A Neural Approach to Ordinal Regression for the Preventive Assessment of Developmental Dyslexia

• URL: http://arxiv.org/abs/2002.02184v2
• Date: Tue, 20 Oct 2020 10:49:21 GMT
• Title: A Neural Approach to Ordinal Regression for the Preventive Assessment of Developmental Dyslexia
• Authors: F.J. Martinez-Murcia, A. Ortiz, Marco A. Formoso, M. Lopez-Zamora, J.L. Luque, A. Gim\'enez
• Abstract summary: Developmental Dyslexia (DD) is a learning disability related to the acquisition of reading skills that affects about 5% of the population. We propose a new methodology to assess the risk of DD before students learn to read.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developmental Dyslexia (DD) is a learning disability related to the acquisition of reading skills that affects about 5% of the population. DD can have an enormous impact on the intellectual and personal development of affected children, so early detection is key to implementing preventive strategies for teaching language. Research has shown that there may be biological underpinnings to DD that affect phoneme processing, and hence these symptoms may be identifiable before reading ability is acquired, allowing for early intervention. In this paper we propose a new methodology to assess the risk of DD before students learn to read. For this purpose, we propose a mixed neural model that calculates risk levels of dyslexia from tests that can be completed at the age of 5 years. Our method first trains an auto-encoder, and then combines the trained encoder with an optimized ordinal regression neural network devised to ensure consistency of predictions. Our experiments show that the system is able to detect unaffected subjects two years before it can assess the risk of DD based mainly on phonological processing, giving a specificity of 0.969 and a correct rate of more than 0.92. In addition, the trained encoder can be used to transform test results into an interpretable subject spatial distribution that facilitates risk assessment and validates methodology.

Related papers

• Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data [0.0]
  This study presents a novel semi-supervised approach for ASD detection using AutoEncoder-based Machine Learning (ML) methods. Our approach utilizes data collected manually through a serious game specifically designed for this purpose. Since the sensitive data collected by the gamified application are susceptible to privacy leakage, we developed a Federated Learning framework.
  arXiv  Detail & Related papers  (2024-10-25T23:00:12Z)
• Deep Learning for Assessment of Oral Reading Fluency [5.707725771108279]
  This work investigates end-to-end modeling on a training dataset of children's audio recordings of story texts labeled by human experts. We report the performance of a number of system variations on the relevant measures, and probe the learned embeddings for lexical and acoustic-prosodic features known to be important to the perception of reading fluency.
  arXiv  Detail & Related papers  (2024-05-29T18:09:35Z)
• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• Detection of developmental language disorder in Cypriot Greek children using a neural network algorithm [0.0]
  The study aims to develop an automated method for the identification of developmental language disorder (DLD) using artificial intelligence. This protocol is applied for the first time in a Cypriot Greek child population with DLD. The performance of the model was evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC/AUC curve.
  arXiv  Detail & Related papers  (2023-11-25T15:23:46Z)
• Ordinal Classification with Distance Regularization for Robust Brain Age Prediction [25.555190119033615]
  Age is one of the major known risk factors for Alzheimer's Disease (AD) Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. These methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects.
  arXiv  Detail & Related papers  (2023-10-25T20:39:07Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• Comparison of Probabilistic Deep Learning Methods for Autism Detection [0.0]
  Autism Spectrum Disorder (ASD) is one neuro developmental disorder that is now widespread in the world. Early detection of the disorder helps in the onset treatment and helps one to lead a normal life.
  arXiv  Detail & Related papers  (2023-03-09T17:49:37Z)
• NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants [73.85768093666582]
  We propose an explainable geometric deep network dubbed NeuroExplainer. NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
  arXiv  Detail & Related papers  (2023-01-01T12:48:12Z)
• Estimating and Improving Fairness with Adversarial Learning [65.99330614802388]
  We propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We evaluate our framework on a large-scale public-available skin lesion dataset.
  arXiv  Detail & Related papers  (2021-03-07T03:10:32Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.