reused via TRESTLE by other researchers seeking comparable results with their peers and current state-of-the-art (SOTA) approaches.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283131/pdf/2277.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9715633","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

Supporting Clinical Care: SOFA on FHIR. 支持临床护理：FHIR 上的 SOFA。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01

Margaux Gatrio, Ariane Morassi Sasso, Julian Sass, Prof Erwin Böttinger, Jonathan Antonio Edelman, Dr Philipp Landgraf

引用次数: 0