AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献_第5页

Temporal Recurrent Neural Networks for Predicting Acute Kidney Injury Recovery by Time of Discharge. 时间递归神经网络预测急性肾损伤出院时间恢复。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Nathan M Tran, Zaid Yousif, Ambarish Athavale, Shamim Nemati, Etienne Macedo

{"title":"Temporal Recurrent Neural Networks for Predicting Acute Kidney Injury Recovery by Time of Discharge.","authors":"Nathan M Tran, Zaid Yousif, Ambarish Athavale, Shamim Nemati, Etienne Macedo","doi":"","DOIUrl":"","url":null,"abstract":"Acute Kidney Injury (AKI) is a common complication in hospitalized patients and is associated with increased in-hospital mortality, readmission, and chronic kidney disease. Early identification of patients at risk of AKI non-recovery can improve discharge planning and follow-up. Using a retrospective cohort of 7,667 patient encounters diagnosed with AKI from the University of California San Diego Health, we compared traditional machine learning (ML) and temporal deep learning (DL) models to predict three AKI recovery outcomes: Recovery, Partial Recovery, and Non-Recovery. The ML models evaluated were Logistic Regression, Random Forest, and XGBoost; while, the DL models were Gated Recurrent Unit (GRU) and Long-Short Term Memory. On the test set, DL models consistently outperformed traditional ML approaches. The GRU model achieved the highest Macro-Area Under the Curve (AUC) (0.822) with strong discrimination for the Non-Recovery class (AUC 0.932). This work demonstrates that temporal modeling of clinical trajectories can enhance AKI recovery prediction.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"468-477"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280160","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-Post Evaluation of Documentation Burden, Time Perception, and Cognitive Workload of Ambient AI Scribe Tools. 环境人工智能抄写工具的文档负担、时间感知和认知工作量的前后评估。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Leopold Arko, Tony Huy Nguyen, Michelle Stoffel, Jaya Kumar, Sameer Badlani, Genevieve B Melton, Rebecca Markowitz

{"title":"Pre-Post Evaluation of Documentation Burden, Time Perception, and Cognitive Workload of Ambient AI Scribe Tools.","authors":"Leopold Arko, Tony Huy Nguyen, Michelle Stoffel, Jaya Kumar, Sameer Badlani, Genevieve B Melton, Rebecca Markowitz","doi":"","DOIUrl":"","url":null,"abstract":"Documentation burden and cognitive load contribute to clinician burnout, and ambient AI scribes offer a promising solution, though real-world evaluations remain limited. This study evaluated the impact of an ambient AI scribe on documentation burden, cognitive workload, and perceived burnout in clinical practice. Forty clinicians participated in a 60-day pilot during which the AI scribe generated draft notes from encounter audio. Pre- and post-implementation surveys included validated single-item burnout measure, workflow assessments, NASA Task Load Index (NASA-TLX) scores, and EHR-autologged data on after-hours documentation. Clinicians reported significant reductions in perceived documentation time and after-hours work (all p < 0.001), with large effect sizes (r = 0.73-0.80). NASA-TLX domains demonstrated significant decreases in mental demand, physical demand, effort, time pressure, and stress (all p ≤0.001; g = 0.76-1.54). In contrast, objective EHR documentation minutes did not change significantly (t (15) = 1.52, p = 0.15, g = 0.36). Perceived documentation time aligned more closely with objective EHR-derived categories following implementation. These findings suggest that ambient AI scribes meaningfully reduce perceived workload and improve calibration of documentation burden, even when measurable documentation time remains stable.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"202-209"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280165","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

Mapping the Storm: Linking Tornado Paths to Emergency Room Surges Through Geocoded Patient Data. 绘制风暴：通过地理编码的患者数据将龙卷风路径与急诊室的激增联系起来。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Katherine Pyasik, Katherine Bieber, Adam Wilcox

引用次数: 0

The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models. 强制公平对重塑机器学习模型中的解释的影响。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Joshua W Anderson, Shyam Visweswaran

{"title":"The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models.","authors":"Joshua W Anderson, Shyam Visweswaran","doi":"","DOIUrl":"","url":null,"abstract":"Trustworthy machine learning in healthcare requires strong predictive performance, fairness, and explanations. While it is known that improving fairness can affect predictive performance, little is known about how fairness improvements influence explainability, an essential ingredient for clinical trust. Clinicians may hesitate to rely on a model whose explanations shift after fairness constraints are applied. In this study, we examine how enhancing fairness through bias mitigation techniques reshapes Shapley-based feature rankings. We quantify changes in feature importance rankings after applying fairness constraints across three datasets: pediatric urinary tract infection risk, direct anticoagulant bleeding risk, and recidivism risk. We also evaluate multiple model classes on the stability of Shapley-based rankings. We find that increasing model fairness across racial subgroups can significantly alter feature importance rankings, sometimes in different ways across groups. These results highlight the need to jointly consider accuracy, fairness, and explainability in model assessment rather than in isolation.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"47-56"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280170","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

