• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.281-286
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• 2023

Diagnosing reading disorders involves complex procedures to evaluate complex cognitive processes. For an accurate diagnosis, a series of tests and evaluations by human experts are required. In this study, we propose a quantitative tool to diagnose reading disorders based on natural reading behaviors using minimal human input. The eye movements of the third- and fourth-grade students were recorded while they read a text at their own pace. Seven machine learning models were used to evaluate the gaze patterns of the words in the presented text and classify the students as normal or having a reading disorder. The accuracy of the machine learning-based diagnosis was measured using the diagnosis by human experts as the ground truth. The highest accuracy of 0.8 was achieved by the support vector machine and random forest classifiers. This result demonstrated that machine learning-based automated diagnosis could substitute for the traditional diagnosis of reading disorders and enable large-scale screening for students at an early age.

• 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.

• 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 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 Medicine and Life Science
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• v.19 no.1
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• pp.1-7
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• 2022

Learning disabilities (LD), also known as learning disorders, refers to cases in which an individual experiences lower academic ability as compared to the normal range of intelligence, visual or hearing impairment, or an inability to peform learning. Children and adolescents with learning disabilities often have emotional or behavioral problems or co-existing conditions, including depression, anxiety disorders, difficulties with peer relationships, family conflicts, and low self-esteem. In most cases, attention deficit and hyperactivity disorder coexists. As learning disabilities have the characteristics of a difficult heterogeneous disease group that cannot be attributed to a single root cause, they are diagnosed based on an interdisciplinary approach through medicine and education, such as mental health medicine, education, psychology, special education, and neurology. In addition, for the accurate diagnosis and treatment of learning disabilities, the diagnosis, prescription, treatment, and educational intervention should be conducted in cooperation with doctors, teachers, and psychologists. The treatment of learning disabilities requires a multimodal approach, including medical and educational intervention. It is suggested that educational interventions such as the Individualized Education Plan (IEP) and the Response to Invention (RTI) should be implemented.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.153-172
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• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.28 no.4
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• pp.192-196
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• 2017

Social (pragmatic) communication disorder (SCD) is a new diagnosis included under communication disorders in the neurodevelopmental disorders section of Diagnostic and Statistical Manual of Mental Disorders-5. SCD is defined as a primary deficit in the social use of nonverbal and verbal communication. SCD has very much in common with pragmatic language impairment, which is characterized by difficulties in understanding and using language in context and following the social rules of language, despite relative strengths in word knowledge and grammar. SCD and Autism Spectrum Disorder (ASD) are similar in that they both involve deficits in social communication skills, however individuals with SCD do not demonstrate restricted interests, repetitive behaviors, insistence on sameness, or sensory abnormalities. It is essential to rule out a diagnosis of ASD by verifying the lack of these additional symptoms, current or past. The criteria for SCD are qualitatively different from those of ASD and are not equivalent to those of mild ASD. It is clinically important that SCD should be differentiated from high-functioning ASD (such as Asperger syndrome) and nonverbal learning disabilities. The ultimate goals are the refinement of the conceptualization, development and validation of assessment tools and interventions, and obtaining a comprehensive understanding of the shared and unique etiologic factors for SCD in relation to those of other neurodevelopmental disorders.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.17 no.2
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• pp.114-123
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• 2006

Objectives : The special class has been made, bringing rapid increase quantitatively. The authors carried out the child psychiatric interview and evaluation for 9 special-classed children in Asan city to find out clinical diagnosis and emotional/behavioral characteristics. Methods : The child psychiatrists evaluated special class children by DSM-IV and K-SADS-PL. Tools for the evaluation were Child Behavior Checklist- Korean version, Korean Personality Inventory for Children, Children's Depression Inventory, Abbreviated Conners Parent-Teacher Rating Scale-Revised, State-Trait Anxiety Inventory for Children, Vineland Social Maturity Scale, Wechsler Intelligence Scale for Children-III, and Childhood Autism Rating Scale. Results : Ultimately 53 children, consisting of 35 boys(67.9%) and 18 girls(32.1%), participated, and the average age was $10.5{\pm}1.3$ years old. Their measure of Vineland Social Maturity Scale was $78.7{\pm}20.0$, Childhood Autism Rating Scales was $25.4{\pm}9.0$, Child Depression Inventory was $22.2{\pm}5.2$, State-Trait Anxiety Inventory for Children was $35.2{\pm}8.2/36.5{\pm}6.2$, and Abbreviated Conners Parent-Teacher Rating Scale was $11.0{\pm}4.6$. In the clinical diagnosis evaluation, the prevalence rate of learning disorder was decreased compared to early research, ADHD had been newly appeared and depression disorder and anxiety disorder had been increased. Conclusion : This result suggests that a lot of children in a special class have complex emotional and behavioral problems in addition to educational problems.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1336-1345
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• 2021

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders in children. The diagnosis of ADHD in children is based on the interviews and observation reports of parents or teachers who have stayed with them. Since this approach cannot avoid long observation time and the bias of observers, another approach based on Electroencephalography(EEG) is emerging. The goal of this study is to develop an assistive tool for diagnosing ADHD by EEG classification. This study explores the frequency bands of EEG and extracts the implied features in them by using the proposed CNN. The CNN architecture has three Convolution-MaxPooling blocks and two fully connected layers. As a result of the experiment, the 30-60 Hz gamma band showed dominant characteristics in identifying EEG, and when other frequency bands were added to the gamma band, the EEG classification performance was improved. They also show that the proposed CNN is effective in detecting ADHD in children.