Search results for "machine learning"
showing 10 items of 1464 documents
Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction
2020
Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total of 26 patients with PD and without deep brain stimulation (DBS), 20 control subjects, and eight patients with PD and with DBS completed the task. Eight metrics, each designed to capture an aspect of motor dysfunction in…
Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting
2020
Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsoni…
Automatic detection and measurement of nuchal translucency.
2017
In this paper we propose a new methodology to support the physician both to identify automatically the nuchal region and to obtain a correct thickness measurement of the nuchal translucency. The thickness of the nuchal translucency is one of the main markers for screening of chromosomal defects such as trisomy 13, 18 and 21. Its measurement is performed during ultrasound scanning in the first trimester of pregnancy. The proposed methodology is mainly based on wavelet and multi resolution analysis. The performance of our method was analysed on 382 random frames, representing mid-sagittal sections, uniformly extracted from real clinical ultrasound videos of 12 patients. According to the groun…
Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
2020
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Towards identifying drug side effects from social media using active learning and crowd sourcing.
2019
Motivation Social media is a largely untapped source of information on side effects of drugs. Twitter in particular is widely used to report on everyday events and personal ailments. However, labeling this noisy data is a difficult problem because labeled training data is sparse and automatic labeling is error-prone. Crowd sourcing can help in such a scenario to obtain more reliable labels, but is expensive in comparison because workers have to be paid. To remedy this, semi-supervised active learning may reduce the number of labeled data needed and focus the manual labeling process on important information. Results We extracted data from Twitter using the public API. We subsequently use Ama…
Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power
2011
A major difficulty in the analysis of complex biological systems is dealing with the low signal-to-noise inherent to nearly all large biological datasets. We discuss powerful bioinformatic concepts for boosting signal-to-noise through external knowledge incorporated in processing units we call filters and integrators. These concepts are illustrated in four landmark studies that have provided model implementations of filters, integrators, or both.
Efficient Online Laplacian Eigenmap Computation for Dimensionality Reduction in Molecular Phylogeny via Optimisation on the Sphere
2019
Reconstructing the phylogeny of large groups of large divergent genomes remains a difficult problem to solve, whatever the methods considered. Methods based on distance matrices are blocked due to the calculation of these matrices that is impossible in practice, when Bayesian inference or maximum likelihood methods presuppose multiple alignment of the genomes, which is itself difficult to achieve if precision is required. In this paper, we propose to calculate new distances for randomly selected couples of species over iterations, and then to map the biological sequences in a space of small dimension based on the partial knowledge of this genome similarity matrix. This mapping is then used …
Sensory methodologies and the taste of water
2009
/WOS: 000285178000010; International audience; Describing the taste of water is a challenge since drinking water is supposed to have almost no taste. In this study, different classical sensory methodologies have been applied in order to assess sensory characteristics of water and have been compared: sensory profiling, Temporal Dominance of Sensations and free sorting task. These methodologies present drawbacks: sensory profile and TDS do not provide an effective discrimination of the taste of water and the free sorting task is efficient but does not enable data aggregation. A new methodology based on comparison with a set of references and named “Polarized Sensory Positioning” (PSP) has bee…
Sort and beer: Everything you wanted to know about the sorting task but did not dare to ask
2011
author cannot archive publisher's version/PDF; International audience; In industries, the sensory characteristics of products are key points to control. The method commonly used to characterize and describe products is the conventional profile. This very efficient method requires a lot of time to train assessors and to teach them how to quantify the sensory characteristics of interest. Over the last few years, other faster and less restricting methods have been developed, such as free choice profile, flash profile, projective mapping or sorting tasks. Among these methods, the sorting task has recently become quite popular in sensory evaluation because of its simplicity: it only requires ass…
Cell state prediction through distributed estimation of transmit power
2019
Determining the state of each cell, for instance, cell outages, in a densely deployed cellular network is a difficult problem. Several prior studies have used minimization of drive test (MDT) reports to detect cell outages. In this paper, we propose a two step process. First, using the MDT reports, we estimate the serving base station’s transmit power for each user. Second, we learn summary statistics of estimated transmit power for various networks states and use these to classify the network state on test data. Our approach is able to achieve an accuracy of 96% on an NS-3 simulation dataset. Decision tree, random forest and SVM classifiers were able to achieve a classification accuracy of…