Completeness of Common Data Elements for Breast Cancer Clinical Trials in Observational Databases. 观察性数据库中乳腺癌临床试验常见数据元素的完整性。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Adit Anand, Yilu Fang, Chunhua Weng, Karthik Natarajan

{"title":"Completeness of Common Data Elements for Breast Cancer Clinical Trials in Observational Databases.","authors":"Adit Anand, Yilu Fang, Chunhua Weng, Karthik Natarajan","doi":"","DOIUrl":"","url":null,"abstract":"Clinical trials serve as a gold standard for data that shapes the healthcare landscape in oncology, but variance in clinical trial eligibility criteria presents challenges for using the data in downstream applications. Identifying common data elements (CDEs) present in cancer trials' eligibility criteria is critical to achieve standardized representations of healthcare data and thus develop trial recruitment tools that generalize to oncology. In this study, we curate a set of CDEs contained in the eligibility criteria of breast cancer clinical trials and evaluate their completeness across different observational databases represented in the Observational Medical Outcomes Partnership (OMOP) Common Data Model. We show that between databases, CDEs are captured with differing levels of completeness across OMOP domains and that there is discordance between the frequency of eligibility criteria CDEs and their completeness in observational databases, which characterizes these databases' utility for subsequent oncology trial matching efforts.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"41-46"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280147","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 Structured Review of Emerging Prompt Instability Indices (PIIs) to Evaluate the Reproducibility of Prompt-Driven Image Segmentation Models. 对新兴提示不稳定性指数（PIIs）的结构化回顾，以评估提示驱动的图像分割模型的可重复性。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Adib Shafipour, Deevakar Rogith, Zulfiia Ditto, Hien V Tran, Katie Stinson, Toufeeq Ahmed Syed

{"title":"A Structured Review of Emerging Prompt Instability Indices (PIIs) to Evaluate the Reproducibility of Prompt-Driven Image Segmentation Models.","authors":"Adib Shafipour, Deevakar Rogith, Zulfiia Ditto, Hien V Tran, Katie Stinson, Toufeeq Ahmed Syed","doi":"","DOIUrl":"","url":null,"abstract":"Prompt-aware image segmentation has introduced new flexibility by allowing models to incorporate user-provided cues, such as points, boxes, or text prompts, to guide object delineation across diverse imaging domains. This flexibility introduces a fundamental challenge: ensuring segmentation results remain reproducible when prompts are altered. Minor variations in prompts produce substantially different outputs, raising concerns about the reliability and scientific validity of research and clinical applications. This paper conducts a structured review of emerging Prompt Instability Indices (PIIs), quantitative measures developed to assess the reproducibility of prompt-driven segmentation models. A systematic search of PubMed and arXiv (2020-2025) identified relevant studies, analyzed based on the definition, and operationalized prompt stability. This preliminary study suggests a taxonomy of PII methods, highlighting their respective advantages, limitations, and interpretive value. Integrating reproducibility metrics such as PIIs alongside traditional accuracy measures is essential for establishing consistent, transparent, and trustworthy performance assessments in next-generation interactive segmentation systems.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"400-407"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280034","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

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes. ClinNoteAgents：从临床记录中预测和解释心力衰竭30天再入院的LLM多代理系统。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Rongjia Zhou, Chengzhuo Li, Carl Yang, Jiaying Lu

{"title":"ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.","authors":"Rongjia Zhou, Chengzhuo Li, Carl Yang, Jiaying Lu","doi":"","DOIUrl":"","url":null,"abstract":"Heart failure (HF) is one of the leading causes of rehospitalization among older adults in the United States. Although clinical notes contain rich, detailed patient information and make up a large portion of electronic health records (EHRs), they remain underutilized for HF readmission risk analysis. Traditional computational models for HF read-mission often rely on expert-crafted rules, medical thesauri, and ontologies to interpret clinical notes, which are typically written under time pressure and may contain misspellings, abbreviations, and domain-specific jargon. We present ClinNoteAgents, an LLM-based multi-agent framework that transforms free-text clinical notes into (1) structured representations of clinical and social risk factors for association analysis and (2) clinician-style abstractions for HF 30-day readmission prediction. We evaluate ClinNoteAgents on 3,544 notes from 2,065 patients (readmission rate=35.16%), demonstrating high extraction fidelity for clinical variables (conditional accuracy ≥90% for multiple vitals), key risk factor identification, and preservation of predictive signal despite 60-90% text reduction. By reducing reliance on structured fields and minimizing manual annotation and model training, ClinNoteAgents provides a scalable and interpretable approach to note-based HF readmission risk modeling in data-limited healthcare systems.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"594-603"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280070","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

