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Use of deep learning model for paediatric elbow radiograph binomial classification: initial experience, performance and lessons learnt.

Singapore medical journal
INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency dep...

Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case-control study.

BMC medicine
BACKGROUND: Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. In present study, we aimed to identify a novel optimized autoantibody panel with high di...

Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data.

BMC pediatrics
BACKGROUND: Healthcare practitioners require a robust predictive system to accurately diagnose diseases, especially in young children with conditions such as anemia. Delays in diagnosis and treatment can have severe consequences, potentially leading ...

Enhancing medical text classification with GAN-based data augmentation and multi-task learning in BERT.

Scientific reports
With the rapid advancement of medical informatics, the accumulation of electronic medical records and clinical diagnostic data provides unprecedented opportunities for intelligent medical text classification. However, challenges such as class imbalan...

The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.

Scientific reports
Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer's disease. We previously develop...

[Machine learning models established to distinguish OA and RA based on immune factors in the knee joint fluid].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology
Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Method...

A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.

BMC infectious diseases
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early identification of patients with sepsis at high risk of developing ARDS is crucial for timely interven...

Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes.

Journal of medical systems
Arrhythmias are common and can affect individuals with or without structural heart disease. Deep learning models (DLMs) have shown the ability to recognize arrhythmias using 12-lead electrocardiograms (ECGs). However, the limited types of arrhythmias...

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Scientific reports
Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a significant global public health threat. HIV co-infection significantly increases the risk of active TB recurrence and prolongs medical treatment for tuberc...

X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head.

Scientific reports
This study aimed to develop an X-ray radiomics model for predicting collapse of early-stage osteonecrosis of the femoral head (ONFH). A total of 87 patients (111 hips; training set: n = 67, test set: n = 44) with non-traumatic ONFH at Association Res...