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DRG grouping by machine learning: from expert-oriented to data-based method.

BMC medical informatics and decision making
BACKGROUND: Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented ...

Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images.

Computational and mathematical methods in medicine
Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase cha...

Diagnosis of Schizophrenia Based on Deep Learning Using fMRI.

Computational and mathematical methods in medicine
Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification...

Association of Gastroesophageal Reflux Disease with Preterm Birth: Machine Learning Analysis.

Journal of Korean medical science
BACKGROUND: This study used machine learning and population data for testing the associations of preterm birth with gastroesophageal reflux disease (GERD) and periodontitis.

General deep learning model for detecting diabetic retinopathy.

BMC bioinformatics
BACKGROUND: Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventio...

Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm.

Computational and mathematical methods in medicine
A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammog...

Coherent false seizure prediction in epilepsy, coincidence or providence?

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to...

Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data.

Sensors (Basel, Switzerland)
A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements a...

A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection.

Computational and mathematical methods in medicine
Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical...

Single Shot Multibox Detector Automatic Polyp Detection Network Based on Gastrointestinal Endoscopic Images.

Computational and mathematical methods in medicine
PURPOSE: In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single S...