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Personalized Depression Treatment

Traditional therapy and medication do not work for many people who are depressed. Personalized treatment may be the solution.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into customized micro-interventions that improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and reveal distinct features that are able to change mood with time.

Predictors of Mood

Depression is the leading cause of mental illness around the world.1 Yet, only half of those suffering from the condition receive treatment. To improve outcomes, healthcare professionals must be able to identify and treat patients with the highest chance of responding to particular treatments.

A customized depression treatment is one way to do this. By using sensors on mobile phones as well as an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to predict which patients will benefit from the treatments they receive. With two grants awarded totaling more than $10 million, they will make use of these techniques to determine biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.

The majority of research conducted to so far has focused on clinical and sociodemographic characteristics. These include demographics like gender, age and education and clinical characteristics like severity of symptom, comorbidities and biological markers.

Very few studies have used longitudinal data in order to predict mood of individuals. Few studies also take into consideration the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that allow for the identification and quantification of personal differences between mood predictors and treatment effects, for instance.

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 allows the team to develop algorithms that can identify different patterns of behavior and emotions that are different between people.

The team also created a machine-learning algorithm that can identify dynamic predictors of the mood of each person's depression. The algorithm blends these individual differences into a unique "digital phenotype" for each participant.

The digital phenotype was associated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. The correlation was low, however (Pearson r = 0,08; BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals.

Predictors of symptoms

Depression is among the leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated due to the stigma attached ways to treat depression them and the lack of effective treatments.

To allow for individualized treatment, identifying patterns that can predict symptoms is essential. However, the methods used to predict symptoms depend on the clinical interview which is unreliable and only detects a limited number of symptoms that are associated with depression.2

Using machine learning to combine continuous digital behavioral phenotypes that are captured through smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing depression treatment psychology Inventory, the CAT-DI) together with other predictors of symptom severity has the potential to improve diagnostic accuracy and increase treatment efficacy for depression. Digital phenotypes can be used to capture a large number of distinct actions and behaviors that are difficult to capture through interviews, and allow for continuous and high-resolution measurements.

The study included University of California Los Angeles (UCLA) students experiencing moderate to severe depressive symptoms. enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical treatment in accordance with their severity of depression. Participants who scored a high on the CAT-DI of 35 or 65 students were assigned online support via the help of a coach. Those with scores of 75 were sent to in-person clinics for psychotherapy.

At the beginning, participants answered a series of questions about their personal characteristics and psychosocial traits. The questions included age, sex, and education and marital status, financial status and whether they were divorced or not, their current suicidal thoughts, intent or attempts, as well as how often they drank. The CAT-DI was used for assessing the severity of depression symptoms on a scale ranging from 0-100. The CAT-DI test was performed every two weeks for participants who received online support and weekly for those who received in-person support.

Predictors of the Reaction to Treatment

A customized treatment for depression is currently a research priority and many studies aim at identifying predictors that allow clinicians to identify the most effective medication for each patient. In particular, pharmacogenetics identifies genetic variants that determine how the body's metabolism reacts to antidepressants. This allows doctors to select medications that are likely to be most effective for each patient, while minimizing the time and effort required in trials and errors, while eliminating any side effects that could otherwise slow the progress of the patient.

Another approach that is promising is to build prediction models that combine the clinical data with neural imaging data. These models can then be used to identify the best combination of variables predictors of a specific outcome, like whether or not a particular medication will improve symptoms and mood. These models can be used to predict the patient's response to treatment, allowing doctors to maximize the effectiveness of their treatment.

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

The study of depression's underlying mechanisms continues, as do predictive models based on ML. Recent research suggests that depression is related to the dysfunctions of specific neural networks. This theory suggests that a individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.

Internet-delivered interventions can be an effective method to achieve this. They can provide more customized and personalized experience for patients. One study found that an internet-based program helped improve symptoms and led to 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 proportion of participants.

Predictors of adverse effects

A major issue in personalizing depression treatment involves identifying and predicting the antidepressant medications that will have the least amount of side effects or none at all. Many patients are prescribed a variety drugs before they find a non drug treatment for anxiety and depression that is effective and tolerated. Pharmacogenetics provides a novel and exciting way to select antidepressant medicines that are more efficient and targeted.

Many predictors can be used to determine which antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g., sex or ethnicity) and comorbidities. To determine the most reliable and valid predictors for a specific treatment, controlled trials that are randomized with larger sample sizes will be required. This is because it could be more difficult to identify moderators or interactions in trials that comprise only a single episode per person instead of multiple episodes spread over time.

Additionally the prediction of a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's personal experience of tolerability and effectiveness. Presently, only a handful of easily measurable sociodemographic and clinical variables seem to be reliable in predicting response to MDD factors, including age, gender race/ethnicity, SES BMI and the presence of alexithymia, and the severity of depressive symptoms.

There are many challenges to overcome in the use of pharmacogenetics for depression treatment. First is a thorough understanding of the genetic mechanisms is essential, as is a clear definition of what is a reliable indicator of treatment response. Ethics like privacy, and the ethical use of genetic information are also important to consider. The use of pharmacogenetics may, in the long run, reduce stigma surrounding mental health natural treatment depression anxiety, Click In this article, and improve the quality of treatment. But, like all approaches to psychiatry, careful consideration and application is essential. In the moment, it's recommended to provide patients with an array of depression medications that work and encourage patients to openly talk with their physicians.