AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献

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The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support. 人工智能在临床决策支持中整合电子健康记录和患者生成数据的应用中的作用。
Jiancheng Ye, Donna Woods, Neil Jordan, Justin Starren
{"title":"The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support.","authors":"Jiancheng Ye, Donna Woods, Neil Jordan, Justin Starren","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"459-467"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201285","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
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。
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":"<p><p>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.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"314-323"},"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
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。
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":"<p><p>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%.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"419-428"},"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
Identifying and Characterizing the Transgender and Nonbinary Population Presenting to Pediatric Psychiatry Emergency Services. 识别和描述向儿科精神科急诊服务求诊的变性和非二元人群。
Wyatt Kim, Kathleen R Donise, Katherine A Brown, Mary Kathryn Cancilliere, Elizabeth S Chen
{"title":"Identifying and Characterizing the Transgender and Nonbinary Population Presenting to Pediatric Psychiatry Emergency Services.","authors":"Wyatt Kim, Kathleen R Donise, Katherine A Brown, Mary Kathryn Cancilliere, Elizabeth S Chen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Transgender and nonbinary (TGNB) individuals have an increased risk of certain mental health outcomes, such as depression and suicide attempts. This population skews younger in the United States and prior studies have not included TGNB patients for the entire pediatric age range in an emergency department (ED) setting. The present study aimed to examine gender identity documentation in the electronic health record and then use that information to identify and further characterize the pediatric TGNB population presenting to a psychiatric emergency service. Preliminary findings include a greater percentage of TGNB patients compared to non-TGNB individuals who had repeat visits to the ED for high acuity psychiatric concerns. A larger portion of TGNB patients also had at least one evaluation that included suicidal ideation. These results call for increased attention on the quality of mental healthcare for TGNB youth both inside and outside of the ED.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"565-574"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201043","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
Interpretability Study for Long Interview Transcripts from Behavior Intervention Sessions for Family Caregivers of Dementia Patients. 痴呆症患者家庭护理人员行为干预课程长访谈记录的可解读性研究。
Weiqing He, Bojian Hou, George Demiris, Li Shen
{"title":"Interpretability Study for Long Interview Transcripts from Behavior Intervention Sessions for Family Caregivers of Dementia Patients.","authors":"Weiqing He, Bojian Hou, George Demiris, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Mental health challenges are significant global public health concerns, affecting millions of people and impacting individuals, families, and communities alike. Therapists play a crucial role in supporting those with mental health issues by providing emotional, practical, and financial assistance, as well as facilitating access to treatment and services. Utilizing one-to-one interviews is an effective approach that yields valuable transcripts for further study. In this paper, we focus on interview transcripts between therapists and caregivers with family members suffering from dementia. We propose a method to efficiently handle long interview transcripts for classification. Then we employ the Shapley-value based interpretability technique to identify important contents that significantly contribute to classification results and build a corpus containing sentences potentially beneficial to the therapy. This approach offers valuable insights for enhancing the treatment of mental health issues.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"201-210"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201061","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。
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":"<p><p>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.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"652-661"},"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)。
Nripendra Acharya, Karthik Natarajan
{"title":"Development and Validation of an Individual Socioeconomic Deprivation Index (ISDI) in the NIH's <i>All of Us</i> Data Network.","authors":"Nripendra Acharya, Karthik Natarajan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Many of the existing composite social determinant of health indices, such as Area Deprivation Index, are constrained by their reliance on geographic approximations and American Community Survey data. This study builds on the body of literature around deprivation indices to construct an individual socioeconomic deprivation index (ISDI) within the NIH's All of Us Data Network by using weighted multiple correspondence analysis on SDOH data elements collected at the participant level. In this study, the correlation between ISDI and another area-approximated index is assessed to the extent possible, along with the changes in an AI models performance due to stratified sampling based on ISDI quintiles. Individual level deprivation indices may have a wide range of utility particularly in the context of precision medicine in both centralized and distributed data networks.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"36-45"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200415","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
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。
Samaya S Badrieh, Lisa Bastarache, Xinnan Niu, Jing He, Jamie R Robinson
{"title":"Driving Precision of Pediatric VTE Risk-stratification through Genetics.","authors":"Samaya S Badrieh, Lisa Bastarache, Xinnan Niu, Jing He, Jamie R Robinson","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This study addresses rising incidence of pediatric venous thromboembolism by validating a VTE phenotype and developing a polygenic risk score (PRS) using UK Biobank data. Our findings demonstrate predictive value of the PRS, enhancing VTE risk assessment in clinical settings. Future steps involve integrating the PRS into risk stratification models.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"498"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200627","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
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。
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":"<p><p>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.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"239-248"},"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》。
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":"<p><p>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.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"334-343"},"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
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