AIMC Topic: Predictive Value of Tests
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Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.
International journal of environmental research and public health
Jan 28, 2019
The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural netw...
Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study.
IEEE journal of biomedical and health informatics
Jan 23, 2019
This paper presents a novel method for hierarchical analysis of machine learning algorithms to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically, we develop a multi-layer machine learning approach to analyze ...
Predicting radiation pneumonitis in locally advanced stage II-III non-small cell lung cancer using machine learning.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 23, 2019
BACKGROUND AND PURPOSE: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine ...
Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI.
Scientific reports
Jan 23, 2019
Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce sy...
Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.
Journal of magnetic resonance imaging : JMRI
Jan 16, 2019
BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a m...
Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results.
Academic radiology
Jan 14, 2019
RATIONALE AND OBJECTIVES: Extraprostatic extension of disease (EPE) has a major role in risk stratification of prostate cancer patients. Currently, pretreatment local staging is performed with MRI, while the gold standard is represented by histopatho...
A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.
NeuroImage
Jan 14, 2019
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to pr...
Automated analysis of cardiovascular magnetic resonance myocardial native T mapping images using fully convolutional neural networks.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Jan 14, 2019
BACKGROUND: Cardiovascular magnetic resonance (CMR) myocardial native T mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T are measured manually by drawing region of interest in motion-corrected T...
Comparison of artificial neural network and logistic regression models for prediction of outcomes in trauma patients: A systematic review and meta-analysis.
Injury
Jan 11, 2019
BACKGROUND: Currently, two models of artificial neural network (ANN) and logistic regression (LR) are known as models that extensively used in medical sciences. The aim of this study was to compare the ANN and LR models in prediction of Health-relate...
Role of interleukin-3 as a prognostic marker in septic patients.
Revista Brasileira de terapia intensiva
Jan 10, 2019
OBJECTIVE: To evaluate the accuracy of IL-3 to predict the outcome of septic patients.
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