Scalable extraction of social determinants of health from clinical notes in a sepsis cohort using instruction-tuned language models [0.03%]
利用指令微调的语言模型从脓毒症队列的临床记录中可扩展地提取健康社会决定因素
Diego Salazar,Pankaj Dipankar,Daniel Smolyak et al.
Diego Salazar et al.
Objectives: Social determinants of health (SDOH) are incompletely captured in structured electronic health records (EHRs) but are frequently documented in unstructured clinical notes. We evaluated large language models (L...
A framework for assessing algorithmic discrimination risks in training data: a case of pediatric type 1 diabetes [0.03%]
一种评估训练数据中的算法歧视风险的框架：以儿童1型糖尿病为例
Ioannis Bilionis,Ricardo C Berrios,Antonio de Arriba Muñoz et al.
Ioannis Bilionis et al.
Objectives: To develop a generalizable framework for identifying algorithmic discrimination risks arising from subgroup imbalances in machine learning training data, with relevance to medical informatics applications wher...
Design and implementation of PharMe: a mobile application to return pharmacogenomic test results to patients [0.03%]
PharMe的设计与实施：一款用于向患者反馈药物基因组检测结果的手机应用程序
Tamara Slosarek,Jannis Baum,Benjamin Frost et al.
Tamara Slosarek et al.
Objective: Pharmacogenomics can optimize therapeutic outcomes while minimizing adverse events. However, the lack of trained healthcare professionals has impeded its widespread adoption. Current patient-facing applications...
Assessment of real-world evidence research competencies in federated research networks: a maternal health fellowship program evaluation [0.03%]
评估联合研究网络中的真实世界证据研究能力：一项针对母胎医学专科医师培训项目的评价
Haeun Lee,Benjamin Martin,Andreea A Creanga et al.
Haeun Lee et al.
Background and significance: Generating real-world evidence (RWE) approaches across federated evidence networks based on real-world data requires learning new methodological skills. However, standardized frameworks for de...
Develop and validate a fair machine learning model to identify patients with high data-continuity in electronic health records data [0.03%]
开发并验证一个公平的机器学习模型，以识别电子健康记录数据中具有高数据连续性的患者
Yao An Lee,Tiange Tang,Yu Huang et al.
Yao An Lee et al.
Objectives: Electronic health record (EHR) data discontinuity, defined as receiving care outside of a particular EHR system, may cause misclassification of study variables. We aimed to: (1) quantify misclassification acro...
Using a translational data platform to create clinical-grade genome-informed risk assessments [0.03%]
利用转化数据平台创建临床级基因组风险评估
Jennifer L Morse,Megan He,Hana Bangash et al.
Jennifer L Morse et al.
Objective: To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materi...
Real-time prediction of atrial fibrillation in intensive care unit: a meta-learning approach [0.03%]
重症监护病房中实时预测房颤：一种元学习方法
Mehran Moazeni,Sebastian Mildiner Moraga,Maaike Wösten et al.
Mehran Moazeni et al.
Objective: Atrial fibrillation (AF) is a common arrhythmia affecting a large fraction of patients in intensive care units (ICUs). Predicting patients at risk of AF in the ICU is a challenging task and is not commonly prac...
Development of a benchmarking dataset for symptom detection using large language models [0.03%]
用于症状检测的大语言模型基准测试数据集的开发
Joshua Davis,Brigitte N Durieux,Chloe Van Dongen et al.
Joshua Davis et al.
Objectives: To develop a pipeline for evaluating large language models (LLMs) on the task of capturing symptoms from clinical encounters. Materials and me...
Mapping oral-systemic health relationships: a data-driven analysis of co-occurrence patterns in biomedical literature [0.03%]
基于生物医学文献中相关模式的口腔全身健康关系映射数据驱动分析
Bhumi Patel,Janusz Wojtusiak
Bhumi Patel
Objective: To systematically map co-occurrence patterns between systemic medical conditions and oral diseases in the biomedical literature. Materials and ...
Build fair machine learning models to predict adverse outcomes for heart failure patients with preserved ejection fraction and with reduced ejection fraction [0.03%]
构建公平的机器学习模型，以预测射血分数保留型和降低型心力衰竭患者的不良预后
YaYun Yeh,YaoAn Lee,Yu Huang et al.
YaYun Yeh et al.
Objectives: Develop and validate subtype-specific, fairness-aware machine learning (ML) models that integrate clinical and social determinants of health (SDoH) information to predict 6-month readmission or mortality after...