Medical Expert Knowledge Meets AI to Enhance Symptom Checker Performance for Rare Disease Identification in Fabry Disease: Mixed Methods Study [0.03%]
基于法布雷病的罕见疾病识别：医学专家知识与人工智能结合以增强症状检查器性能的混合研究方法研究
Anne Pankow,Nico Meißner-Bendzko,Jessica Kaufeld et al.
Anne Pankow et al.
Background: Rare diseases, which affect millions of people worldwide, pose a major challenge, as it often takes years before an accurate diagnosis can be made. This delay results in substantial burdens for patients and he...
Identification and Categorization of the Top 100 Articles and the Future of Large Language Models: Thematic Analysis Using Bibliometric Analysis [0.03%]
基于 bibliometrics 的主题分析：顶级论文识别分类与大模型未来展望
Ethan Bernstein,Anya Ramsamooj,Kelsey L Millar et al.
Ethan Bernstein et al.
Background: Since the release of ChatGPT and other large language models (LLMs), there has been a significant increase in academic publications exploring their capabilities and implications across various fields, such as ...
Predicting Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial Intelligence: Derivation Study [0.03%]
基于常规生理参数和人工智能预测重症监护病房的低觉醒状态发作：衍生型研究
Raphaëlle Giguère,Victor Niaussat,Monia Noël-Hunter et al.
Raphaëlle Giguère et al.
Background: Delirium is prevalent in intensive care units (ICUs), often leading to adverse outcomes. Hypoactive delirium is particularly difficult to detect. Despite the development of new tools, the timely identification...
Performance of DeepSeek and GPT Models on Pediatric Board Preparation Questions: Comparative Evaluation [0.03%]
DeepSeek和GPT模型在儿科备考题上的表现：比较评估
Masab Mansoor,Andrew Ibrahim,Ali Hamide
Masab Mansoor
Background: Limited research exists evaluating artificial intelligence (AI) performance on standardized pediatric assessments. This study evaluated 3 leading AI models on pediatric board preparation questions. ...
Intensive Care Unit Patient Outcome Prediction Using ν-Support Vector Classification and Stochastic Signal Processing-Based Feature Extraction Techniques: Algorithm Development and Validation Study [0.03%]
基于ν支持向量分类和随机信号处理特征提取技术的重症监护病房患者预后预测算法的研发与验证研究
Shaodong Wang,Yiqun Jiang,Qing Li et al.
Shaodong Wang et al.
Background: Intensive care units (ICUs) treat patients with life-threatening illnesses. Worldwide, intensive care demand is massive. Predicting patient outcomes in ICUs holds significant importance for health care operati...
Domain-Specific Pretraining of NorDeClin-Bidirectional Encoder Representations From Transformers for International Statistical Classification of Diseases, Tenth Revision, Code Prediction in Norwegian Clinical Texts: Model Development and Evaluation Study [0.03%]
基于领域的NorDeClin-BERT在挪威临床文本中ICD-10编码预测中的应用：模型开发与评估研究
Phuong Dinh Ngo,Miguel Ángel Tejedor Hernández,Taridzo Chomutare et al.
Phuong Dinh Ngo et al.
Background: Accurately assigning ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes is critical for clinical documentation, reimbursement processes, epidemiological studies, and health car...
Heterogeneity in Effects of Automated Results Feedback After Online Depression Screening: Secondary Machine-Learning Based Analysis of the DISCOVER Trial [0.03%]
基于机器学习的DISCOVER试验二次分析：在线抑郁筛查后自动化结果反馈作用的异质性
Matthias Klee,Byron C Jaeger,Franziska Sikorski et al.
Matthias Klee et al.
Background: Online depression screening tools may increase uptake of evidence-based care and consequently lead to symptom reduction. However, results of the DISCOVER trial suggested no effect of automated results feedback...
Effectiveness of the GPT-4o Model in Interpreting Electrocardiogram Images for Cardiac Diagnostics: Diagnostic Accuracy Study [0.03%]
GPT-4o模型在心电图图像心脏诊断解读中的有效性：诊断准确性研究
Haya Engelstein,Roni Ramon-Gonen,Avi Sabbag et al.
Haya Engelstein et al.
Background: Recent progress has demonstrated the potential of deep learning models in analyzing electrocardiogram (ECG) pathologies. However, this method is intricate, expensive to develop, and designed for specific purpo...
Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics [0.03%]
使用个人基础模型的AI个性化可导致更精确的数字治疗学应用于心理健康领域
Peter Washington
Peter Washington
Digital health interventions often use machine learning (ML) models to make predictions of repeated adverse health events. For example, models may be used to analyze patient data to identify patterns that can anticipate the likelihood of di...
A Real-Time Signal-Based Wavelet Long Short-Term Memory Method for Length-of-Stay Prediction for the Intensive Care Unit: Development and Evaluation Study [0.03%]
基于实时信号的长时记忆小波神经网络用于重症监护病房住院时间预测：发展与评定研究
Yiqun Jiang,Qing Li,Wenli Zhang
Yiqun Jiang
Background: Efficient allocation of health care resources is essential for long-term hospital operation. Effective intensive care unit (ICU) management is essential for alleviating the financial strain on health care syst...