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Personalized Depression Treatment

Traditional therapies and medications are not effective for a lot of patients suffering from depression. Personalized treatment may be the solution.

Cue is an intervention platform that converts passively acquired sensor data from smartphones into personalised micro-interventions for improving mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and reveal distinct characteristics that can be used to predict changes in mood as time passes.

Predictors of Mood

Depression is the leading cause of mental depression treatment illness in the world.1 Yet the majority of people with the condition receive non drug treatment for Depression. To improve the outcomes, doctors must be able to identify and treat patients who are most likely to respond to specific treatments.

The ability to tailor depression treatments is one method to achieve this. By using mobile phone sensors and an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from the treatments they receive. Two grants totaling more than $10 million will be used to determine biological and behavioral predictors of response.

The majority of research conducted to date has focused on sociodemographic and clinical characteristics. These include factors that affect the demographics like age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.

A few studies have utilized longitudinal data in order to predict mood in individuals. Few also take into account the fact that mood varies significantly between individuals. It is therefore important to develop methods which allow for the determination and quantification of the individual differences in mood predictors, treatment effects, etc.

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 various patterns of behavior and emotion that differ between individuals.

In addition to these modalities, the team also developed a machine-learning algorithm that models the dynamic predictors of each person's depressed mood. The algorithm combines these individual differences into a unique "digital phenotype" for each participant.

This digital phenotype was correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was low, however (Pearson r = 0,08, P-value adjusted for BH = 3.55 x 10 03) and varied widely between individuals.

Predictors of symptoms

Depression is a leading cause of disability in the world1, but it is often not properly diagnosed and treated. In addition the absence of effective interventions and stigma associated with depressive disorders stop many from seeking treatment.

To allow for individualized treatment in order to provide a more personalized treatment, identifying factors that predict the severity of symptoms is crucial. The current prediction methods rely heavily on clinical interviews, which are not reliable and only reveal a few features associated with depression.

Using machine learning to combine continuous digital behavioral phenotypes that are captured by sensors on smartphones and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory the CAT-DI) with other predictors of symptom severity has the potential to improve the accuracy of diagnosis and the effectiveness of treatment for depression. Digital phenotypes permit continuous, high-resolution measurements and capture a wide range of unique behaviors and activity patterns that are difficult to capture with interviews.

The study included University of California Los Angeles students who had mild 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 referred to online support or to clinical treatment depending on the severity of their depression. Patients who scored high on the CAT-DI of 35 65 were assigned online support with the help of a coach. Those with a score 75 patients were referred to in-person clinical care for psychotherapy.

At baseline, participants provided a series of questions about their personal demographics and psychosocial characteristics. These included sex, age education, work, and financial status; if they were divorced, married or single; the frequency of suicidal thoughts, intentions or attempts; and the frequency with that they consumed alcohol. Participants also scored their level of depression symptom severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted each other week for participants that received online support, and once a week for those receiving in-person care.

Predictors of Treatment Response

Research is focusing on personalization of treatment for depression. Many studies are aimed at finding predictors, which can aid clinicians in identifying the most effective drugs to treat each patient. In particular, pharmacogenetics identifies genetic variants that influence the way that the body processes antidepressants. This lets doctors choose the medications that are most likely to work for each patient, while minimizing time and effort spent on trials and errors, while avoid any negative side effects.

Another promising approach is building prediction models using multiple data sources, such as data from clinical studies and neural imaging data. These models can then be used to determine the most effective combination of variables that are predictive of a particular outcome, like whether or not a medication is likely to improve mood and symptoms. These models can be used to determine the patient's response to a treatment, allowing doctors maximize the effectiveness.

A new generation employs machine learning techniques such as the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects from multiple variables to improve the accuracy of predictive. These models have been proven to be useful in predicting outcomes of treatment like the response to antidepressants. These methods are becoming popular in psychiatry and it is likely that they will become the norm for the future of clinical practice.

In addition to ML-based prediction models research into the underlying mechanisms of how depression is treated continues. Recent research suggests that the disorder is linked with neural dysfunctions that affect specific circuits. This theory suggests that individualized depression treatment will be built around targeted therapies that target these neural circuits to restore normal function.

One method of doing this is through internet-delivered interventions which can offer an individualized and personalized experience for patients. One study found that an internet-based program helped improve symptoms and led to a better quality of life for MDD patients. A randomized controlled study of an individualized treatment for depression showed that a significant percentage of participants experienced sustained improvement and had fewer adverse negative effects.

Predictors of side effects

In the treatment of depression a major challenge is predicting and determining which antidepressant medication will have very little or no negative side effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics offers a new and exciting method of selecting antidepressant drugs that are more efficient and targeted.

There are several predictors that can be used to determine the antidepressant to be prescribed, such as gene variations, phenotypes of the patient such as gender or ethnicity and the presence of comorbidities. However finding the most reliable and valid predictive factors for a specific treatment is likely to require controlled, randomized trials with considerably larger samples than those normally enrolled in clinical trials. This is because it could be more difficult to identify the effects of moderators or interactions in trials that comprise only one episode per participant instead of multiple episodes spread over a long period of time.

Furthermore, the prediction of a patient's response to a specific medication is likely to require information about symptoms and comorbidities and the patient's previous experience with tolerability and efficacy. At present, only a handful of easily identifiable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include gender, age, race/ethnicity as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics to depression electromagnetic treatment for depression is still in its beginning stages and there are many obstacles to overcome. First, it is important to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, and an understanding of an accurate indicator of the response to treatment. In addition, ethical concerns, such as privacy and the appropriate use of personal genetic information, should be considered with care. Pharmacogenetics can eventually reduce stigma associated with mental health treatments and improve treatment outcomes. As with any psychiatric approach, it is important to take your time and carefully implement the plan. At present, it's best to offer patients an array of depression medications that are effective and urge them to speak openly with their doctor.