AI in Psychology
Traumatic events, such as natural disasters, may cause fear and anxiety among children. Therefore, it is prudent to ensure that they receive coping mechanisms before it completely affects their mental health. According to Dr. Cynthia Langtiw in Good Day Chicago, initiating time to talk to children about the event will be crucial in calming them down. The psychology expert also advises that guardians should allow children to talk about their feelings and answer he questions that children ask according to the traumatic event. From the video clip, it can be deduced that diagnosing anxiety among children should be prioritized. Other types of stressful events that may affect children include those related to family discord and health (Tandon et al., 2009). Discussing topics that cause stress among children is important since it may help them overcome anxiety and stress. Furthermore, talking with them reduces the chances of overwhelming themselves. This analysis asserts that Artificial Intelligence will help in diagnosing stress and anxiety among children.
Background
Among a group of five children, one child suffers from anxiety and depression. Offering full diagnosis for them is difficult since children below age 8 typically experience difficulties in communicating their emotional state. Additionally, they depend on adults to comprehend their feelings. However, Artificial Intelligence (AI) will be the solution to this issue. A recent study revealed that machine learning algorithms could detect signs of anxiety ad stress, both known as “internalizing disorders,” in children’s speech patterns (McGinnis et al., 2019). this new approach could be groundbreaking in the diagnosis of children with anxiety and depression. Moreover, it offers quick and efficient ways of identifying the conditions that were initially difficult to see and often overlooked among children. Children respond better to treatment as they still develop, making early diagnosis crucial. When anxiety and depression are left untreated, it places them at increased risk of suicide and drug abuse later in life.
How it Works
In the groundbreaking study on AI’s role in diagnosis, the researchers employed “an adapted version of a mood induction task,” also named the Trier-Social Stress Task. It is meant to cause stressful and anxious feelings in a participant. 71 children participated in the study, and their ages were between 3 and 8 (McGinnis et al., 2019). They were told to improvise a story that took three minutes and would then get judged on if it was interesting. The researcher was the judge and maintained stern and negative feedback. The judge would notify them of the amount of time left. Also, children in the study got diagnosed by the use of structured clinical interviews as well as a parent questionnaire. These two techniques are known ways that help identify internalizing disorders among children. Machine learning algorithm assisted in analyzing data from the audio recordings of the children who narrated their stories, then related to the child diagnosis. It was found that the algorithm successfully diagnosed the children. It also gave results faster compared to conventional techniques. This algorithm takes only a few seconds to process time as soon as the task is over to offer its diagnosis.
Diagnosing depression and anxiety among children were further complicated by parents’ failure to recognize symptoms, insurance issues, and appointment waiting lists. These circumstances prevented children from receiving treatment.However, with the successful research, developing the algorithm to become a universal tool for clinical purposes through smartphone apps that can record and diagnose immediately is the next step. This new development, alongside motion analysis, could together combine to assist in identifying kids who are at risk of anxiety and stress early on before parents even suspect.
References
Fox32. (2017). Coping with the stress and anxiety born out of natural disasters. Retrieved from https://www.fox32chicago.com/video/395232
McGinnis, E. W., Anderau, S. P., Hruschak, J., Gurchiek, R. D., Lopez-Duran, N. L., Fitzgerald, K., … & McGinnis, R. S. (2019). Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood. IEEE Journal of biomedical and health informatics, 23(6), 2294-2301.
Tandon, M., Cardeli, E., & Luby, J. (2009). Internalizing disorders in early childhood: A review of depressive and anxiety disorders. Child and Adolescent Psychiatric Clinics, 18(3), 593-610.
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