AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第2页

Using Discrete Event Simulation to Design and Assess an AI-aided Workflow for Same-day Diagnostic Testing of Women Undergoing Breast Screening. 利用离散事件模拟设计和评估人工智能辅助工作流程，为接受乳腺筛查的妇女提供当天诊断测试。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Yannan Lin, Anne C Hoyt, Vladimir G Manuel, Moira Inkelas, William Hsu

{"title":"Using Discrete Event Simulation to Design and Assess an AI-aided Workflow for Same-day Diagnostic Testing of Women Undergoing Breast Screening.","authors":"Yannan Lin, Anne C Hoyt, Vladimir G Manuel, Moira Inkelas, William Hsu","doi":"","DOIUrl":"","url":null,"abstract":"The process of patients waiting for diagnostic examinations after an abnormal screening mammogram is inefficient and anxiety-inducing. Artificial intelligence (AI)-aided interpretation of screening mammography could reduce the number of recalls after screening. We proposed a same-day diagnostic workup to alleviate patient anxiety by employing an AI-aided interpretation to reduce unnecessary diagnostic testing after an abnormal screening mammogram. However, the potential unintended consequences of introducing this workflow in a high-volume breast imaging center are unknown. Using discrete event simulation, we observed that implementing the AI-aided screening mammogram interpretation and same-day diagnostic workflow would reduce daily patient volume by 4%, increase the time a patient would be at the clinic by 24%, and increase waiting times by 13-31%. We discuss how changing the hours of operation and introducing new imaging equipment and personnel may alleviate these negative impacts.","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":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201267","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

FAIR privacy-preserving operation of large genomic variant calling format (VCF) data without download or installation. 无需下载或安装，即可对大型基因组变异调用格式 (VCF) 数据进行 FAIR 隐私保护操作。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Yasmmin C Martins, Praphulla Ms Bhawsar, Jeya B Balasubramanian, Daniel Russ, Wendy Sw Wong, Wolfgang Maass, Jonas S Almeida

{"title":"FAIR privacy-preserving operation of large genomic variant calling format (VCF) data without download or installation.","authors":"Yasmmin C Martins, Praphulla Ms Bhawsar, Jeya B Balasubramanian, Daniel Russ, Wendy Sw Wong, Wolfgang Maass, Jonas S Almeida","doi":"","DOIUrl":"","url":null,"abstract":"Motivation: The proliferation of genetic testing and consumer genomics represents a logistic challenge to the personalized use of GWAS data in VCF format. Specifically, the challenge of retrieving target genetic variation from large compressed files filled with unrelated variation information. Compounding the data traversal challenge, privacy-sensitive VCF files are typically managed as large stand-alone single files (no companion index file) composed of variable-sized compressed chunks, hosted in consumer-facing environments with no native support for hosted execution. Results: A portable JavaScript module was developed to support in-browser fetching of partial content using byte-range requests. This includes on-the-fly decompressing irregularly positioned compressed chunks, coupled with a binary search algorithm iteratively identifying chromosome-position ranges. The in-browser zero-footprint solution (no downloads, no installations) enables the interoperability, reusability, and user-facing governance advanced by the FAIR principles for stewardship of scientific data. Availability - https://episphere.github.io/vcf, including supplementary material.","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":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200821","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

Comparative Analysis of Fusion Strategies for Imaging and Non-imaging Data - Use-case of Hospital Discharge Prediction. 成像与非成像数据融合策略的比较分析--以出院预测为例。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Vedant Parikh, Amara Tariq, Bhavik Patel, Imon Banerjee

{"title":"Comparative Analysis of Fusion Strategies for Imaging and Non-imaging Data - Use-case of Hospital Discharge Prediction.","authors":"Vedant Parikh, Amara Tariq, Bhavik Patel, Imon Banerjee","doi":"","DOIUrl":"","url":null,"abstract":"Accurate prediction of future clinical events such as discharge from hospital can not only improve hospital resource management but also provide an indicator of a patient's clinical condition. Within the scope of this work, we perform a comparative analysis of deep learning based fusion strategies against traditional single source models for prediction of discharge from hospital by fusing information encoded in two diverse but relevant data modalities, i.e., chest X-ray images and tabular electronic health records (EHR). We evaluate multiple fusion strategies including late, early and joint fusion in terms of their efficacy for target prediction compared to EHR-only and Image-only predictive models. Results indicated the importance of merging information from two modalities for prediction as fusion models tended to outperform single modality models and indicate that the joint fusion scheme was the most effective for target prediction. Joint fusion model merges the two modalities through a branched neural network that is jointly trained in an end-to-end fashion to extract target-relevant information from both modalities.","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":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199535","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

Development and Validation of an Individual Socioeconomic Deprivation Index (ISDI) in the NIH's All of Us Data Network. 在美国国立卫生研究院的 "我们所有人 "数据网络中开发和验证个人社会经济贫困指数 (ISDI)。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Nripendra Acharya, Karthik Natarajan

引用次数: 0

Driving Precision of Pediatric VTE Risk-stratification through Genetics. 通过遗传学推动儿科 VTE 风险分级的精确性。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Samaya S Badrieh, Lisa Bastarache, Xinnan Niu, Jing He, Jamie R Robinson

引用次数: 0

Pre-test Prediction of Non-ischemic Cardiomyopathies using Time-Series EHR Data. 利用时间序列电子病历数据对非缺血性心肌病进行测试前预测。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Kary Ishwaran, Bryan Q Abadie, Po-Hao Chen, Michael Bolen, Tara Karamlou, Richard Grimm, W H Wilson Tang, Christopher Nguyen, Deborah Kwon, David Chen

