Med-MGF: multi-level graph-based framework for handling medical data imbalance and representation [0.03%]
基于多级图的医疗数据处理框架用于解决医学数据不平衡及表示问题的方法研究
Tuong Minh Nguyen,Kim Leng Poh,Shu-Ling Chong et al.
Tuong Minh Nguyen et al.
Background: Modeling patient data, particularly electronic health records (EHR), is one of the major focuses of machine learning studies in healthcare, as these records provide clinicians with valuable information that ca...
Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective [0.03%]
基于医疗保健视角的模糊AHP-TOPSIS数据统计方法选择研究
Abdullah Alharbi,Wael Alosaimi,Hashem Alyami et al.
Abdullah Alharbi et al.
The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed wit...
Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care [0.03%]
关于急性护理中病情恶化患者的临床预测模型的设计的医师观点和建议
Robin Blythe,Sundresan Naicker,Nicole White et al.
Robin Blythe et al.
Background: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discri...
Special supplement issue on quality assurance and enrichment of biological and biomedical ontologies and terminologies [0.03%]
关于生物和生物医学本体和术语的质控与增强的特刊
Licong Cui,Ankur Agrawal
Licong Cui
Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance an...
From admission to discharge: a systematic review of clinical natural language processing along the patient journey [0.03%]
从入院到出院：患者就诊途经的临床自然语言处理系统综述
Katrin Klug,Katharina Beckh,Dario Antweiler et al.
Katrin Klug et al.
Background: Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful tr...
How successful is the CatBoost classifier in diagnosing different dental anomalies in patients via sella turcica and vertebral morphologic alteration? [0.03%]
CatBoost分类器通过蝶椎基底点和椎骨形态变化诊断患者不同牙颌畸形的效果如何？
Merve Gonca,Busra Beser Gul,Mehmet Fatih Sert
Merve Gonca
Background: To investigate how successfully the classification of patients with and without dental anomalies was achieved through four experiments involving different dental anomalies. ...
A deep convolutional neural network approach using medical image classification [0.03%]
一种基于医疗图像分类的深度卷积神经网络方法
Mohammad Mousavi,Soodeh Hosseini
Mohammad Mousavi
The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as well as protecting the uninfec...
Development and validation of a machine learning-based model to assess probability of systemic inflammatory response syndrome in patients with severe multiple traumas [0.03%]
基于机器学习的评估重度多发伤患者全身炎症反应综合征概率模型的建立与验证
Alexander Prokazyuk,Aidos Tlemissov,Marat Zhanaspayev et al.
Alexander Prokazyuk et al.
Background: Systemic inflammatory response syndrome (SIRS) is a predictor of serious infectious complications, organ failure, and death in patients with severe polytrauma and is one of the reasons for delaying early total...
Observational Study
BMC medical informatics and decision making. 2024 Aug 27;24(1):235. DOI:10.1186/s12911-024-02640-x 2024
Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis [0.03%]
基于贝叶斯优化与深度学习模型的蛋白质序列分类优化方法研究
Umesh Kumar Lilhore,Sarita Simiaya,Musaed Alhussein et al.
Umesh Kumar Lilhore et al.
Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-B...
Predicting high blood pressure using machine learning models in low- and middle-income countries [0.03%]
利用机器学习模型预测中低收入国家的高血压患病率
Ekaba Bisong,Noor Jibril,Preethi Premnath et al.
Ekaba Bisong et al.
Responding to the rising global prevalence of noncommunicable diseases (NCDs) requires improvements in the management of high blood pressure. Therefore, this study aims to develop an explainable machine learning model for predicting high bl...