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Personalized Depression Treatment For many suffering from depression, traditional therapies and medication are ineffective. The individual approach to treatment could be the answer. Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalised micro-interventions to improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to discover their predictors of feature and reveal distinct features that deterministically change mood with time. Predictors of Mood Depression is a major cause of mental illness around the world.1 Yet only half of those suffering from the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat patients who are the most likely to respond to certain treatments. Personalized depression treatment can help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will gain the most from specific treatments. They make use of sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavioral predictors of response. To date, the majority of research into predictors of depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographics such as age, gender, and education, and clinical characteristics like severity of symptom and comorbidities, as well as biological markers. While many of these variables can be predicted by the information available in medical records, very few studies have employed longitudinal data to study predictors of mood in individuals. Few also take into account the fact that moods vary significantly between individuals. Therefore, it is critical to develop methods that allow for the identification of individual differences in mood predictors and the effects of treatment. The team's new approach uses daily, in-person evaluations of mood and lifestyle variables using a smartphone app called AWARE, a cognitive evaluation with the BiAffect app and electroencephalography — an imaging technique that monitors brain activity. This enables the team to develop algorithms that can identify different patterns of behavior and emotions that differ between individuals. In addition to these modalities, the team developed a machine-learning algorithm to model the changing variables that influence each person's mood. The algorithm combines these individual differences into a unique “digital phenotype” for each participant. This digital phenotype was found to be associated with CAT DI scores, a psychometrically validated severity scale for symptom severity. However the correlation was tinny (Pearson's r = 0.08, BH-adjusted P-value of 3.55 1003) and varied widely across individuals. Predictors of Symptoms Depression is among the most prevalent causes of disability1 but is often untreated and not diagnosed. Depressive disorders are often not treated due to the stigma attached to them, as well as the lack of effective treatments. To allow for individualized treatment, identifying factors that predict the severity of symptoms is crucial. The current methods for predicting symptoms rely heavily on clinical interviews, which are unreliable and only detect a few characteristics that are associated with depression. Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral patterns gathered from sensors on smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes capture a large number of unique behaviors and activities that are difficult to document through interviews and permit continuous and high-resolution measurements. The study involved University of California Los Angeles students with moderate to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online assistance or in-person clinics depending on their depression severity. Patients with a CAT DI score of 35 65 students were assigned online support with an instructor and those with scores of 75 were sent to in-person clinical care for psychotherapy. At preventive measures for depression , participants provided the answers to a series of questions concerning their personal demographics and psychosocial characteristics. The questions asked included age, sex, and education and financial status, marital status, whether they were divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. The CAT-DI was used to rate the severity of depression symptoms on a scale ranging from 0-100. The CAT-DI assessment was performed every two weeks for participants who received online support, and weekly for those who received in-person support. Predictors of Treatment Response The development of a personalized depression treatment is currently a research priority and many studies aim at identifying predictors that will help clinicians determine the most effective medication for each patient. Pharmacogenetics, for instance, uncovers genetic variations that affect how the human body metabolizes drugs. This lets doctors choose the medications that are most likely to work for each patient, reducing the time and effort needed for trials and errors, while avoid any negative side negative effects. Another option is to create prediction models that combine clinical data and neural imaging data. These models can then be used to determine the best combination of variables that are predictors of a specific outcome, such as whether or not a medication is likely to improve mood and symptoms. These models can be used to determine the patient's response to treatment, allowing doctors maximize the effectiveness. A new generation uses machine learning techniques like algorithms for classification and supervised learning such as regularized logistic regression, and tree-based methods to combine the effects of several variables and improve predictive accuracy. These models have proven to be useful in the prediction of treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry and it is expected that they will become the norm for future clinical practice. In addition to ML-based prediction models The study of the mechanisms that cause depression continues. Recent research suggests that depression is related to the dysfunctions of specific neural networks. This suggests that individualized depression treatment will be built around targeted therapies that target these circuits to restore normal function. One method of doing this is to use internet-based interventions that offer a more personalized and customized experience for patients. One study found that a web-based program improved symptoms and provided a better quality life for MDD patients. Furthermore, a randomized controlled study of a customized treatment for depression demonstrated an improvement in symptoms and fewer adverse effects in a significant number of participants. Predictors of Side Effects A major obstacle in individualized depression treatment is predicting which antidepressant medications will cause very little or no side effects. Many patients experience a trial-and-error approach, using various medications prescribed until they find one that is effective and tolerable. Pharmacogenetics offers a new and exciting method to choose antidepressant medicines that are more effective and specific. There are many variables that can be used to determine the antidepressant to be prescribed, such as gene variations, patient phenotypes like gender or ethnicity and the presence of comorbidities. However, identifying the most reliable and reliable factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with considerably larger samples than those typically enrolled in clinical trials. This is due to the fact that the identification of moderators or interaction effects can be a lot more difficult in trials that only take into account a single episode of treatment per patient, rather than multiple episodes of treatment over time. Furthermore, the estimation of a patient's response to a particular medication will also likely require information on comorbidities and symptom profiles, and the patient's previous experiences with the effectiveness and tolerability of the medication. Presently, only a handful of easily measurable sociodemographic and clinical variables are believed to be reliable in predicting response to MDD factors, including age, gender race/ethnicity BMI and the presence of alexithymia and the severity of depressive symptoms. The application of pharmacogenetics in treatment for depression is in its infancy and there are many obstacles to overcome. First is a thorough understanding of the underlying genetic mechanisms is required as well as a clear definition of what is a reliable indicator of treatment response. Ethics, such as privacy, and the responsible use genetic information must also be considered. In the long run pharmacogenetics can provide an opportunity to reduce the stigma that surrounds mental health care and improve the outcomes of those suffering with depression. As with all psychiatric approaches it is crucial to take your time and carefully implement the plan. At present, it's recommended to provide patients with various depression medications that are effective and encourage patients to openly talk with their physicians.