scholarly journals Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Jörn Lötsch ◽  
Susanne Schiffmann ◽  
Katja Schmitz ◽  
Robert Brunkhorst ◽  
Florian Lerch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Serum Concentration ◽  
High Accuracy ◽  
Lipid Mediator ◽  
Multiple Sclerosis Patients ◽  
Concentration Patterns

Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls

2018 ◽  
Vol 31 (3) ◽  
pp. 346-363 ◽  
Author(s):  
Hanni Kiiski ◽  
Lee Jollans ◽  
Seán Ó. Donnchadha ◽  
Hugh Nolan ◽  
Róisín Lonergan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Cognitive Functioning ◽  
Processing Speed ◽  
Multiple Sclerosis Patients

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

2021 ◽  
Author(s):  
Furkan Bilek ◽  
Ferhat Balgetir ◽  
Caner Feyzi Demir ◽  
Gökhan Alkan ◽  
Seda Arslan-Tuncer
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Sensitivity And Specificity ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Spatiotemporal Parameters ◽  
Multiple Sclerosis Patients

Abstract Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC). Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively. Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.

Serum immunologic markers in multiple sclerosis patients on continuous combined therapy with beta-interferon 1a, prednisone and azathioprine

2006 ◽  
Vol 12 (5) ◽  
pp. 652-658 ◽  
Author(s):  
C A Braun Hashemi ◽  
Y CQ Zang ◽  
J A Arbona ◽  
J A Bauerle ◽  
M L Frazer ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Serum Level ◽  
Serum Concentration ◽  
Combined Therapy ◽  
Intercellular Adhesion Molecule ◽  
Intercellular Adhesion Molecule 1 ◽  
Beta Interferon ◽  
Immunologic Markers ◽  
Multiple Sclerosis Patients ◽  
Factor Α

Break-through symptoms (BTS) in multiple sclerosis (MS) patients on beta-interferon (beta-IFN) monotherapy are most frequently treated with a brief administration of steroids. Here, we report the results of monitoring serum immunologic markers recorded at three-month intervals for 1.5 years in responders to beta-INF 1a (Avonex) monotherapy ( n = 21) and MS patients placed on Avonex with prednisone ( n = 83) and Avonex, prednisone and azathioprine (AZA) ( n = 21) because of BTS. Compared to 23 healthy controls, patients on Avonex monotherapy and Avonex with prednisone, in individuals on Avonex, prednisone and AZA, a significant decrease in serum concentration of soluble intercellular adhesion molecule-1 (sICAM-1) ( P = 0.001) was established. Combined therapy with Avonex, prednisone and AZA was associated with a significant increase in the serum level of interleukin (IL)10 ( P < 0.001). Compared to Avonex monotherapy, combined therapy suppressed the serum level of IL12p40, antagonized elevation in the serum concentration of soluble IL2 receptor (sIL2R) and inhibited an increase in the serum soluble CD95 (sCD95) molecule. In patients studied, no significant differences in the serum level of IL18 and tumor necrosis factor-α (TNF-α) were established. These findings are important in understanding some of the immunoregulatory mechanisms induced by combined therapy in MS.

scholarly journals Prediction of disease progression and outcomes in multiple sclerosis with machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mauro F. Pinto ◽  
Hugo Oliveira ◽  
Sónia Batista ◽  
Luís Cruz ◽  
Mafalda Pinto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Age At Onset ◽  
Clinical Information ◽  
Chronic Inflammatory Disease ◽  
Compensatory Mechanisms ◽  
Disease Evolution ◽  
Secondary Progressive ◽  
First Case ◽  
Multiple Sclerosis Patients

AbstractMultiple Sclerosis is a chronic inflammatory disease, affecting the Central Nervous System and leading to irreversible neurological damage, such as long term functional impairment and disability. It has no cure and the symptoms vary widely, depending on the affected regions, amount of damage, and the ability to activate compensatory mechanisms, which constitutes a challenge to evaluate and predict its course. Additionally, relapsing-remitting patients can evolve its course into a secondary progressive, characterized by a slow progression of disability independent of relapses. With clinical information from Multiple Sclerosis patients, we developed a machine learning exploration framework concerning this disease evolution, more specifically to obtain three predictions: one on conversion to secondary progressive course and two on disease severity with rapid accumulation of disability, concerning the 6th and 10th years of progression. For the first case, the best results were obtained within two years: AUC=$$0.86\pm 0.07$$ 0.86 ± 0.07 , sensitivity=$$0.76\pm 0.14$$ 0.76 ± 0.14 and specificity=$$0.77\pm 0.05$$ 0.77 ± 0.05 ; and for the second, the best results were obtained for the 6th year of progression, also within two years: AUC=$$0.89\pm 0.03$$ 0.89 ± 0.03 , sensitivity=$$0.84\pm 0.11$$ 0.84 ± 0.11 , and specificity=$$0.81\pm 0.05$$ 0.81 ± 0.05 . The Expanded Disability Status Scale value, the majority of functional systems, affected functions during relapses, and age at onset were described as the most predictive features. These results demonstrate the possibility of predicting Multiple Sclerosis progression by using machine learning, which may help to understand this disease’s dynamics and thus, advise physicians on medication intake.

