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Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality.
JAMA network open
Nov 2, 2020
IMPORTANCE: Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use.
Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.
Scientific reports
Nov 2, 2020
Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, and it provides insights into the underlying vascular pathology (CAA vs. hypertensive angiopathy) to ...
m5CPred-SVM: a novel method for predicting m5C sites of RNA.
BMC bioinformatics
Oct 30, 2020
BACKGROUND: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-meth...
A deep learning approach for sepsis monitoring via severity score estimation.
Computer methods and programs in biomedicine
Oct 28, 2020
BACKGROUND AND OBJECTIVE: Sepsis occurs in response to an infection in the body and can progress to a fatal stage. Detection and monitoring of sepsis require multi-step analysis, which is time-consuming, costly and requires medically trained personne...
Style transfer strategy for developing a generalizable deep learning application in digital pathology.
Computer methods and programs in biomedicine
Oct 25, 2020
BACKGROUND AND OBJECTIVES: Despite recent advances in artificial intelligence for medical images, the development of a robust deep learning model for identifying malignancy on pathology slides has been limited by problems related to substantial inter...
Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors.
Korean journal of radiology
Oct 21, 2020
OBJECTIVE: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on d...
Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.
Frontiers in endocrinology
Oct 21, 2020
BACKGROUND: Central lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. Th...
Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.
Scientific reports
Oct 16, 2020
We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinom...
Head and Neck Neoplasms
Regression Analysis
Area Under Curve
Papillomaviridae
Oropharyngeal Neoplasms
Multiparametric Magnetic Resonance Imaging
ROC Curve
Computational Biology
Papillomavirus Infections
Squamous Cell Carcinoma of Head and Neck
Adult
Male
Middle Aged
Humans
Machine Learning
Retrospective Studies
Female
Aged
Aged, 80 and over
Differential diagnosis for esophageal protruded lesions using a deep convolution neural network in endoscopic images.
Gastrointestinal endoscopy
Oct 13, 2020
BACKGROUND AND AIMS: Recent advances in deep convolutional neural networks (CNNs) have led to remarkable results in digestive endoscopy. In this study, we aimed to develop CNN-based models for the differential diagnosis of benign esophageal protruded...
Early Feasibility of Automated Artificial Intelligence Angiography Based Fractional Flow Reserve Estimation.
The American journal of cardiology
Oct 13, 2020
Despite the evidence of improved patients' outcome, fractional flow reserve (FFR) is underused in current everyday practice. We aimed to evaluate the feasibility of a novel automated artificial intelligence angiography-based FFR software (AutocathFFR...
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