Artificial intelligence, or AI, is a game-changing innovation and has many capabilities. One of those is the ability to detect signs of anxiety and depression in the speech patterns of children. According to a new study published in the Journal of Biomedical and Health Informatics, a machine-learning algorithm could be an easy way to spot anxiety and depression.<\/span><\/p>\n

The study lead author Ellen McGinnis, a Ph.D. candidate at the University of Vermont, made an objective test to find out which children are suffering to get them the help they need as soon as possible.<\/span><\/p>\n

Early diagnosis of various conditions can be a vital aspect of treatment. Children respond well to it when their brains are still developing, but if left untreated, they\u2019re at an even greater chance of substance abuse and suicide later in life.<\/span><\/p>\n

The study used a mood induction task called the Trier-Social Stress Task. This task is used to create feelings of stress and anxiety in the children to see if they suffer from these conditions. The researchers picked seventy-one kids of different ages to work on a 3-minute story and were told how they would be graded.<\/span><\/p>\n

The judge appeared to keep a straight face throughout and provided mostly negative feedback. After 90 seconds, a judge would tell them their time was up. They designed the task in a way that\u2019s stressful, so it helps to put them in the mindset of being judged by someone.<\/span><\/p>\n

Another task they used was to diagnose the children using a structured clinical interview and parent questionnaire. Both these methods are well-established ways of identifying internalizing disorders in children.<\/span><\/p>\n

The researchers next fed a Machine Learning algorithm with the statistical features of an audio recording. With the algorithm, they could accurately diagnose which type of childhood disability the child had. The algorithm was highly successful at diagnosing children.<\/span><\/p>\n

The algorithm would identify children with an internalizing disorder with 80% accuracy, then compare it to the accuracy of the parent checklist.<\/span><\/p>\n

The study also showed that the algorithm provides results much more quickly. Once it finishes, it takes only a few seconds for the program to provide a diagnosis.\u00a0<\/span><\/p>\n

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We Can Help!<\/h5>\n

MHThrive provides Individual Therapy, Couples and Marriage Counseling, and Family Therapy at our locations in Katy, The Woodlands, and the Clear Lake area of Houston, Texas. We also provide telehealth therapy for anyone who resides within the State of Texas. To schedule an appointment with one of the MHThrive therapists, contact us at\u00a0713-477-0333<\/a>\u00a0or visit\u00a0www.mhthrive.com<\/a>\u00a0to learn more.<\/p>\n

If you or someone you know is experiencing any mental health or substance abuse issues,\u00a0New Dimensions<\/a>\u00a0can help. Our team of experienced therapists and psychiatrists can help you overcome these challenges and help you develop the skills you need to thrive. To schedule a complementary assessment or to find out more about our programs, contact us at\u00a01-800-685-9796<\/a>.<\/p>\n

Online Treatment Programs<\/a>\u00a0provides Teletherapy Partial Hospitalization and Intensive Outpatient Programs allowing participants to receive intensive therapy with our licensed therapists and psychiatrists without having to leave home. \u00a0If you or someone you know is struggling to overcome depression, anxiety, bipolar disorder, trauma, panic attacks, PTSD, alcoholism, drug abuse, or other mental health or addiction issues, we can help. \u00a0To schedule a complementary assessment or to find out more about our teletherapy programs, contact us at\u00a01-800-685-9796<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Artificial intelligence, or AI, is a game-changing innovation and has many capabilities. One of those is the ability to detect signs of anxiety and depression in the speech patterns of children. According to a new study published in the Journal of Biomedical and Health Informatics, a machine-learning algorithm could be an easy way to spot […]<\/p>\n","protected":false},"author":1,"featured_media":2997,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[10],"tags":[],"yoast_head":"\n