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Personalized Depression Treatment

For many suffering from depression, traditional therapies and medication are ineffective. Personalized treatment may be the solution.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into personalized micro-interventions for improving mental health. We examined the most effective-fitting personalized ML models to each person, using Shapley values to determine their characteristic predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 Yet, only half of those suffering from the disorder receive treatment1. To improve outcomes, doctors must be able to recognize and treat patients who have the highest chance of responding to certain treatments.

Personalized depression treatment can help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most from specific treatments. They are using mobile phone sensors and a voice assistant incorporating artificial intelligence and other digital tools. With two grants awarded totaling more than $10 million, they will employ these technologies to identify the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.

To date, the majority of research on factors that predict depression treatment effectiveness; clashofcryptos.trade, has focused on the sociodemographic and clinical aspects. These include demographics like gender, age and education, and clinical characteristics like symptom severity and comorbidities as well as biological markers.

A few studies have utilized longitudinal data in order to predict mood in individuals. Few studies also take into account the fact that mood can differ significantly between individuals. Therefore, it is critical to develop methods that permit the identification of different mood predictors for each person 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. This enables the team to create algorithms that can detect distinct patterns of behavior and emotions that are different between people.

In addition to these modalities the team also developed a machine-learning algorithm that models the dynamic variables that influence each person's mood. The algorithm blends these individual variations into a distinct "digital phenotype" for each participant.

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

Predictors of symptoms

Depression is the most common cause of disability around the world1, but it is often not properly diagnosed and treated. Depressive disorders are often not treated due to the stigma that surrounds them and the absence of effective treatments.

To aid in the development of a personalized treatment, it is essential to identify the factors that predict symptoms. However, the current methods for predicting symptoms rely on clinical interview, which has poor reliability and only detects a small variety of characteristics related to depression.2

Using machine learning to integrate continuous digital behavioral phenotypes captured through smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of severity of symptoms has the potential to improve diagnostic accuracy and increase the effectiveness of home treatment for depression for depression. These digital phenotypes allow continuous, high-resolution measurements and capture a wide range of distinct behaviors and patterns that are difficult to record with interviews.

The study involved University of California Los Angeles students who had mild depression treatment to severe depression symptoms who were enrolled 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 in-person clinical treatment according to the severity of their depression. Patients with a CAT DI score of 35 or 65 were given online support via a coach and those living with treatment resistant depression scores of 75 were routed to in-person clinical care for psychotherapy.

Participants were asked a set of questions at the beginning of the study concerning their demographics and psychosocial characteristics. The questions covered education, age, sex and gender and marital status, financial status and whether they were divorced or not, the frequency of suicidal thoughts, intent or attempts, and how often they drank. The CAT-DI was used for assessing the severity of depression symptoms on a scale ranging from zero to 100. The CAT DI assessment was carried out every two weeks for those who received online support and weekly for those who received in-person support.

Predictors of Treatment Response

The development of a personalized depression treatment is currently a research priority and many studies aim to identify predictors that allow clinicians to identify the most effective medication for each person. Particularly, pharmacogenetics is able to identify genetic variants that determine how depression is treated the body metabolizes antidepressants. This allows doctors to select medications that are likely to work best for each patient, minimizing the time and effort required in trial-and-error treatments and avoiding side effects that might otherwise slow advancement.

Another promising method is to construct models of prediction using a variety of data sources, combining the clinical information with neural imaging data. These models can then be used to identify the most effective combination of variables that are predictive of a particular outcome, such as whether or not a drug will improve mood and symptoms. These models can be used to determine the response of a patient to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of the current treatment.

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. These models have shown to be useful for predicting treatment outcomes such as the response to antidepressants. These approaches are gaining popularity in psychiatry and it is expected that they will become the standard for the future of clinical practice.

In addition to ML-based prediction models research into the mechanisms behind depression continues. Recent research suggests that the disorder is connected with dysfunctions in specific neural circuits. This suggests that an individualized treatment for depression will depend on targeted therapies that restore normal function to these circuits.

Internet-based interventions are an effective method to achieve this. They can offer more customized and personalized experience for patients. One study found that an internet-based program improved symptoms and provided a better quality of life for MDD patients. Additionally, a randomized controlled study of a personalised approach to depression treatment showed an improvement in symptoms and fewer adverse effects in a large proportion of participants.

Predictors of side effects

A major issue in personalizing depression treatment is predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed a variety medications before finding a medication that is safe and effective. Pharmacogenetics offers a new and exciting way to select antidepressant drugs that are more effective and specific.

Many predictors can be used to determine which antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g. gender, sex or ethnicity) and the presence of comorbidities. However, identifying the most reliable and reliable factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with considerably larger samples than those that are typically part of clinical trials. This is due to the fact that the identification of moderators or interaction effects could be more difficult in trials that consider a single episode of treatment per patient instead of multiple sessions of treatment over time.

In addition the prediction of a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's subjective perception of the effectiveness and tolerability. At present, only a few easily measurable sociodemographic and clinical variables appear to be reliably associated with the response to MDD factors, including age, gender, race/ethnicity and SES BMI and the presence of alexithymia, and the severity of depressive symptoms.

Many issues remain to be resolved in the application of pharmacogenetics in the treatment of depression. It is crucial to be able to comprehend and understand the definition of the genetic factors 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, must be considered carefully. The use of pharmacogenetics may, in the long run help reduce stigma around mental health treatment and improve the quality of treatment. Like any other psychiatric treatment it is essential to take your time and carefully implement the plan. At present, the most effective course of action is to offer patients a variety of effective depression medications and encourage them to talk with their physicians about their experiences and concerns.