Estimation of Left Ventricular Systolic Function in Pediatric and Congenital Heart Disease from Serial Electrocardiograms. 从连续心电图估计小儿和先天性心脏病左心室收缩功能。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Platon Lukyanenko, Sunil J Ghelani, John K Triedman, Joshua Mayourian, William G La Cava

{"title":"Estimation of Left Ventricular Systolic Function in Pediatric and Congenital Heart Disease from Serial Electrocardiograms.","authors":"Platon Lukyanenko, Sunil J Ghelani, John K Triedman, Joshua Mayourian, William G La Cava","doi":"","DOIUrl":"","url":null,"abstract":"Estimating left ventricular ejection fraction (LVEF) from electrocardiograms (ECGs) is a useful task enabled by artificial intelligence (AI-ECG). Prior work focuses on predicting LVEF from single ECGs. Here, we investigate whether a patient's history of ECGs can improve LVEF prediction. We study a longitudinal cohort from Boston Children's Hospital, deriving LVEF from echocardiograms conducted within 2 days of ECGs (n=178,495, n. patients: 70,226, median age: 10.6). We propose a sequential AI-ECG approach using convolutional layers to represent single ECGs and a sequential neural network to reason over longitudinal ECGs. We build and test several sequential architectures. For predictions with at least 5 previous ECGs, sequential AI-ECG improved median AUROC (IQR) by 3.4 points (1.4, 4.4). When predicting the LVEF value, sequential AI-ECG improves Pearson R by 0.08 (0.02, 0.13). Results suggest that patients' longitudinal ECG history contains valuable information for improving AI-ECG risk stratification beyond current snapshot-based models.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"293-302"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280161","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

Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence. 基于多模态深度学习的Burkitt淋巴瘤复发预测模型。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Avery C Maytin, Jessica A Patricoski-Chavez, Ari Pelcovits, Sanjay Mishra, Adam Olszewski, Jeremy L Warner, Ece D Gamsiz Uzun

{"title":"Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.","authors":"Avery C Maytin, Jessica A Patricoski-Chavez, Ari Pelcovits, Sanjay Mishra, Adam Olszewski, Jeremy L Warner, Ece D Gamsiz Uzun","doi":"","DOIUrl":"","url":null,"abstract":"Burkitt Lymphoma (BL) is an aggressive B-cell non-Hodgkin lymphoma. Recurrent BL has extremely poor prognosis, and does not have a standard treatment option. Although the disease pathology is well characterized, there is currently a lack of research specifically on predictive modeling of BL recurrence. We developed a deep learning (DL) model, BLIMP (Burkitt Lymphoma multI-Modal recurrence Predictor) to predict BL recurrence by combining clinical, gene expression, and mutation data, utilizing a cohort of 184 patients from a publicly available dataset. Our approach achieved an AUC of 0.788 on a held-out testing set, outperforming traditional machine learning (ML) models. Explainability analysis was performed, and features which contributed to BLIMP's predictions are consistent with known patterns of BL pathophysiology and cancer progression. These results demonstrated the effectiveness of using genomic data in DL-based models for BL recurrence prediction, suggesting promise for future applications to BL research.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"303-312"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280192","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

Advances in LLM Reasoning Enable Flexibility in Clinical Problem-Solving. 法学硕士推理的进步使临床问题解决的灵活性。

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2026-06-01 eCollection Date: 2026-01-01

Kie Shidara, Preethi Prem, Jonathan Kim, Anna Podlasek, Feng Liu, Ahmed Alaa, Danilo Bernardo

{"title":"Advances in LLM Reasoning Enable Flexibility in Clinical Problem-Solving.","authors":"Kie Shidara, Preethi Prem, Jonathan Kim, Anna Podlasek, Feng Liu, Ahmed Alaa, Danilo Bernardo","doi":"","DOIUrl":"","url":null,"abstract":"Large Language Models (LLMs) have achieved high accuracy on medical question-answer (QA) benchmarks, yet their capacity for flexible clinical reasoning has been debated. Here, we asked whether advances in reasoning LLMs improve their cognitive flexibility in clinical reasoning. We assessed reasoning models from the OpenAI, Grok, Gemini, Claude, and DeepSeek families on the medicine abstraction and reasoning corpus (mARC), an adversarial medical QA benchmark which utilizes the Einstellung effect to induce inflexible overreliance on learned heuristic patterns in contexts where they become suboptimal. We found that strong reasoning models avoided Einstellung-based traps more often than weaker reasoning models, achieving human-level performance on mARC: physician average accuracy was 66% [95% CI: 56-76%] and the top performing model was Claude with 75% accuracy [95% CI: 74-76%]. On questions most commonly missed by physicians, the top 5 performing models answered 50% to 70% correctly with high confidence, indicating that these models may be less susceptible than humans to Einstellung effects. Our results indicate that strong reasoning models demonstrate improved flexibility in medical reasoning, achieving performance on par with humans on mARC.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2026 ","pages":"428-437"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280059","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