Personalized Depression Treatment

Traditional treatment and medications don't work for a majority of patients suffering from depression. Personalized treatment may be the solution.

Cue is an intervention platform that transforms sensor data collected from smartphones into personalised micro-interventions to improve mental health. We looked at the best-fitting personal ML models to each subject using Shapley values to discover their feature predictors. This revealed distinct features that were deterministically changing mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 Yet, only half of people suffering from the disorder receive treatment1. To improve the outcomes, doctors must be able to recognize and treat patients with the highest probability of responding to certain treatments.

A customized Situational Depression Treatment treatment plan can aid. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will gain the most from specific treatments. They use 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 tools to identify the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

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

While many of these factors can be predicted from the data in medical records, only a few studies have used longitudinal data to study the factors that influence mood in people. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to create methods that allow the determination of individual differences in mood predictors 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 develop algorithms that can detect distinct patterns of behavior and emotion that differ between individuals.

The team also created a machine learning algorithm to model dynamic predictors for each person's mood for depression. The algorithm integrates the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype has been linked to CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was not strong however (Pearson r = 0,08; P-value adjusted by BH 3.55 x 10 03) and varied widely between individuals.

Predictors of symptoms

Depression is one of the leading causes of disability1 but is often underdiagnosed and undertreated2. In addition an absence of effective treatments and stigma associated with depressive disorders stop many people from seeking help.

To allow for individualized treatment to improve treatment, identifying the predictors of symptoms is important. Current prediction methods rely heavily on clinical interviews, which aren't reliable and only reveal a few characteristics that are associated with bipolar depression treatment.

Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes provide a wide range of distinct actions and behaviors that are difficult to capture through interviews, and also allow for high-resolution, continuous measurements.

The study enrolled University of California Los Angeles (UCLA) students with mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment depending on their depression severity. Participants who scored a high on the CAT-DI scale of 35 or 65 were assigned online support with an online peer coach, whereas those with a score of 75 patients were referred for psychotherapy in-person.

At the beginning, participants answered a series of questions about their personal demographics and psychosocial features. These included sex, age, education, work, and financial status; whether they were divorced, partnered, or single; current suicidal ideas, intent or attempts; and the frequency with the frequency they consumed alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale ranging from zero to 100. The CAT-DI test was carried out every two weeks for participants who received online support, and weekly for those who received in-person care.

Predictors of Treatment Reaction

Research is focusing on personalized treatment for depression. Many studies are focused on identifying predictors, which will help doctors determine the most effective drugs to treat each individual. Particularly, pharmacogenetics can identify genetic variants that determine how the body's metabolism reacts to antidepressants. This enables doctors to choose medications that are likely to work best for each patient, minimizing the time and effort involved in trial-and-error procedures and avoid any adverse effects that could otherwise hinder advancement.

Another promising approach is to develop prediction models combining the clinical data with neural imaging data. These models can be used to determine the variables that are most predictive of a particular outcome, like whether a medication will 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 type of research utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables to improve predictive accuracy. These models have been shown to be useful in predicting treatment outcomes for example, the response to antidepressants. These methods are becoming more popular in psychiatry, and are likely to be the norm in future clinical practice.

In addition to prediction models based on ML, research into the underlying mechanisms of depression is continuing. Recent research suggests that recurrent depression treatment is connected to the malfunctions of certain neural networks. This theory suggests that a individualized treatment for depression will be based on targeted therapies that restore normal function to these circuits.

One method to achieve this is through internet-delivered interventions that can provide a more personalized and customized experience for patients. A study showed that an internet-based program improved symptoms and improved quality of life for MDD patients. A controlled study that was randomized to an individualized treatment for depression found that a significant number of participants experienced sustained improvement as well as fewer side effects.

Predictors of adverse effects

A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will have very little or no side effects. Many patients are prescribed a variety of medications before settling on a treatment that is effective and tolerated. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and precise.

Several predictors may be used to determine the best antidepressant to prescribe, including genetic variants, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. However it is difficult to determine the most reliable and valid factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials of much 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 take into account a single episode of non pharmacological treatment for depression per patient instead of multiple sessions of treatment over a period of time.

Furthermore the prediction of a patient's reaction to a specific medication will likely also require information on symptoms and comorbidities in addition to the patient's personal experiences with the effectiveness and tolerability of the medication. Presently, only a handful of easily assessable sociodemographic and clinical variables seem to be reliably associated with the response to MDD like age, gender 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 for depression treatment. First, it is essential to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, as well as a clear definition of an accurate predictor of treatment response. In addition, ethical concerns like privacy and the appropriate use of personal genetic information must be considered carefully. In the long-term, pharmacogenetics may offer a chance to lessen the stigma that surrounds mental health care and improve treatment outcomes for those struggling with depression. As with all psychiatric approaches it is crucial to carefully consider and implement the plan. For now, the best method is to offer patients an array of effective depression medications and encourage them to talk freely with their doctors about their experiences and concerns.