scholarly journals Machine Learning Analysis of Motor Evoked Potential Time Series to Predict Disability Progression in Multiple Sclerosis

2019 ◽  
Author(s):  
Jan Yperman ◽  
Thijs Becker ◽  
Dirk Valkenborg ◽  
Veronica Popescu ◽  
Niels Hellings ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Time Series ◽  
Hypothesis Test ◽  
Small Data ◽  
Disability Progression ◽  
Data Set ◽  
Extra Time ◽  
Multiple Sclerosis Patients ◽  
Learning Analysis

AbstractBackgroundEvoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years. Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabiliation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients.MethodsWe extracted a large number of time series features from the motor EPs with the highly comparative time series analysis software package. Mutual information with the target and the Boruta method are used to find features which contain information not included in the features studied in the literature. We use random forests (RF) and logistic regression (LR) classifiers to predict disability progression after two years. Statistical significance of the performance increase when adding extra features is checked with the DeLong hypothesis test.ResultsIncluding extra time series features in motor EPs leads to a statistically significant improvement compared to using only the known features, although the effect is limited in magnitude (∆AUC = 0.02 for RF and ∆AUC = 0.05 for LR). RF with extra time series features obtains the best performance (AUC = 0.75 ± 0.07), which is good considering the limited number of biomarkers in the model. RF (a nonlinear classifier) outperforms LR (a linear classifier).ConclusionsUsing machine learning methods on EPs shows promising predictive performance. Using additional EP time series features beyond those already in use leads to a modest increase in performance. Larger datasets, preferably multi-center, are needed for further research. Given a large enough dataset, these models may be used to support clinicians in their decision making process regarding future treatment.

scholarly journals The classification of neurodegenerative disease from acoustic speech data

2021 ◽  
Author(s):  
Benjamin Schultz ◽  
Zaher Joukhadar ◽  
Maria del Mar Quiroga ◽  
Usha Nattala ◽  
Gustavo Noffs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Neurodegenerative Diseases ◽  
Neurodegenerative Disease ◽  
Spectral Power ◽  
Friedreich Ataxia ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Acoustic Features

Abstract Neurodegenerative diseases often affect speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to differentiate healthy speech and speech from people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We extracted 74 spectral and temporal prosodic features from the speech recordings, which were subjected to machine learning. Results showed that Friedreich ataxia, multiple sclerosis and healthy controls were all identified with high accuracy (over 82%). Twenty-one acoustic features were strong markers of neurodegenerative diseases, falling under the categories of spectral qualia, spectral power, and speech rate. We demonstrated that speech markers can delineate neurodegenerative diseases and distinguish healthy speech from pathological speech with high accuracy. Findings emphasize the importance of examining speech outcomes when assessing indicators of neurodegenerative disease. We propose large-scale initiatives to broaden the scope for differentiating other neurological diseases and affective disorders.

scholarly journals Machine learning-optimized Combinatorial MRI scale (COMRISv2) correlates highly with cognitive and physical disability scales in Multiple Sclerosis patients

2021 ◽  
Author(s):  
Erin Kelly ◽  
Mihael Varosanec ◽  
Peter Kosa ◽  
Mary Sandford ◽  
Vesna Prchkovska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Physical Disability ◽  
Brain Mri ◽  
Cognitive Disability ◽  
Mri Imaging ◽  
Tissue Destruction ◽  
Clinical Scales ◽  
Volumetric Features ◽  
Multiple Sclerosis Patients

AbstractComposite MRI scales of central nervous system tissue destruction correlate stronger with clinical outcomes than their individual components in multiple sclerosis (MS) patients. Using machine learning (ML), we previously developed Combinatorial MRI scale (COMRISv1) solely from semi-quantitative (semi-qMRI) biomarkers. Here, we asked how much better COMRISv2 might become with the inclusion of quantitative (qMRI) volumetric features and employment of more powerful ML algorithm.The prospectively acquired MS patients, divided into training (n=172) and validation (n=83) cohorts underwent brain MRI imaging and clinical evaluation. Neurological examination was transcribed to NeurEx app that automatically computes disability scales. qMRI features were computed by LesionTOADS algorithm. Modified random forest pipeline selected biomarkers for optimal model(s) in the training cohort.COMRISv2 models validated moderate correlation with cognitive disability (Rho = 0.674; Linh’s concordance coefficient [CCC] = 0.458; p<0.001) and strong correlations with physical disability (Spearman Rho = 0.830-0.852; CCC = 0.789-0.823; p<0.001). The NeurEx led to the strongest COMRISv2 model. Addition of qMRI features enhanced performance only of cognitive disability model, likely because semi-qMRI biomarkers measure infratentorial injury with greater accuracy.COMRISv2 models predict most granular clinical scales in MS with remarkable criterion validity, expanding scientific utilization of cohorts with missing clinical data.

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