scholarly journals Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Feng Lin ◽  
Jiarui Han ◽  
Teng Xue ◽  
Jilan Lin ◽  
Shenggen Chen ◽  
...  
Keyword(s):  
Age Of Onset ◽  
Brain Mri ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Epileptic Patients ◽  
Optimal Feature Selection ◽  
State Examination ◽  
Optimal Feature ◽  
Clinical Characteristic

AbstractMany studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in outpatients with epilepsy. Data from 441 outpatients with epilepsy were included; of these, 433 patients met the 12 clinical characteristic criteria and were divided into training (n = 304) and experimental (n = 129) groups. After descriptive statistics were analyzed, cross-validation was used to select the optimal model. The random forest (RF) algorithm was combined with the redundancy analysis (RDA) algorithm; then, optimal feature selection and resampling were carried out after removing linear redundancy information. The features that contributed more to multiple outcomes were selected. Finally, the external traceability of the model was evaluated using the follow-up data. The RF algorithm was the best prediction model for both MMSE and MoCA outcomes. Finally, seven markers were screened by overlapping the top ten important features for MMSE ranked by RF modeling, those ranked for MoCA ranked by RF modeling, and those for both assessments ranked by RDA. The optimal combination of features were namely, sex, age, age of onset, seizure frequency, brain MRI abnormalities, epileptiform discharge in EEG and usage of drugs. which was the most efficient in predicting outcomes of MMSE, MoCA, and both assessments.

scholarly journals Oxytocin and elderly MRI-based hippocampus and amygdala volume: a 7-year follow-up study

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Ryuzo Orihashi ◽  
Yoshito Mizoguchi ◽  
Yoshiomi Imamura ◽  
Shigeto Yamada ◽  
Takefumi Ueno ◽  
...  
Keyword(s):  
Elderly People ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Brain Volume ◽  
Brain Mri ◽  
Statistical Parametric Mapping ◽  
Clinical Dementia Rating ◽  
Amygdala Volume ◽  
State Examination

Abstract Oxytocin is deeply involved in human relations. In recent years, it is becoming clear that oxytocin is also involved in social cognition and social behaviour. Oxytocin receptors are also thought to be present in the hippocampus and amygdala, and the relationship between oxytocin and the structure and function of the hippocampus and amygdala has been reported. However, a few studies have investigated oxytocin and its relationship to hippocampus and amygdala volume in elderly people. The aim of this study is to investigate the association between serum oxytocin levels and hippocampus and amygdala volume in elderly people. The survey was conducted twice in Kurokawa-cho, Imari, Saga Prefecture, Japan, among people aged 65 years and older. We collected data from 596 residents. Serum oxytocin level measurements, brain MRI, Mini–Mental State Examination and Clinical Dementia Rating were performed in Time 1 (2009–11). Follow-up brain MRI, Mini–Mental State Examination and Clinical Dementia Rating were performed in Time 2 (2016–17). The interval between Time 1 and Time 2 was about 7 years. Fifty-eight participants (14 men, mean age 72.36 ± 3.41 years, oxytocin 0.042 ± 0.052 ng/ml; 44 women, mean age 73.07 ± 4.38 years, oxytocin 0.123 ± 0.130 ng/ml) completed this study. We analysed the correlation between serum oxytocin levels (Time 1) and brain volume (Time 1, Time 2 and Times 1–2 difference) using voxel-based morphometry implemented with Statistical Parametric Mapping. Analysis at the cluster level (family-wise error; P < 0.05) showed a positive correlation between serum oxytocin levels (Time 1) and brain volume of the region containing the left hippocampus and amygdala (Time 2). This result suggests that oxytocin in people aged 65 years and older may be associated with aging-related changes in hippocampus and amygdala volume.

scholarly journals Development of a Novel Neural Network Model for Brain Image Classification

2019 ◽  
Vol 8 (3) ◽  
pp. 7230-7235 ◽  
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Brain Mri ◽  
Classification Performance ◽  
Feature Reduction ◽  
Machine Learning Techniques ◽  
Mr Image ◽  
Medical Practitioners ◽  
Learning Techniques ◽  
The Brain

The analysis of brain MRI images is highly beneficial for the medical practitioners. Since the manual study of these images are time consuming and tedious, the automated process using software based system have been developed. The machine learning techniques are applied in developing brain MR image classification process. The classification process consists of dataset preparation, feature extraction, feature reduction and the use of classifier. In this paper, 2D DWT is used for feature extraction and PCA is used for feature reduction. ELM model is used as a classifier. The input weights and biases in ELM are randomly assigned. So EHO algorithm, a newly developed bio inspired algorithm is used to optimally determine the input weights and biases of ELM model. The classification performance of the EHO-ELM model is compared with basic ELM model for three of the brain MR image datasets. From the simulation study, it is found that the proposed EHO-ELM model outperformed the basic ELM model.

scholarly journals Alzheimer's Disease: A Survey

2021 ◽  
Vol 8 (1) ◽  
pp. 33-39
Author(s):  
Harshitha ◽  
Gowthami Chamarajan ◽  
Charishma Y
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Neural Networks ◽  
Brain Area ◽  
Brain Mri ◽  
Three Dimensional ◽  
The Other ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Brain

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease.

Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: an analysis of the ELSA-Brasil study

2020 ◽  
pp. 1-9
Author(s):  
Diego Librenza-Garcia ◽  
Ives Cavalcante Passos ◽  
Jacson Gabriel Feiten ◽  
Paulo A. Lotufo ◽  
Alessandra C. Goulart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Adult Health ◽  
Recurrent Depression ◽  
Elastic Net Regularization ◽  
Diagnosis And Prognosis ◽  
Learning Techniques ◽  
Occupational Cohort

Abstract Background Depression is highly prevalent and marked by a chronic and recurrent course. Despite being a major cause of disability worldwide, little is known regarding the determinants of its heterogeneous course. Machine learning techniques present an opportunity to develop tools to predict diagnosis and prognosis at an individual level. Methods We examined baseline (2008–2010) and follow-up (2012–2014) data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), a large occupational cohort study. We implemented an elastic net regularization analysis with a 10-fold cross-validation procedure using socioeconomic and clinical factors as predictors to distinguish at follow-up: (1) depressed from non-depressed participants, (2) participants with incident depression from those who did not develop depression, and (3) participants with chronic (persistent or recurrent) depression from those without depression. Results We assessed 15 105 and 13 922 participants at waves 1 and 2, respectively. The elastic net regularization model distinguished outcome levels in the test dataset with an area under the curve of 0.79 (95% CI 0.76–0.82), 0.71 (95% CI 0.66–0.77), 0.90 (95% CI 0.86–0.95) for analyses 1, 2, and 3, respectively. Conclusions Diagnosis and prognosis related to depression can be predicted at an individual subject level by integrating low-cost variables, such as demographic and clinical data. Future studies should assess longer follow-up periods and combine biological predictors, such as genetics and blood biomarkers, to build more accurate tools to predict depression course.

scholarly journals Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images using a Convolutional Neural Network Scheme

2019 ◽  
Author(s):  
Carlos Uziel Perez Malla ◽  
Maria del C. Valdes Hernandez ◽  
Muhammad Febrian Rachmadi ◽  
Taku Komura
Keyword(s):  
Magnetic Resonance ◽  
Ischaemic Stroke ◽  
Data Augmentation ◽  
Imaging Modality ◽  
Brain Mri ◽  
Machine Learning Techniques ◽  
Magnetic Resonance Perfusion ◽  
Mr Perfusion Imaging ◽  
Learning Techniques ◽  
The Impact

ABSTRACTMagnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describes the blood’s passage through the brain’s vascular network. Therefore it is widely used to assess cerebral ischaemia. Convolutional Neural Networks (CNN) constitute the state-of-the-art method in automatic pattern recognition and hence, in segmentation tasks. But none of the CNN architectures developed to date have achieved high accuracy when segmenting ischaemic stroke lesions, being the main reasons their heterogeneity in location, shape, size, image intensity and texture, especially in this imaging modality. We use a freely available CNN framework, developed for MR imaging lesion segmentation, as core algorithm to evaluate the impact of enhanced machine learning techniques, namely data augmentation, transfer learning and post-processing, in the segmentation of stroke lesions using the ISLES 2017 dataset, which contains expert annotated diffusion-weighted perfusion and diffusion brain MRI of 43 stroke patients. Of all the techniques evaluated, data augmentation with binary closing achieved the best results, improving the mean Dice score in 17% over the baseline model. Consistent with previous works, better performance was obtained in the presence of large lesions.

scholarly journals Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Luis Miguel Núñez ◽  
Enrique Romero ◽  
Margarida Julià-Sapé ◽  
María Jesús Ledesma-Carbayo ◽  
Andrés Santos ◽  
...  
Keyword(s):  
Therapy Response ◽  
Primary Brain Tumor ◽  
Machine Learning Techniques ◽  
Data Transformations ◽  
Learning Techniques ◽  
Human Adults ◽  
Therapy Strategies ◽  
And Control ◽  
Source Extraction

AbstractGlioblastoma is the most frequent aggressive primary brain tumor amongst human adults. Its standard treatment involves chemotherapy, for which the drug temozolomide is a common choice. These are heterogeneous and variable tumors which might benefit from personalized, data-based therapy strategies, and for which there is room for improvement in therapy response follow-up, investigated with preclinical models. This study addresses a preclinical question that involves distinguishing between treated and control (untreated) mice bearing glioblastoma, using machine learning techniques, from magnetic resonance-based data in two modalities: MRI and MRSI. It aims to go beyond the comparison of methods for such discrimination to provide an analytical pipeline that could be used in subsequent human studies. This analytical pipeline is meant to be a usable and interpretable tool for the radiology expert in the hope that such interpretation helps revealing new insights about the problem itself. For that, we propose coupling source extraction-based and radiomics-based data transformations with feature selection. Special attention is paid to the generation of radiologist-friendly visual nosological representations of the analyzed tumors.

Data Classification Using Support Vector Machines with Mixture Kernels

2013 ◽  
Vol 662 ◽  
pp. 936-939 ◽  
Author(s):  
Li Wei Wei ◽  
Chuan Shen Wei ◽  
Xia Qing Wan
Keyword(s):  
Statistical Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feature Subset ◽  
Computational Costs ◽  
Learning Techniques ◽  
Single Feature ◽  
Vector Machines ◽  
Optimal Feature Subset ◽  
Optimal Feature

Recent studies have showed that machine learning techniques are advantageous to statistical models for medicine database classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. Three UCI databases are used to demonstrate the good performance of the SVM- MK.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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