AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第7页

Automated Family Histories Significantly Improve Risk Prediction in an EHR. 电子病历中的自动家族病史可显著改善风险预测。

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

Xiayuan Huang, Ross Kleiman, David Page, Scott Hebbring

{"title":"Automated Family Histories Significantly Improve Risk Prediction in an EHR.","authors":"Xiayuan Huang, Ross Kleiman, David Page, Scott Hebbring","doi":"","DOIUrl":"","url":null,"abstract":"We recently demonstrated that electronically constructed family pedigrees (e-pedigrees) have great value in epidemiologic research using electronic health record (EHR) data. Prior to this work, it has been well accepted that family health history is a major predictor for a wide spectrum of diseases, reflecting shared effects of genetics, environment, and lifestyle. With the widespread digitalization of patient data via EHRs, there is an unprecedented opportunity to use machine learning algorithms to better predict disease risk. Although predictive models have previously been constructed for a few important diseases, we currently know very little about how accurately the risk for most diseases can be predicted. It is further unknown if the incorporation of e-pedigrees in machine learning can improve the value of these models. In this study, we devised a family pedigree-driven high-throughput machine learning pipeline to simultaneously predict risks for thousands of diagnosis codes using thousands of input features. Models were built to predict future disease risk for three time windows using both Logistic Regression and XGBoost. For example, we achieved average areas under the receiver operating characteristic curves (AUCs) of 0.82, 0.77 and 0.71 for 1, 6, and 24 months, respectively using XGBoost and without e-pedigrees. When adding e-pedigree features to the XGBoost pipeline, AUCs increased to 0.83, 0.79 and 0.74 for the same three time periods, respectively. E-pedigrees similarly improved the predictions when using Logistic Regression. These results emphasize the potential value of incorporating family health history via e-pedigrees into machine learning with no further human time.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"221-229"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201450","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

Effects of Added Emphasis and Pause in Audio Delivery of Health Information. 在健康信息音频传播中增加强调和暂停的效果。

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

Arif Ahmed, Gondy Leroy, Stephen A Rains, Philip Harber, David Kauchak, Prosanta Barai

引用次数: 0

Investigating Cross-Domain Binary Relation Classification in Biomedical Natural Language Processing. 研究生物医学自然语言处理中的跨域二元关系分类。

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

Alberto Purpura, Natasha Mulligan, Uri Kartoun, Eileen Koski, Vibha Anand, Joao Bettencourt-Silva

{"title":"Investigating Cross-Domain Binary Relation Classification in Biomedical Natural Language Processing.","authors":"Alberto Purpura, Natasha Mulligan, Uri Kartoun, Eileen Koski, Vibha Anand, Joao Bettencourt-Silva","doi":"","DOIUrl":"","url":null,"abstract":"This paper addresses the challenge of binary relation classification in biomedical Natural Language Processing (NLP), focusing on diverse domains including gene-disease associations, compound protein interactions, and social determinants of health (SDOH). We evaluate different approaches, including fine-tuning Bidirectional Encoder Representations from Transformers (BERT) models and generative Large Language Models (LLMs), and examine their performance in zero and few-shot settings. We also introduce a novel dataset of biomedical text annotated with social and clinical entities to facilitate research into relation classification. Our results underscore the continued complexity of this task for both humans and models. BERT-based models trained on domain-specific data excelled in certain domains and achieved comparable performance and generalization power to generative LLMs in others. Despite these encouraging results, these models are still far from achieving human-level performance. We also highlight the significance of high-quality training data and domain-specific fine-tuning on the performance of all the considered models.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"384-390"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201132","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

Selection and Implementation of Virtual Scribe Solutions to Reduce Documentation Burden: A Mixed Methods Pilot. 选择和实施虚拟抄写员解决方案以减轻文档记录负担：混合方法试验。

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

Carly Hudelson, Melissa A Gunderson, Debbie Pestka, Tori Christiaansen, Bret Stotka, Lynn Kissock, Rebecca Markowitz, Sameer Badlani, Genevieve B Melton

{"title":"Selection and Implementation of Virtual Scribe Solutions to Reduce Documentation Burden: A Mixed Methods Pilot.","authors":"Carly Hudelson, Melissa A Gunderson, Debbie Pestka, Tori Christiaansen, Bret Stotka, Lynn Kissock, Rebecca Markowitz, Sameer Badlani, Genevieve B Melton","doi":"","DOIUrl":"","url":null,"abstract":"Electronic health record (EHR) documentation is a leading reason for clinician burnout. While technology-enabled solutions like virtual and digital scribes aim to improve this, there is limited evidence of their effectiveness and minimal guidance for healthcare systems around solution selection and implementation. A transdisciplinary approach, informed by clinician interviews and other considerations, was used to evaluate and select a virtual scribe solution to pilot in a rapid iterative sprint over 12 weeks. Surveys, interviews, and EHR metadata were analyzed over a staggered 30 day implementation with live and asynchronous virtual scribe solutions. Among 16 pilot clinicians, documentation burden metrics decreased for some but not all. Some clinicians had highly positive comments, and others had concerns regarding scribe training and quality. Our findings demonstrate that virtual scribes may reduce documentation burden for some clinicians and describe a method for a collaborative and iterative technology selection process for digital tools in practice.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"230-238"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201261","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 Predicting Smoking Events for Just-in-time Interventions. 预测吸烟事件，进行及时干预。

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

Hang Yu, Michael Kotlyar, Paul Thuras, Sheena Dufresne, Serguei Vs Pakhomov

引用次数: 0

Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks. 利用大型语言模型生成合成数据，提高基于 BERT 的神经网络的性能。

