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Personalized Depression Treatment Traditional therapy and medication don't work for a majority of people suffering from depression. A customized treatment may be the solution. Cue is a digital intervention platform that transforms passively acquired smartphone sensor data into personalized micro-interventions to improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to discover their feature predictors and reveal distinct features that deterministically change mood over time. Predictors of Mood Depression is a leading cause of mental illness in the world.1 Yet, only half of those with the condition receive treatment. To improve outcomes, clinicians must be able identify and treat patients who are most likely to benefit from certain treatments. Personalized depression treatment is one way to do this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit the most from certain treatments. They make use of mobile phone sensors and a voice assistant incorporating artificial intelligence and other digital tools. Two grants worth more than $10 million will be used to discover biological and behavior factors that predict response. The majority of research done to date has focused on clinical and sociodemographic characteristics. These include demographic variables such as age, sex and education, clinical characteristics including symptom severity and comorbidities, and biological markers like neuroimaging and genetic variation. Few studies have used longitudinal data to predict mood in individuals. Few also take into account the fact that mood varies significantly between individuals. Therefore, it is essential to develop methods that allow for the determination of individual differences in mood predictors and treatment effects. 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. The team is able to develop algorithms to identify patterns of behaviour and emotions that are unique to each person. In addition to these modalities, the team created a machine learning algorithm that models the dynamic variables that influence each person's mood. The algorithm integrates the individual differences to create a unique “digital genotype” for each participant. This digital phenotype has been linked to CAT DI scores, a psychometrically validated symptom severity scale. The correlation was not strong however (Pearson r = 0,08; BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals. Predictors of symptoms Depression is the leading cause of disability in the world1, however, it is often misdiagnosed and untreated2. Depressive disorders are often not treated due to the stigma that surrounds them and the lack of effective treatments. To aid in the development of a personalized treatment, it is crucial to identify the factors that predict symptoms. However, the current methods for predicting symptoms rely on clinical interview, which is unreliable and only detects a small number of symptoms related to depression.2 Machine learning is used to integrate continuous digital behavioral phenotypes that are captured by smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory the CAT-DI) with other predictors of symptom severity could increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements and capture a wide variety of unique behaviors and activity patterns that are difficult to record using interviews. The study included University of California Los Angeles (UCLA) students who were suffering from moderate to severe depressive symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were referred to online support or to clinical treatment according to the severity of their depression. Those with a CAT-DI score of 35 65 were allocated online support with an online peer coach, whereas those with a score of 75 patients were referred to in-person clinical care for psychotherapy. At the beginning, participants answered an array of questions regarding their personal demographics and psychosocial features. These included sex, age, education, work, and financial situation; whether they were partnered, divorced, or single; current suicidal thoughts, intentions or attempts; and the frequency with which they drank alcohol. Participants also rated their degree of depression severity on a scale of 0-100 using the CAT-DI. The CAT DI assessment was carried out every two weeks for participants who received online support, and weekly for those who received in-person assistance. Predictors of Treatment Reaction The development of a personalized depression treatment is currently a major research area, and many studies aim at identifying predictors that will enable clinicians to determine the most effective medications for each patient. Pharmacogenetics in particular is a method of identifying genetic variations that affect how the body's metabolism reacts to drugs. This allows doctors select medications that will likely work best for each patient, while minimizing time and effort spent on trial-and error treatments and avoid any negative side effects. Another promising approach is to develop predictive models that incorporate the clinical data with neural imaging data. These models can then be used to identify the most effective combination of variables that are predictors of a specific outcome, such as whether or not a drug is likely to improve the mood and symptoms. These models can be used to determine the patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of treatment currently being administered. A new type of research uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables and improve the accuracy of predictive. depression treatment options have shown to be effective in the prediction of treatment outcomes like the response to antidepressants. These approaches are becoming more popular in psychiatry and could become the standard of future treatment. In addition to the ML-based prediction models research into the mechanisms that cause depression is continuing. Recent research suggests that the disorder is connected with neurodegeneration in particular circuits. This theory suggests that individual depression treatment will be built around targeted treatments that target these neural circuits to restore normal functioning. One way to do this is through internet-delivered interventions that can provide a more individualized and tailored experience for patients. A study showed that a web-based program improved symptoms and led to a better quality of life for MDD patients. A controlled study that was randomized to an individualized treatment for depression found that a substantial percentage of patients experienced sustained improvement as well as fewer side effects. Predictors of adverse effects In the treatment of depression the biggest challenge is predicting and determining which antidepressant medications will have minimal or zero side effects. Many patients experience a trial-and-error method, involving a variety of medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting way to select antidepressant medicines that are more effective and precise. A variety of predictors are available to determine which antidepressant to prescribe, including gene variations, phenotypes of patients (e.g., sex or ethnicity) and comorbidities. However it is difficult to determine the most reliable and valid predictive factors for a specific treatment will probably require randomized controlled trials with much larger samples than those normally enrolled in clinical trials. This is because it could be more difficult to detect interactions or moderators in trials that comprise only one episode per person instead of multiple episodes over time. Furthermore to that, predicting a patient's reaction will likely require information about the comorbidities, symptoms profiles and the patient's own perception of effectiveness and tolerability. At present, only a handful of easily assessable sociodemographic variables and clinical variables appear to be consistently associated with response to MDD. These include gender, age, race/ethnicity as well as BMI, SES and the presence of alexithymia. There are many challenges to overcome in the application of pharmacogenetics for depression treatment. First it is necessary to have a clear understanding of the genetic mechanisms is required, as is a clear definition of what constitutes a reliable predictor for treatment response. Ethics like privacy, and the responsible use genetic information should also be considered. Pharmacogenetics could, in the long run reduce stigma associated with mental health treatments and improve the quality of treatment. However, as with any other psychiatric treatment, careful consideration and implementation is essential. The best method is to provide patients with a variety of effective medications for depression and encourage them to speak openly with their doctors about their experiences and concerns.