• Educational Technology International
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• v.23 no.2
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• pp.157-181
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• 2022

Scale scores for cognitive domains have been used as an important indicator for both academic achievement and clinical diagnosis. For example, in education, Cognitive Abilities Test (CogAT) has been used to measure student's capability in academic learning. In a clinical setting, Cognitive Impairment Screening Test utilizes items measuring cognitive ability as a dementia screening test. We demonstrated a procedure of generating cognitive ability test items similar as in CogAT but the theory associated with the generation is totally different. When creating cognitive test items, we applied automatic item generation (AIG) that reduces errors in predictions of cognitive ability but attains higher reliability. We selected two cognitive ability test items, categorized as a time estimation item for measuring quantitative reasoning and a paper-folding item for measuring visualization. As CogAT has widely used as a cognitive measurement test, developing an AIG-based cognitive test items will greatly contribute to education field. Since CLASS is the only LMS including AIG technology, we used it for the AIG software to construct item models. The purpose of this study is to demonstrate the item generation process using AIG implemented within CLASS, along with proving quantitative and qualitative strengths of AIG. In result, we confirmed that more than 10,000 items could be made by a single item model in the quantitative aspect and the validity of items could be assured by the procedure based on ECD and AE in the qualitative aspect. This reliable item generation process based on item models would be the key of developing accurate cognitive measurement tests.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.860-863
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• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme. However, the previously proposed scheme has the problem of lower diagnostic resolution. In order to improve the diagnostic resolution, a new diagnostic scheme based on extended FCM which incorporates the concept of fuzzy number into FCM is developed in this paper. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and pattern matching scheme are also proposed.

• Korean Journal of Adult Nursing
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• v.26 no.1
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• pp.34-45
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• 2014

Purpose: The purpose of this study was to investigate the effect of laughter therapy and cognitive reinforcement program on self-efficacy, depression and cognitive functions of the elderly with mild cognitive impairments (MCI). Methods: The study design was a non-equivalent control group pre and posttest design. Thirty-six subjects over the age of 65 with a diagnosis of mild cognitive impairment were assigned either to a treatment or a comparison group. Data were collected from February 7 to March 27, 2012 in the dementia supporting center. An eight week treatment program that included laughter therapy coupled with a cognitive reinforcing program including hand exercise, laughter dance routine, laughter technic and cognitive training for attention, memory, orientation and execution skill. Results: MoCA-K (t=-6.86, p<.001) and Stroop test CW correct (t=-2.54, p=.008), self-efficacy (t=-3.62, p=.001) in the treatment group were significantly higher than those of the comparison group. Reported depression (t=2.29, p=.014), Stroop test CW error (U=53.50, p<.001) in the treatment group was significantly less than the comparison group. Conclusion: In this study, the treatment was effective in improving self-efficacy, cognitive function and reducing depression in the elderly with MCI.

• School Mathematics
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• v.10 no.2
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• pp.259-277
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• 2008

Conventional assessments only provide a single summary score that indicates the overall performance level or achievement level of a student in a single learning area. For assessments to be more effective, test should provide useful diagnostic information in addition to single overall scores. Cognitive diagnosis modeling provides useful information by estimating individual knowledge states by assessing whether an examinee has mastered specific attributes measured by the test(Embretson, 1990; DiBello, Stout, & Rousses, 1995; Tatsuoka, 1995). Attributes are skills or cognitive processes that are required to perform correctly on a particular item. By the results of this study, students, parents, and teachers would be able to see where a student stands with respect to mastering the attributes. Such information could be used to guide the learner and teacher toward areas requiring more study. By being able to assess where they stand in regard to the attributes that compose an item, students can plan a more effective learning path to be desired proficiency levels. It would be very helpful to the examinee if score reports can provide the scale scores as well as the skill profiles. While the scale scores are believed to provide students' math ability by reporting only one score point, the skill profiles can offer a skill level of strong, weak or mixed for each student for each skill.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.1063-1067
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• 2015