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

Chancellor R Woolsey, Prakash Bisht, Joshua Rothman, Gondy Leroy

{"title":"Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks.","authors":"Chancellor R Woolsey, Prakash Bisht, Joshua Rothman, Gondy Leroy","doi":"","DOIUrl":"","url":null,"abstract":"An important problem impacting healthcare is the lack of available experts. Machine learning (ML) models may help resolve this by aiding in screening and diagnosing patients. However, creating large, representative datasets to train models is expensive. We evaluated large language models (LLMs) for data creation. Using Autism Spectrum Disorders (ASD), we prompted GPT-3.5 and GPT-4 to generate 4,200 synthetic examples of behaviors to augment existing medical observations. Our goal is to label behaviors corresponding to autism criteria and improve model accuracy with synthetic training data. We used a BERT classifier pretrained on biomedical literature to assess differences in performance between models. A random sample (N=140) from the LLM-generated data was also evaluated by a clinician and found to contain 83% correct behavioral example-label pairs. Augmenting the dataset increased recall by 13% but decreased precision by 16%. Future work will investigate how different synthetic data characteristics affect ML outcomes.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"429-438"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201271","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

Electronic Phenotyping of Urinary Tract Infections as a Silver Standard Label for Machine Learning. 将尿路感染电子表型作为机器学习的银标准标签

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

Stephen P Ma, Ebru Hosgur, Conor K Corbin, Ivan Lopez, Amy Chang, Jonathan H Chen

引用次数: 0

Pathophysiological Features in Electronic Medical Records Sustain Model Performance under Temporal Dataset Shift. 电子病历中的病理生理学特征可在数据集时空转移的情况下维持模型性能。

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

Raphael Brosula, Conor K Corbin, Jonathan H Chen

{"title":"Pathophysiological Features in Electronic Medical Records Sustain Model Performance under Temporal Dataset Shift.","authors":"Raphael Brosula, Conor K Corbin, Jonathan H Chen","doi":"","DOIUrl":"","url":null,"abstract":"Access to real-world data streams like electronic medical records (EMRs) has accelerated the development of supervised machine learning (ML) models for clinical applications. However, few studies investigate the differential impact of particular features in the EMR on model performance under temporal dataset shift. To explain how features in the EMR impact models over time, this study aggregates features into feature groups by their source (e.g. medication orders, diagnosis codes and lab results) and feature categories based on their reflection of patient pathophysiology or healthcare processes. We adapt Shapley values to explain feature groups' and feature categories' marginal contribution to initial and sustained model performance. We investigate three standard clinical prediction tasks and find that while feature contributions to initial performance differ across tasks, pathophysiological features help mitigate temporal discrimination deterioration. These results provide interpretable insights on how specific feature groups contribute to model performance and robustness to temporal dataset shift.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"95-104"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201197","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

Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic. 在 COVID-19 大流行中自动生成国际多因素自适应平台试验的病例报告表。

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

Andrew J King, Lisa Higgins, Carly Au, Salim Malakouti, Edvin Music, Kyle Kalchthaler, Gilles Clermont, William Garrard, David T Huang, Bryan J McVerry, Christopher W Seymour, Kelsey Linstrum, Amanda McNamara, Cameron Green, India Loar, Tracey Roberts, Oscar Marroquin, Derek C Angus, Christopher M Horvat

{"title":"Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.","authors":"Andrew J King, Lisa Higgins, Carly Au, Salim Malakouti, Edvin Music, Kyle Kalchthaler, Gilles Clermont, William Garrard, David T Huang, Bryan J McVerry, Christopher W Seymour, Kelsey Linstrum, Amanda McNamara, Cameron Green, India Loar, Tracey Roberts, Oscar Marroquin, Derek C Angus, Christopher M Horvat","doi":"","DOIUrl":"","url":null,"abstract":"Objectives: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial.Methods: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization.Results: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center.Conclusions: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"276-284"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201499","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

Designing a Consumer-centric Care Management Program by Prioritizing Interventions Using Deep Learning Causal Inference. 利用深度学习因果推理确定干预措施的优先次序，设计以消费者为中心的护理管理计划。

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

Tianhao Li, Haoyun Feng, Vikram Bandugula, Ying Ding

{"title":"Designing a Consumer-centric Care Management Program by Prioritizing Interventions Using Deep Learning Causal Inference.","authors":"Tianhao Li, Haoyun Feng, Vikram Bandugula, Ying Ding","doi":"","DOIUrl":"","url":null,"abstract":"Care management is a team-based and patient-centered approach to reduce health risks and improve outcomes for managed populations. Post Discharge Management (PDM) is an important care management program at Elevance Health, which is aimed at reducing 30-day readmission risk for recently discharged patients. The current PDM program suffers from low engagement. When assigning interventions to patients, case managers choose the interventions to be conducted in each call only based on their limited personal experiences. In this work, we use deep learning causal inference to analyze the impact of interventions conducted on the first call on consumer engagement in the PDM program, which provides a reliable reference for case managers to select interventions to promote consumer engagement. With three experiments cross-validating the results, our results show that consumers will engage more in the program if the case manager conducts interventions that require more nurse-patient interactions on the first call. On the other hand, conducting less interactive and more technical interventions on the first call leads to relatively poor consumer engagement. These findings correspond to the clinical sense of experienced nurses and are consistent with previous findings in patient engagement in hospital settings.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"584-592"},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200165","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