Development of Computerized Adaptive Testing for Emotion Regulation

Explainable Deep Attention Active Learning for Sentimental Analytics of Mental Disorder

With the increasing use of online mediums, Internet-delivered psychological treatments (IDPs) are becoming an essential tool for improving mental disorders. Online-based health therapies can help a large segment of the population with little resource investment. The task is greatly complicated by the overlapping emotions for specific mental health. Early adoption of a deep learning system presented severe difficulties, including ethical and legal considerations that contributed to a lack of trust. Modern models required highly interpretable, intuitive explanations that humans could understand. To achieve this, we present a deep attention model based on fuzzy classification that uses the linguistic features of patient texts to build emotional lexicons. In medical applications, a diversified dataset generates work. Active learning techniques are used to extend fuzzy rules and the learned dataset gradually. From this, the model can gain a reduction in labeling efforts in mental health applications. In this way, difficulties such as the amount of vocabulary per class, method of generation, the source of data, and the baseline for human performance level can be solved. Moreover, this work illustrates fuzzy explainability by using weighted terms. The proposed method incorporates a subset of unstructured data into the set for training and uses a similarity-based approach. The approach then updates the model training using the new training points in the subsequent cycle of the active learning mechanism. The cycle is repeated until the optimal solution is found. At this point, all unlabeled text is converted into the set for training. The experimental results show that the emotion-based enhancement improves test accuracy and helps develop quality criteria. In the blind test, the bidirectional LSTM architecture with an attention mechanism and fuzzy classification achieved an F1 score of 0.89.

Development of a Computerized Adaptive Test for Problematic Mobile Phone Use

The great number of mobile phone users in the world has increased in recent years. More time spent on a phone, more negative effects such as problematic mobile phone use. Many researchers have devoted themselves to revise tools to measure problematic mobile phone use better and more precisely. Previous studies have shown that these tools have good reliability and validity, but that most of them have some shortcomings because they were traditional paper-and-pencil tests based on Classical Test Theory (CTT). This study, based on Item Response Theory (IRT) in order to solve these shortcomings, developed Computerized Adaptive Test for problematic mobile phone use (CAT-PMPU) and discussed the performance of CAT-PMPU. Then, we used real data to simulate CAT, and the measurement accuracy and reliability between a paper-and-pencil test and CAT-PMPU were compared under the same test length. The results showed that CAT-PMPU was better than the paper-and-pencil test in all aspects, and that it can reduce the number of items and improve measurement efficiency effectively. In conclusion, the CAT-PMPU was developed in this study has good reliability, and it provided novel technical support for the measurement of problematic mobile phone use. It had a good application prospect.

Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment

Internet-Delivered Psychological Treatments (IDPT) consider the mental problems based on Internet interaction. As the increases of the pandemic, more online tools are then widely used to result in evidence-based mental health serves. This increase helps to cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help to solve mental health issues speedily. In this research, we propose a fuzzy contrast-based model that uses the attention network for positional weighted words, classifies mental patient authored text into distinct symptoms. After that, the trained embedding is then used to label the mental data. Then attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes. The method is compared with non-embedding and traditional techniques to demonstrate the proposed model. From the experiments, the feature vector can achieve a high ROC-Curve of 0.82 with nine symptoms problems.