{"title":"Pre-test Prediction of Non-ischemic Cardiomyopathies using Time-Series EHR Data.","authors":"Kary Ishwaran, Bryan Q Abadie, Po-Hao Chen, Michael Bolen, Tara Karamlou, Richard Grimm, W H Wilson Tang, Christopher Nguyen, Deborah Kwon, David Chen","doi":"","DOIUrl":"","url":null,"abstract":"Clinical imaging is an important diagnostic test to diagnose non-ischemic cardiomyopathies (NICM). However, accurate interpretation of imaging studies often requires readers to review patient histories, a time consuming and tedious task. We propose to use time-series analysis to predict the most likely NICMs using longitudinal electronic health records (EHR) as a pseudo-summary of EHR records. Time-series formatted EHR data can provide temporality information important towards accurate prediction of disease. Specifically, we leverage ICD-10 codes and various recurrent neural network architectures for predictive modeling. We trained our models on a large cohort of NICM patients who underwent cardiac magnetic resonance imaging (CMR) and a smaller cohort undergoing echocardiogram. The performance of the proposed technique achieved good micro-area under the curve (0.8357), F1 score (0.5708) and precision at 3 (0.8078) across all models for cardiac magnetic resonance imaging (CMR) but only moderate performance for transthoracic echocardiogram (TTE) of 0.6938, 0.4399 and 0.5864 respectively. We show that our model has the potential to provide accurate pre-test differential diagnosis, thereby potentially reducing clerical burden on physicians.","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":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201203","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 Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction. 针对类不平衡数据，通过模型复杂性驱动的类比例调整增强临床预测建模：阿片类药物过量预测实证研究》。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Yinan Liu, Xinyu Dong, Weimin Lyu, Richard N Rosenthal, Rachel Wong, Tengfei Ma, Jun Kong, Fusheng Wang

{"title":"Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction.","authors":"Yinan Liu, Xinyu Dong, Weimin Lyu, Richard N Rosenthal, Rachel Wong, Tengfei Ma, Jun Kong, Fusheng Wang","doi":"","DOIUrl":"","url":null,"abstract":"Class imbalance issues are prevalent in the medical field and significantly impact the performance of clinical predictive models. Traditional techniques to address this challenge aim to rebalance class proportions. They generally assume that the rebalanced proportions are derived from the original data, without considering the intricacies of the model utilized. This study challenges the prevailing assumption and introduces a new method that ties the optimal class proportions to model complexity. This approach allows for individualized tuning of class proportions for each model. Our experiments, centered on the opioid overdose prediction problem, highlight the performance gains achieved by this approach. Furthermore, rigorous regression analysis affirms the merits of the proposed theoretical framework, demonstrating a statistically significant correlation between hyperparameters controlling model complexity and the optimal class proportions.","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":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201743","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

Best of Both Worlds: Bridging One Model for All and Group-Specific Model Approaches using Ensemble-based Subpopulation Modeling. 两全其美：利用基于集合的子群体建模，将 "一个模型适用于所有群体 "和 "特定群体模型 "方法结合起来。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Purity Mugambi, Stephanie Carreiro

{"title":"Best of Both Worlds: Bridging One Model for All and Group-Specific Model Approaches using Ensemble-based Subpopulation Modeling.","authors":"Purity Mugambi, Stephanie Carreiro","doi":"","DOIUrl":"","url":null,"abstract":"Subpopulation models have become of increasing interest in prediction of clinical outcomes because they promise to perform better for underrepresented patient subgroups. However, the personalization benefits gained from these models tradeoff their statistical power, and can be impractical when the subpopulation's sample size is small. We hypothesize that a hierarchical model in which population information is integrated into subpopulation models would preserve the personalization benefits and offset the loss of power. In this work, we integrate ideas from ensemble modeling, personalization, and hierarchical modeling and build ensemble-based subpopulation models in which specialization relies on whole group samples. This approach significantly improves the precision of the positive class, especially for the underrepresented subgroups, with minimal cost to the recall. It consistently outperforms one model for all and one model for each subgroup approaches, especially in the presence of a high class-imbalance, for subgroups with at least 380 training samples.","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":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201518","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

Measuring and Reducing Racial Bias in a Pediatric Urinary Tract Infection Model. 在小儿尿路感染模型中测量并减少种族偏见。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Joshua W Anderson, Nader Shaikh, Shyam Visweswaran

引用次数: 0

Deep Learning Approaches to Predict Exercise Exertion Levels Using Wearable Physiological Data. 利用可穿戴生理数据预测运动消耗水平的深度学习方法。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01

Aref Smiley, Joseph Finkelstein

{"title":"Deep Learning Approaches to Predict Exercise Exertion Levels Using Wearable Physiological Data.","authors":"Aref Smiley, Joseph Finkelstein","doi":"","DOIUrl":"","url":null,"abstract":"Using physiological data from wearable devices, the study aimed to predict exercise exertion levels by building deep learning classification and regression models. Physiological data were obtained using an unobtrusive chest-worn ECG sensor and portable pulse oximeter from healthy individuals who performed 16-minute cycling exercise sessions. During each session, real-time ECG, pulse rate, oxygen saturation, and revolutions per minute (RPM) data were collected at three intensity levels. Subjects' ratings of perceived exertion (RPE) were collected once per minute. Each 16-minute exercise session was divided into eight 2-minute windows. The self-reported RPEs, heart rate, RPMs, and oxygen saturation levels were averaged for each window to form the predictive features. In addition, heart rate variability (HRV) features were extracted from the ECG for each window. Different feature selection algorithms were used to choose top-ranked predictors. The best predictors were then used to train and test deep learning models for regression and classification analysis. Our results showed the highest accuracy and F1 score of 98.2% and 98%, respectively in training the models. For testing the models, the highest accuracy and F1 score were 80%.","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":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200160","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