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Personalized Depression Treatment

Traditional therapy and medication don't work for a majority of people suffering from depression. The individual approach to treatment could be the solution.

Cue is an intervention platform that converts sensors that are passively gathered 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 understand their feature predictors and reveal distinct features that are able to change mood over time.

Predictors of Mood

Depression is the leading cause of mental illness around the world.1 Yet, only half of those affected receive treatment. To improve the outcomes, doctors must be able to identify and treat patients who are the most likely to respond to specific treatments.

Personalized postpartum depression treatment treatment can help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most effective treatment for depression from certain treatments. They use sensors for mobile phones, a voice assistant with artificial intelligence, and other digital tools. Two grants totaling more than $10 million will be used to determine biological and behavior factors that predict response.

The majority of research to the present has been focused on clinical and sociodemographic characteristics. These include demographics like gender, age and education, as well as clinical aspects like severity of symptom, comorbidities and biological markers.

Very 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 crucial to develop methods that allow for the identification of the individual differences in mood predictors and treatments 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 can then develop algorithms to detect patterns of behaviour and emotions that are unique to each individual.

In addition to these modalities, the team developed a machine-learning algorithm to model the dynamic predictors of each person's depressed mood. The algorithm blends these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, which is a psychometrically validated scale for assessing severity of symptom. However, the correlation was weak (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

postpartum depression treatment is among the leading causes of disability1, but it is often underdiagnosed and undertreated2. In addition an absence of effective interventions and stigma associated with depressive disorders prevent many people from seeking help.

To allow for individualized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. The current prediction methods rely heavily on clinical interviews, which are not reliable and only identify a handful of characteristics that are associated with depression.

Using machine learning to blend continuous digital behavioral phenotypes captured by sensors on smartphones and an online mental health tracker (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. These digital phenotypes allow continuous, high-resolution measurements as well as capture a variety of distinct behaviors and patterns that are difficult to record through interviews.

The study comprised University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or clinical care according to the degree of their depression. Patients with a CAT DI score of 35 or 65 were assigned online support by a coach and those with scores of 75 patients were referred to in-person clinical care for psychotherapy.

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial traits. These included age, sex education, work, and financial status; if they were divorced, partnered or single; the frequency of suicidal ideation, intent or attempts; and the frequency with that they consumed alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale from zero to 100. The CAT-DI tests were conducted every other week for participants that received online support, and every week for those who received in-person care.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic and many studies aim at identifying predictors that help clinicians determine the most effective medications for each patient. In particular, pharmacogenetics identifies genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors to select drugs that are likely to work best for each patient, while minimizing the time and effort in trials and errors, while avoiding side effects that might otherwise hinder advancement.

Another approach that is promising is to build models of prediction using a variety of data sources, combining data from clinical studies and neural imaging data. These models can be used to identify the best combination of variables that are predictors of a specific outcome, like whether or not a particular medication is likely to improve symptoms and mood. These models can be used to determine a patient's response to treatment that is already in place which allows doctors to maximize the effectiveness of the current therapy.

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

Research into the underlying causes of depression continues, as do predictive models based on ML. Recent research suggests that depression is related to dysfunctions in specific neural networks. This suggests that an individualized depression treatment will be built around targeted therapies that target these neural circuits to restore normal functioning.

Internet-based-based therapies can be an effective method to accomplish this. They can provide an individualized and tailored experience for patients. One study found that an internet-based program improved symptoms and improved quality of life for MDD patients. Additionally, a randomized controlled trial of a personalized approach to depression treatment showed sustained improvement and reduced adverse effects in a significant proportion of participants.

Predictors of adverse effects

In the treatment of depression, the biggest challenge is predicting and determining the antidepressant that will cause minimal or zero side effects. Many patients take a trial-and-error approach, with a variety of medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting method to choose antidepressant medicines that are more efficient and targeted.

There are many predictors that can be used to determine which antidepressant should be prescribed, including gene variations, phenotypes of the patient such as gender or ethnicity, and the presence of comorbidities. To identify the most reliable and reliable predictors for a specific treatment, random controlled trials with larger samples will be required. This is due to the fact that it can be more difficult to determine moderators or interactions in trials that only include one episode per participant rather than multiple episodes over time.

Furthermore, the prediction of a patient's response to a specific medication is likely to require information on symptoms and comorbidities as well as the patient's previous experience of its tolerability and effectiveness. At present, only a few easily assessable sociodemographic and clinical variables appear to be reliably associated with the severity of MDD, such as gender, age race/ethnicity, SES BMI, the presence of alexithymia and the severity of depressive symptoms.

There are many challenges to overcome in the use of pharmacogenetics to Treat Depression; Chessdatabase.Science,. First is a thorough understanding of the genetic mechanisms is needed, as is a clear definition of what is a reliable predictor of treatment response. Additionally, ethical issues such as privacy and the ethical use of personal genetic information, must be carefully considered. In the long run the use of pharmacogenetics could provide an opportunity to reduce the stigma that surrounds mental health care and improve treatment outcomes for those struggling with depression. As with any psychiatric approach it is essential to carefully consider and implement the plan. At present, the most effective method is to offer patients a variety of effective depression medications and encourage them to talk with their physicians about their experiences and concerns.