Deep learning-based in-ambulance speech recognition and generation of prehospital emergency diagnostic summaries using LLMs [0.03%]
基于深度学习的救护车内的语音识别和使用大语言模型生成院前急救诊断摘要
Chen Chen,Yingying Hu,Wenwei Cai et al.
Chen Chen et al.
Objective: The timely and accurate submission of prehospital electronic medical records is crucial for the efficiency of medical rescue operations. However, personnel professional experience, training cycles, and environm...
Development and validation of a machine learning model for early screening of high-risk mild cognitive impairment from the multi-cohort data [0.03%]
基于多队列数据的轻度认知障碍高危患者早期筛查机器学习模型的开发与验证
Xuan Wu,Xuecheng Yao,Jianing Shi et al.
Xuan Wu et al.
Background: Early screening of mild cognitive impairment (MCI) in older populations is crucial for timely intervention. MCI often precedes dementia, but current diagnostic tools are time-consuming and not widely accessibl...
Development and validation of a machine learning-based clinical prediction model for monitoring liver injury in patients with pan-cancer receiving immunotherapy [0.03%]
一种基于机器学习的临床预测模型在监测泛癌种免疫治疗患者肝损伤中的建立与验证
Yi Wang,Jing Lei,Zhiping Jin et al.
Yi Wang et al.
Background: Immune checkpoint inhibitor (ICI)-related liver injury poses a considerable clinical challenge for cancer patients. This study aimed to develop and validate an interpretable predictive model employing machine ...
Developing a multi-label learning model to predict major adverse cardiovascular events in patients with unstable angina pectoris: A prospective cohort study [0.03%]
开发多标签学习模型预测不稳定性心绞痛患者主要不良心血管事件的前瞻性队列研究
Jing Li,Hong Yang,Yu Zhang et al.
Jing Li et al.
Background: Major adverse cardiovascular events (MACE) represent critical endpoints in cardiovascular research. The occurrence of MACE in patients with unstable angina pectoris (UAP) exhibits multidimensional complexity. ...
A web-based tool utilizing machine learning algorithms for predicting illicit drug use in emergency departments [0.03%]
基于网络的工具：利用机器学习算法预测急诊室里的非法药物使用
Tsung-Chien Lu,Chih-Chuan Lin,Te-I Weng et al.
Tsung-Chien Lu et al.
Background: Identifying illicit drug use through urine testing is time-consuming in the era of new psychoactive substances. This study aimed to develop a machine learning (ML) prediction model for early identification of ...
Comparing ChatGPT and physicians' answers to endometriosis questions on Reddit: A blind expert evaluation [0.03%]
一项盲法专家评估：比较ChatGPT和医生对Reddit上子宫内膜异位症问题的回答
Clémence Beaulieu,Aubert Agostini,Patrice Crochet et al.
Clémence Beaulieu et al.
Objectives: To compare the perceived quality, safety, and relevance of ChatGPT responses to those provided by verified physicians on Reddit, a large online discussion platform, in response to questions related to endometr...
Comparing the accuracy of large language models and prompt engineering in diagnosing realworld cases [0.03%]
比较大型语言模型和提示工程在诊断实际案例中的准确性
Guanhong Yao,WuJi Zhang,Yingxi Zhu et al.
Guanhong Yao et al.
Importance: Large language models (LLMs) hold potential in clinical decision-making, especially for complex and rare disease diagnoses. However, real-world applications require further evaluation for accuracy and utility....
Associations between climate-related physical risks and lung disease among older adults in China: A cross-sectional study [0.03%]
中国老年人气候变化相关物理风险与肺部疾病之间的关联：一项横断面研究
Shasha Jiang,Xiaoqiu Yang,Yufang Li et al.
Shasha Jiang et al.
Background: Climate extremes may adversely affect respiratory health, especially in older adults, but evidence from low- and middle-income countries is limited. ...
Enhancing AI for citation screening in literature reviews: Improving accuracy with ensemble models [0.03%]
利用集成模型提高引文筛查中AI的准确性的文献综述研究
Zhihong Zhang,Mohamad Javad Momeni Nezhad,Pallavi Gupta et al.
Zhihong Zhang et al.
Background: Healthcare literature reviews underpin evidence-based practice and clinical guideline development, with citation screening as a critical yet time-consuming step. This study evaluates the effectiveness of indiv...
From text to data: Open-source large language models in extracting cancer related medical attributes from German pathology reports [0.03%]
从文本到数据：开源大型语言模型在从德国病理报告中提取癌症相关医学属性方面的应用
Stefan Bartels,Jasmin Carus
Stefan Bartels
Structured oncological documentation is vital for data-driven cancer care, yet extracting clinical features from unstructured pathology reports remains challenging-especially in German healthcare, where strict data protection rules require ...