Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Mar 11, 2021
Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic r...
OBJECTIVES: To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the c...
PURPOSE: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.
BACKGROUND: Early diagnosis of severe acute pancreatitis (SAP) is essential to minimize its mortality and improve prognosis. We aimed to develop an accurate and applicable machine learning predictive model based on routine clinical testing results fo...
The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learnin...
Autologous reconstruction using abdominal flaps remains the most popular method for breast reconstruction worldwide. We aimed to evaluate a prediction model using machine-learning methods and to determine which factors increase abdominal flap donor s...
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 10, 2021
BACKGROUND: The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsuperv...
BACKGROUND: Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates ma...
BACKGROUND: Robotic prostatectomy is a costly new technology, but the costs may be offset by changes in treatment patterns. The net effect of this technology on Medicaid spending has not been assessed.
OBJECTIVE: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.
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