Integration of intraoperative data in interpretable machine learning models to predict postoperative AKI in noncardiac surgery patients [0.03%]
基于可解释机器学习模型的术中数据整合在非心脏手术患者术后急性肾损伤预测中的应用
Justin Do,Karan H Shah,Melissa Chen Xu et al.
Justin Do et al.
Objectives: We aimed to (1) quantify changes in discrimination when adding intraoperative data to preoperative data and (2) compare tabular machine learning with feature engineering against a time-aware LSTM-based model. ...
Explainable AI for mental health emergency returns: integrating large language models with predictive modeling [0.03%]
可解释的人工智能在心理健康紧急情况复发中的应用：大型语言模型与预测建模的融合
Abdulaziz Ahmed,Mohammad Saleem,Mohammed Alzeen et al.
Abdulaziz Ahmed et al.
Objective: Emergency department (ED) returns for mental health (MH) conditions pose a substantial burden on healthcare systems. Traditional machine learning (ML) models have shown promise in predicting ED returns but ofte...
Data-derived Identity Verification as a principle for the dissemination, harmonization, and artificial intelligence reuse of sensitive biomedical data [0.03%]
基于数据的身份验证作为传播、协调和人工智能重新使用敏感生物医学数据的原则
Benjamin A Cordier,Erik S Benton,Eamon J Dysinger et al.
Benjamin A Cordier et al.
Objectives: To facilitate responsible biomedical data sharing, large-scale data harmonization, and cutting-edge artificial intelligence (AI) applications by augmenting controlled-access data dissemination models (DDMs) wi...
Large language model chatbot-based text-to-SQL application for database analyses in liver diseases and hepatology research [0.03%]
基于大型语言模型聊天机器人的文本到SQL应用程序，用于肝病和肝脏研究中的数据库分析
Aryana T Far,Steve Sun,Gabrielle Jutras et al.
Aryana T Far et al.
Objective: Large language models (LLMs) may improve analysis of structured clinical data by enabling natural language (NL) queries. We developed an interactive, PHI-compliant "Text2SQL" chatbot as a proof-of-concept for h...
Early experience with a patient-facing AI chatbot integrated in a patient portal [0.03%]
面向患者的AI聊天机器人整合入患者门户网站的初期体验报告
Ming Tai-Seale,Marlene Millen,Florin Vaida et al.
Ming Tai-Seale et al.
Background and significance: Many patients struggle to interpret lab and test results. Objective: To evaluate patient experience, usabi...
Evaluating the feasibility of the CIPHER metadata framework towards building a conceptual phenotype standard [0.03%]
评估CIPHER元数据框架构建概念性表型标准的可行性
Jacqueline Honerlaw,Yuk-Lam Ho,Michael Murray et al.
Jacqueline Honerlaw et al.
Objective: Phenotype libraries accelerate health research through the reuse of algorithms. However, multiple libraries exist with differing metadata standards. We describe the integration of libraries using shared concept...
Enhancing the quality and trustworthiness of large language model-generated summaries of clinical oncology literature [0.03%]
提高大型语言模型生成的临床肿瘤学文献摘要的质量和可信度
Arnulf Stenzl,Eamonn Rogers,Sophia Ananiadou et al.
Arnulf Stenzl et al.
Objectives: This study evaluated the quality and trustworthiness of large language model (LLM)-generated scientific and plain language summaries (PLS) from clinical oncology literature, focusing on faithfulness (absence o...
Validation of pregnancy date estimation from outpatient electronic health record data [0.03%]
基于门诊电子健康记录的孕龄估算准确性验证研究
Kristin Lyon-Scott,Jenine Dankovchik,Jenna Donovan et al.
Kristin Lyon-Scott et al.
Objectives: Electronic health record (EHR) data can enrich investigation of maternal health and pregnancy in large populations. However, many outpatient care settings lack data infrastructure to record pregnancy start and...
Benchmarking reliability and calibration of LLMs for multi-cancer early detection test communication [0.03%]
用于多癌早期检测测试沟通的LLM可靠性与校准基准测试
Koki Takabatake,Maria Sol Rosito,Danielle Braun et al.
Koki Takabatake et al.
Objectives: To assess the reliability and confidence calibration of Large Language Models (LLMs) when communicating multi-cancer early detection (MCED) test characteristics in an expert-informed clinical evaluation. ...
irAE.AI: AI-powered exploration of real-world immune-related adverse events [0.03%]
基于人工智能的免疫相关不良事件探索系统
Gabriela Fort,David Stone,Ching-Nung Lin et al.
Gabriela Fort et al.
Objectives: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but their clinical benefit is limited by the onset of immune-related adverse events (irAEs). Real-world pharmacovigilance data, such as...