Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.amjmed.2016.06.034 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.
BMC Med Inform Decis Mak
January 2025
Department of Pediatrics, School of Medicine, Ekbatan Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method.
View Article and Find Full Text PDFQuantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.
BMC Cancer
January 2025
Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, P. R. China.
Background: This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).
Methods: In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method.
Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China.
BMC Public Health
January 2025
Department of Infectious Diseases, Nanning Center for Disease Control and Prevention, Nanning, 530023, China.
Introduction: COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.
View Article and Find Full Text PDFConsistency between headache diagnoses and ICHD-3 criteria across different levels of care.
J Headache Pain
January 2025
Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Background: Diagnosing headache disorders poses significant challenges, particularly in primary and secondary levels of care (PSLC), potentially leading to misdiagnosis and underdiagnosis. This study evaluates diagnostic agreement for migraine, tension-type headache (TTH), and cluster headache (CH) between PSLC and tertiary care (TLC) and assesses adherence to the International Classification of Headache Disorders 3rd edition (ICHD-3) guidelines.
Methods: A retrospective, cross-sectional analysis was conducted at Charité - Universitätsmedizin Berlin's tertiary headache center.
A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.
J Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!