Objectives Psychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists. Design Cross-sectional study. Setting We conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists. Participants An AI model of the neural network and six psychiatrists. Primary outcome The accuracies of the AI model and psychiatrists for predicting psychological distress. Methods In total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model. Results The accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy. Conclusions A machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views. No data are available.

Depression is a global problem and has serious implications for personal health and the economy. Therefore, there is an urgent need for fast and effective screening tools to combat the epidemic of depression. Currently, Japanese researchers have discovered that artificial intelligence (AI) can be used to detect signs of depression. In a study published this month BMJ Open, University of Tsukuba researchers, AI system Machine learning I was able to predict Psychological distress Among workers who are risk factors for depression. There are many questionnaires, Mental health statusIndividuals may hesitate to answer questions about subjective mood honestly because of social stigma about mental health..

Tsukuba, Japan - Depression is a worldwide problem, with serious consequences for individual health and the economy, and rapid and effective screening tools are thus urgently needed to counteract its increasing prevalence. Now, researchers from Japan have found that artificial intelligence (AI) can be used to detect signs of depression. In a study published this month in BMJ Open, researchers from University of Tsukuba have revealed that an AI system using machine learning could predict psychological distress among workers, which is a risk factor for depression. Although many questionnaires exist that screen for mental health conditions, individuals may be hesitant to answer truthfully questions about subjective mood due to social stigma regarding mental health. However, a machine learning system could be used to screen depression/psychological distress without such data, something the researchers at University of Tsukuba aimed to address.

Emotional intelligence is a cornerstone of human interactions--an essential part of what it means to be human. But now, artificial intelligences are being developed to better read and process human emotions, which is already changing the way we interact with robots. In the early 1990s, psychologists Salovey and Mayer were the first to recognize emotional intelligence as a set of knowledge and skills distinct from other forms of intelligence, defining it as "the ability to monitor one's own and other's feelings and emotions, to discriminate among them, and to use this information to guide one's thinking and actions." Emotional intelligence is something that seems wonderfully and innately human. But it turns out the tenets of emotional intelligence--which we start picking up in infancy and which seem so closely linked to human nature itself--can be quantified and reduced to logical procedures and algorithms.

We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress. We emphasize on the perception part of the system, a multimodal framework which captures and analyzes user state for both behavioral understanding and interactional purposes.