Topic modeling and sentiment analysis of Greek clinician-patient conversations in hematologic malignancies [0.03%]
希腊血癌临床医生与患者的对话 topic modeling 和情感分析
Maria Evangelia Chatzimina,Helen A Papadaki,Charalampos Pontikoglou et al.
Maria Evangelia Chatzimina et al.
Background: Conversations in clinical settings, especially those involving hematologic cancers or palliative care are not only informational but also emotionally charged. Understanding how these conversations are structur...
A multi-task deep sequential neural network for IgA nephropathy Oxford classification and prognosis prediction [0.03%]
用于IgA肾病Oxford分型与预后预测的多任务深度序列神经网络
Sai Pan,Yibing Fu,Lai Jiang et al.
Sai Pan et al.
Background: While deep learning has advanced pathological analysis in IgA nephropathy (IgAN), the lack of integrated models that combine multi-label structural identification, Oxford classification, and prognosis predicti...
Regression discontinuity in Time: Evaluating the impact of evolving digital health interventions [0.03%]
时间上的回归断点：评估不断变化的数字健康干预措施的影响
Isha Thapa,Pierre-Amaury Laforcade,Franziska K Bishop et al.
Isha Thapa et al.
Background: Clinics continue to adopt digital health interventions (DHIs) in which algorithms analyze data to help direct patient care. Changes to these algorithms are rarely evaluated rigorously, despite their potential ...
Leveraging open-source large language models (LLMs) in scoping reviews: a case study on disability and AI applications [0.03%]
开源大型语言模型在范围审查中的应用研究：以残疾与人工智能应用为例
Azadeh Bayani,Leandre Parfait Epoh Ewane,Davllyn Santos Oliveira Dos Anjos et al.
Azadeh Bayani et al.
Background: Large language models (LLMs) have the potential to offer solutions for automating many of the manual tasks involved in scientific reviews, including data extraction, literature screening, summarization, and qu...
Predicting nonsurgical treatment outcomes in lumbar disc herniation: leveraging sparse electronic health records for patient phenotyping [0.03%]
基于稀疏电子健康记录的腰椎间盘突出症非手术治疗结局预测及患者分型研究
Ye-Seul Lee,Yoon Jae Lee,In-Hyuk Ha
Ye-Seul Lee
Objective: Electronic health records (EHRs) offer a wealth of patient data but often fail to capture the dynamic progression of symptoms, particularly in non-continuously monitored conditions like lumbar disc herniation (...
Comparative study of LOINC and SNOMED CT in panel mapping: enhancing interoperability in laboratory testing [0.03%]
LOINC和SNOMED CT在检测组合映射中的比较研究：提高实验室检测的互操作性
Hyejin Ryu,Sumi Sung,Kuenyoul Park et al.
Hyejin Ryu et al.
Background: We aimed to evaluate and compare the applicability of Logical Observation Identifiers Names and Codes (LOINC) and SNOMED CT in mapping frequently requested panel tests. ...
Natural language processing in medical text processing: A scoping literature review [0.03%]
医学文本处理中的自然语言处理：系统综述文献回顾研究
Luis B Elvas,Ana Almeida,João C Ferreira
Luis B Elvas
Background: The exponential growth of digitized medical data has created significant challenges for healthcare professionals, as medical documentation transitions from simple text records to complex, multi-dimensional dat...
Bridging the digital divide: artificial intelligence as a catalyst for health equity in primary care settings [0.03%]
弥合数字鸿沟：人工智能在初级保健环境中促进健康平等的作用
Ayokunle Osonuga,Adewoyin A Osonuga,Sandra Chinaza Fidelis et al.
Ayokunle Osonuga et al.
Background: Health inequalities remain one of the most pressing challenges in contemporary healthcare, with primary care serving as both a gateway to services and a potential source of disparities. Artificial intelligence...
Towards practical federated learning and evaluation for medical prediction models [0.03%]
面向医疗预测模型的实用化联合学习与评估方法研究
Andrei Kazlouski,Ileana Montoya Perez,Faiza Noor et al.
Andrei Kazlouski et al.
Background: Federated learning (FL) is a rapidly advancing technique that enables collaborative model training while preserving data privacy. This approach is particularly relevant in healthcare, where privacy concerns an...
LLM-powered breast cancer staging from PET/CT reports: a comparative performance study [0.03%]
基于PET/CT报告的LLM乳腺癌分期：一项比较性能研究
Daniel Spitzl,Markus Mergen,Rickmer Braren et al.
Daniel Spitzl et al.
Purpose: Imaging reports are crucial in breast cancer management, with the tumor-node-metastasis (TNM) classification serving as a widely used model for assessing disease severity, guiding treatment decisions, and predict...