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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.

Scientific reports
Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machin...

Engineering approaches for characterizing soft tissue mechanical properties: A review.

Clinical biomechanics (Bristol, Avon)
From cancer diagnosis to detailed characterization of arterial wall biomechanics, the elastic property of tissues is widely studied as an early sign of disease onset. The fibrous structural features of tissues are a direct measure of its health and f...

The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.

Journal of medical systems
According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients' health. However, ...

Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection ...

Application of artificial neural network model in diagnosis of Alzheimer's disease.

BMC neurology
BACKGROUND: Alzheimer's disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explo...

Justifying diagnosis decisions by deep neural networks.

Journal of biomedical informatics
An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition...

Scale-space approximated convolutional neural networks for retinal vessel segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation...

Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: A blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purp...

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

Journal of biomedical informatics
BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations...

Neural networks versus Logistic regression for 30 days all-cause readmission prediction.

Scientific reports
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the health...