Background: Tumor associated fatigue (TAF) or cancer related fatigue (CRF) is not a new concept. Nonetheless, no real headway has been made in the quantitative analysis of its successful treatment via cognitive behavioral therapy. Since 20 to 30% of all breast cancer patients suffer from anxiety and/or depression within the first year of their diagnosis, this issue needs to be addressed and a standard treatment protocol has to be developed. This study focused on developing a simple, reproducible and short (8 weeks) protocol for the cognitive behavioral therapy support of tumor associated fatigue patients. Materials and Methods: Between the year 2011 and 2012, 23 breast cancer patients fulfilled the diagnosis TAF requirements and were introduced into this study. Our method focused on a psycho-oncological support group using a predetermined, highly structured and reproducible, cognitive behavioral therapy treatment manual. Eight weekly, 90 minute sessions were conducted and patients were evaluated before and after this eight session block. Tumor fatigue specific questionnaires such as the multidimensional fatigue inventory (MFI) as well as the hospital anxiety and depression scale (HADS) were used in order to quantitatively evaluate patient TAF. Results: Of the 23 patients enrolled in the study, only 7 patients fulfilled the TAF diagnostic criteria after the psycho-oncological group treatment. This represents a 70% reduction in diagnosable tumor associated fatigue. The HADS analysis showed a 33% reduction in patient anxiety as well as a 57% reduction in patient depression levels. The MFI scores showed a significant reduction in 4 of the 5 evaluate categories. With the exception of the "mental fatigue" MFI category all results were statistically significant. Conclusions: This study showed that a highly structured, cognitive behavioral therapy group intervention will produce significant improvements in breast cancer patient tumor associated fatigue levels after only 8 weeks.

• Korean Journal of Veterinary Research
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• v.62 no.3
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• pp.25.1-25.9
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• 2022

Canine cognitive dysfunction syndrome (CDS) is a neurodegenerative disease that causes cognitive and behavioral disorders and reduces the quality of life in dogs and their guardians. This study reviewed the complementary and alternative medicine (CAM) for CDS and compared the diagnosis and therapy of CAM between CDS in canines and dementia in humans. The evaluation tools for the diagnosis of CDS and dementia were similar in the neurological and neuropsychiatric examinations, daily life activity, cognitive tests, and neuroimaging, but the evaluation for dementia was further subdivided. In CAM, pattern identification is a diagnostic method for accurate, personalized treatment, such as herbal medicine. For herbal medicine treatment of cognitive impairment in canines and humans, a similar pattern identification classified as deficiency (Qi, blood, and Yin) and Excess (phlegm, Qi stagnation, and blood stasis) is being used. However, the veterinary clinical basis for verifying the efficacy and safety of CAM therapies for CDS is limited. Therefore, based on CAM evidence in dementia, it is necessary to establish CDS-targeted CAM diagnostic methods and therapeutic techniques considering the anatomical, physiological, and pathological characteristics of dogs.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.517-527
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• 2022

This study aimed to compare changes in cognitive function, depression and ability to perform activity of daily living (ADL) in patients with dementia, mild cognitive impairment (MCI), and ischemic stroke (IS) and to identify factors associated with changes in instrumental ADL. A total of 86 patients (dementia=30, MCI=32, and IS=24) were included to analyse cognitive function, depression, and basic and instrumental ADL obtained at the time of diagnosis and 1 year after baseline. Repeated measures analysis of variance and multiple linear regression were used. A significant group by time interaction was found in executive function (p=.037) and instrumental ADL (p=.023) across groups. The MCI group has little change in executive function and instrumental ADL from the baseline to 1 year after diagnosis while other two groups showed changes with the dementia group showing declines and the group of IS having improvement in these factors over time. Changes in executive function(p=.030) and basic ADL (p<.001) explained 26.9% in the variance of changes in instrumental ADL. These findings showed a different changing pattern in executive function during the first year after diagnosis of dementia, MCI, and IS which have cognitive changes as their main symptoms, probably leading to a different changing pattern in instrumental ADL. Healthcare professionals should routinely assess for executive function and instrumental ADL problems and intervene to maintain and improve these functional outcomes immediately after disease.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.638-649
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• 2019

The purpose of this study was to investigate the usefulness of the cognitive function test tool in the clinical or multi-life environment for the elderly and high-risk demented subjects after the development of the non-clinical dementia early diagnosis test content(Co-Wis) based on the contents of the International Clinical Data Standard(CDISC TAUG-Alzheimer's v 2.0, SDTMIG v3.3) And to verify the validity and reliability of the data. To do this, after searching for dementia diagnosis process, we developed a non-clinical dementia diagnosis content(Co-Wis) that can supplement the shortcomings of the existing paper test. We selected 30 subjects from elders who were over 60 years old and verified the validity of test and the reliability of retest among cognitive domains of the Korean MMSE-K, Seoul Neuropsychological Test(SNSB-II) and non-medication dementia diagnosis content(Co-Wis). As a result, we showed high correlation and reliability in all cognitive domains. However, the limitations of insufficient subjects and regional distribution were identified. Based on the results of the study, we discussed the necessity of supplementing and expanding further studies such as various methods of verifying validity and reliability.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.185-192
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• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.