• Clinical and Experimental Pediatrics
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• v.49 no.4
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• pp.341-353
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• 2006

Learning disorders are diagnosed when the individual's achievement on standardized tests in reading, mathematics, or written expression is substantially below that expected for age, schooling, and level of intelligence. Subtypes of learning disorders may be classified into two groups, language-based type learning disorders including reading and writing disorder, and nonverbal type learning disorder (NLD) such as those relating to mathematics & visuospatial skills, and those in the autism spectrum. Converging evidence indicates that reading disorder represents a disorder within the language system and more specifically within a particular subcomponent of that system, phonological processing. Recent advances in neuroimaging technology, particularly the development of fMRI, provide evidences of a neurobiological basis for reading disorder, specifically a disruption of two left hemisphere posterior brain systems, one parieto-temporal, the other occipito-temporal. The former is the reading system for beginner reading, the latter for skilled reading. Compensatory engagement of anterior systems around the inferior frontal gyrus(Broca's area) and a posterior(right occipito-temporal) system is noted in persistent poor readers in long-term follow up study. The theoretical model proposed to explain NLD's source is not right hemisphere damage, but rather the white matter model. The working hypothesis of the white matter model is that the underdevelopment of, damage to, or dysfunction of cerebral white matter(long myelinated fibers) is the source of this disorder. The role of an evidence-based effective intervention in the remediation of children with learning disorder is discussed.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.31 no.3
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• pp.97-104
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• 2020

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

• Psychiatry investigation
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• v.15 no.12
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• pp.1144-1153
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• 2018

Objective One of the areas of social cognition is Theory of Mind (ToM) is defined as the capacity to interpret, infer and explain mental states underlying the behavior of others. When social cognition studies on neurodevelopmental disorders are examined, it can be seen that this skill has not been studied sufficiently in children with Specific Learning Disorder (SLD). Methods In this study, social cognition skills in children diagnosed with attention deficit hyperactivity disorder (ADHD), SLD or Autism Spectrum Disorder (ASD) evaluated before puberty and compared with controls. To evaluate the ToM skills, the first and second-order false belief tasks, the Hinting Task, the Faux Pas Test and the Reading the Mind in the Eyes Task were used. Results We found that children with neurodevelopmental disorders as ADHD, ASD, and SLD had ToM deficits independent of intelligence and language development. There was a significant correlation between social cognition deficits and problems experienced in many areas such as social communication and interaction, attention, behavior, and learning. Conclusion Social cognition is an important area of impairment in SLD and there is a strong relationship between clinical symptoms and impaired functionality.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.101-112
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• 1997

The purpose of this study was to examine usefulness of KEDI-Individual Basic Learning Skills Test as a diagnostic tool of learning disorders(LD). Learning disorder group consisted of two subgroups, verbal learning disorder group(VLD, n＝34) and nonverbal learning disorder group(NVLD, n＝14). Comparison group consisted of Dysthymia Disorder subgroup(n＝11) and Normal subgroup(n＝20). Performance of intelligence test and achievement test was examined in all 4 subgroups. In KEDI-WISC, VLD subgroup revealed primary problems in vocabulary, information and verbal-auditory attention test. NVLD group revealed primary problems in almost all performance tests such as visual acuity, psycho-motor coordination speed and visual-spatial organizations ability subtest. In KEDI-Individual Basic Learning Test, VLD group revealed primary problems in phonological coding process, word recognition and mathematics. For successful classification of LD children, the importance of achievement test and intelligence test was discussed by discriminant analysis and factor analysis. The results indicate that KEDI-Individual Basic Learning Skills is of considerable usefulness in diagnosing LD, but must be used in subtests, and additional tests must be conducted for thorough exploration of LD.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.253-256
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• 2017

This paper deals with the implementation of speech disorder detection system based on machine learning classification. Problems with speech are a common early symptom of a stroke or other brain injuries. Therefore, detection of speech disorder may lead to correction and fast medical treatment of strokes or cerebrovascular accidents. The speech disorder system can be implemented by extracting features from the input speech and classifying the features using machine learning algorithms. Ten machine learning algorithms with various scaling methods were used to discriminate speech disorder from normal speech. The detection system was evaluated by the TORGO database which contains dysarthric speech collected from speakers with either cerebral palsy or amyotrophic lateral sclerosis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7599-7606
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• 2015

The objective of this study was to examine the correlations between smart phone addictions (SPA) and learning disorder (LD), attention deficit hyperactivity disorder (ADHD), and depression of post secondary level students, who were believed to have decent degree of self-commands. The correlation between the degree of smart phone addiction and learning disorder was 46 (p<0.001) and the correlation between the degree of smart phone addiction and ADHD was 48 (p<0.001). Meanwhile, the correlation between learning disorder and ADHD was 64 (p<0.001). From the multiple comparison of learning disorders, bothe the learning disorder and the ADHD of a group with lower degree of smart phone addiction showed mean differences that were more statistically significant than those of a group with higher degree of smart phone addiction. The depression of a group with lower degree of smart phone addition was also more statistically significant than that of a group with higher degree of smart phone addiction.

• Clinical and Experimental Pediatrics
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• v.51 no.9
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• pp.911-921
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• 2008

Learning disabilities (LD) are heterogeneous group of disorders with evidences of genetic or familial trait, intrinsic to the individual and presume to be due to central nervous dysfunction. Learning disabilities and attention deficit hyperactivity disorder (ADHD) are the two of the most common disorders in the population of school-age children. Typically academic achievements in children with learning disabilities are significantly lower than expected by their normal or above normal range of IQ. Although academic and cognitive deficits are hallmarks of children with LD, those children are also at risk for a broad range of behavioral and emotional problems. Almost all cases meet criteria for at least one additional diagnosis such as ADHD, developmental coordination disorder, depression, anxiety, obsessive compulsive disorder, tic disorder, among which ADHD is particularly predominant. Because of the response to the therapeutic intervention program is promising and positive when applied early, it is critical to recognize patients as early as possible. Pediatricians often are the first to hear from parents worried about a childs academic progress. It is not the responsibility of pediatrician to make a diagnosis, referring children for a diagnostic evaluation of LD is a reasonable first step. Pediatricians can make early referral of suspicious children by asking some serial short questions about basic and processing skills. With a basic knowledge about the clinical characteristics, diagnostic and therapeutic procedures of LD, pediatricians also can provide primary counseling and education for parents at their outpatient clinical settings.

• International Journal of Contents
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• v.9 no.4
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• pp.11-15
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• 2013

"Protein Folding Problem" is considered to be one of the "Great Challenges of Computer Science" and prediction of disordered protein is an important part of the protein folding problem. Machine learning models can predict the disordered structure of protein based on its characteristic of "learning from examples". Among many machine learning models, we investigate the possibility of multilayer perceptron (MLP) as the predictor of protein disorder. The investigation includes a single hidden layer MLP, multi hidden layer MLP and the hierarchical structure of MLP. Also, the target node cost function which deals with imbalanced data is used as training criteria of MLPs. Based on the investigation results, we insist that MLP should have deep architectures for performance improvement of protein disorder prediction.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.