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Personalized Depression Treatment For many suffering from depression, traditional therapies and medications are not effective. A customized treatment may be the answer. Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions designed to improve mental health. We analyzed the best-fitting personalized ML models to each subject, using Shapley values to determine their feature predictors. The results revealed distinct characteristics that deterministically changed mood over time. Predictors of Mood Depression is among the leading causes of mental illness.1 However, only half of those who have the disorder receive treatment1. To improve outcomes, clinicians need to be able to identify and treat patients who have the highest chance of responding to particular treatments. The ability to tailor depression treatments is one way to do this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from specific treatments. They make use of mobile phone sensors as well as a voice assistant that incorporates artificial intelligence and other digital tools. With two grants totaling more than $10 million, they will employ these technologies to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy. The majority of research to so far has focused on sociodemographic and clinical characteristics. These include factors that affect the demographics such as age, gender and education, clinical characteristics including the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation. Few studies have used longitudinal data to determine mood among individuals. A few studies also consider the fact that mood can vary significantly between individuals. Therefore, it is essential to develop methods that allow for the determination of the 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 create algorithms that can detect different patterns of behavior and emotion that differ between individuals. The team also devised a machine learning algorithm to model dynamic predictors for each person's depression mood. The algorithm combines these personal differences into a unique “digital phenotype” for each participant. This digital phenotype has been associated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, BH-adjusted P-value of 3.55 1003) and varied widely among individuals. Predictors of symptoms Depression is among the most prevalent causes of disability1, but it is often underdiagnosed and undertreated2. Depressive disorders are often not treated due to the stigma attached to them and the absence of effective treatments. To allow for individualized treatment to improve treatment, identifying the patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which aren't reliable and only reveal a few features associated with depression. Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavior patterns gathered from sensors on smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes provide a wide range of unique actions and behaviors that are difficult to capture through interviews, and allow for high-resolution, continuous measurements. The study included University of California Los Angeles students with moderate to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment depending on their depression severity. Those with a CAT-DI score of 35 or 65 were given online support via an instructor and those with a score 75 were sent to in-person clinics for psychotherapy. At the beginning of the interview, participants were asked a series of questions about their personal demographics and psychosocial characteristics. These included sex, age and education, as well as work and financial situation; whether they were divorced, married or single; their current suicidal ideation, intent, or attempts; and the frequency with which they drank alcohol. Participants also rated their level of depression symptom severity on a 0-100 scale using the CAT-DI. The CAT-DI test was conducted every two weeks for those who received online support and weekly for those who received in-person assistance. Predictors of Treatment Response Research is focused on individualized treatment for depression. Many studies are aimed at identifying predictors, which will help clinicians identify the most effective drugs to treat each patient. In particular, pharmacogenetics identifies genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors to select medications that are likely to work best for each patient, while minimizing the time and effort involved in trial-and-error procedures and avoiding side effects that might otherwise slow the progress of the patient. Another option is to develop predictive models that incorporate the clinical data with neural imaging data. These models can be used to determine the variables that are most predictive of a specific outcome, like whether a medication can help with symptoms or mood. These models can be used to determine the response of a patient to treatment, allowing doctors to maximize the effectiveness. A new era of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be effective in predicting outcomes of treatment for example, the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to be the norm in future treatment. In addition to prediction models based on ML The study of the mechanisms behind depression continues. Recent findings suggest that depression is related to dysfunctions in specific neural networks. This suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits. One method of doing this is by using internet-based programs which can offer an personalized and customized experience for patients. For example, one study discovered that a web-based treatment was more effective than standard care in improving symptoms and providing the best quality of life for people with MDD. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed sustained improvement and reduced side effects in a significant proportion of participants. Predictors of side effects A major issue in personalizing depression treatment is predicting which antidepressant medications will cause very little or no side effects. Many patients are prescribed a variety medications before settling on a treatment that is effective and tolerated. Pharmacogenetics provides a novel and exciting method of selecting antidepressant drugs that are more effective and specific. Several predictors may be used to determine which antidepressant to prescribe, including gene variants, patient phenotypes (e.g. sexual orientation, gender or ethnicity) and co-morbidities. However finding the most reliable and accurate predictive factors for a specific treatment will probably require controlled, randomized trials with significantly larger numbers of participants than those that are typically part of clinical trials. This is because the identifying of moderators or interaction effects can be a lot more difficult in trials that only take into account a single episode of treatment per participant, rather than multiple episodes of treatment over time. Furthermore the prediction of a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's personal experience of tolerability and effectiveness. Presently, only a handful of easily identifiable sociodemographic and clinical variables seem to be correlated with the response to MDD, such as age, gender race/ethnicity BMI, the presence of alexithymia and the severity of depressive symptoms. Many challenges remain in the use of pharmacogenetics to treat depression. First, depression treatment techniques of the genetic mechanisms is required as well as an understanding of what constitutes a reliable predictor for treatment response. In addition, ethical concerns, such as privacy and the ethical use of personal genetic information must be carefully considered. In the long term the use of pharmacogenetics could provide an opportunity to reduce the stigma that surrounds mental health treatment and improve the treatment outcomes for patients with depression. However, as with any other psychiatric treatment, careful consideration and application is necessary. The best option is to provide patients with an array of effective depression medication options and encourage them to speak freely with their doctors about their experiences and concerns.