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Personalized Depression Treatment

For many suffering from depression, traditional therapy and medication isn't effective. Personalized treatment could be the solution.

Cue is a digital intervention platform that transforms passively acquired sensor data from smartphones into personalised micro-interventions to improve mental health. We examined the most effective-fitting personalized ML models to each subject using Shapley values to discover 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 However, only half of people suffering from the disorder receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients who are most likely to respond to certain treatments.

The ability to tailor depression treatments is one way to do this. By using sensors on mobile phones, an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to predict which patients will benefit from which treatments. With two grants totaling more than $10 million, they will employ these technologies to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

So far, the majority of research on predictors for depression treatment effectiveness has been focused on sociodemographic and clinical characteristics. These include demographics like gender, age, and education, as well as clinical characteristics such as symptom severity, comorbidities and biological markers.

While many of these variables can be predicted from the data in medical records, few studies have utilized longitudinal data to explore predictors of mood in individuals. Many studies do not take into consideration the fact that mood varies significantly between individuals. Therefore, it is crucial to create methods that allow the recognition of different mood predictors for each person and the effects of treatment.

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 various patterns of behavior and emotions that vary between individuals.

The team also developed a machine learning algorithm to model dynamic predictors for each person's depression mood. The algorithm combines these individual characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype has been linked to CAT DI scores which is a psychometrically validated symptom severity scale. The correlation was low, however (Pearson r = 0,08; P-value adjusted by BH 3.55 10 03) and varied widely between individuals.

Predictors of symptoms

Depression is the leading 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 associated with them and the absence of effective interventions.

To allow for individualized treatment options for depression, identifying factors that predict the severity of symptoms is crucial. However, current prediction methods are based on the clinical interview, which is unreliable and only detects a limited number of symptoms related to depression.2

Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes gathered from smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing depression treatment food Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements. They also capture a wide range of distinct behaviors and patterns that are difficult to document using interviews.

The study included University of California Los Angeles (UCLA) students experiencing mild depression treatments to severe depression symptoms. who were enrolled 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 to clinical treatment depending on the severity of their depression. Patients who scored high on the CAT DI of 35 65 students were assigned online support with an instructor and those with a score 75 were sent to clinics in-person for psychotherapy.

At baseline, participants provided a series of questions about their personal characteristics and psychosocial traits. The questions covered education, age, sex and gender as well as marital status, financial status as well as whether they divorced or not, current 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 0-100. CAT-DI assessments were conducted every week for those that received online support, and weekly for those receiving in-person support.

Predictors of Treatment Reaction

Research is focusing on personalized depression treatment resistant anxiety and depression. Many studies are focused on finding predictors, which can aid clinicians in identifying the most effective medications for each person. Pharmacogenetics, in particular, identifies genetic variations that determine how the body's metabolism reacts to drugs to treat depression and anxiety. This allows doctors to select the medications that are most likely to be most effective for each patient, minimizing the time and effort required in trials and errors, while eliminating any side effects that could otherwise hinder advancement.

Another approach that is promising is to build models for prediction using multiple data sources, combining data from clinical studies and neural imaging data. These models can be used to identify which variables are the most predictive of a particular outcome, like whether a medication will help with symptoms or mood. These models can be used to determine the patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of treatment currently being administered.

A new generation of studies uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been demonstrated to be effective in predicting treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry, and it is expected that they will become the standard for future clinical practice.

In addition to ML-based prediction models The study of the underlying mechanisms of depression continues. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This theory suggests that individual depression treatment will be built around targeted therapies that target these circuits to restore normal function.

One way to do this is by using internet-based programs that offer a more individualized and personalized experience for patients. For example, one study found that a web-based program was more effective than standard care in alleviating symptoms and ensuring a better quality of life for people with MDD. A controlled, randomized study of a personalized treatment for depression revealed that a substantial percentage of patients experienced sustained improvement as well as fewer side negative effects.

Predictors of Side Effects

A major challenge in personalized depression treatment is predicting which antidepressant medications will cause minimal or no side effects. Many patients have a trial-and error approach, using a variety of medications being prescribed before settling on one that is safe and effective. Pharmacogenetics offers a fascinating new method for an efficient and specific approach to selecting antidepressant treatments.

A variety of predictors are available to determine which antidepressant to prescribe, including gene variations, phenotypes of patients (e.g. gender, sex or ethnicity) and comorbidities. To identify the most reliable and valid predictors for a specific treatment, random controlled trials with larger numbers of participants will be required. This is because it could be more difficult to identify interactions or moderators in trials that only include one episode per person instead of multiple episodes spread over a long period of time.

Additionally the estimation of a patient's response to a specific medication is likely to need to incorporate information regarding comorbidities and symptom profiles, as well as the patient's personal experience of its tolerability and effectiveness. Currently, only a few easily measurable sociodemographic variables as well as clinical variables appear to be consistently associated with response to MDD. These include gender, age, race/ethnicity, BMI, SES and the presence of alexithymia.

There are many challenges to overcome in the use of pharmacogenetics for depression treatment. First is a thorough understanding of the underlying genetic mechanisms is needed as well as an understanding of what constitutes a reliable predictor for treatment response. In addition, ethical concerns, such as privacy and the ethical use of personal genetic information, must be carefully considered. In the long term the use of pharmacogenetics could be a way to lessen the stigma associated with mental health treatment and improve treatment outcomes for those struggling with depression. But, like any approach to psychiatry careful consideration and planning is essential. In the moment, it's best to offer patients a variety of medications for depression that are effective and urge them to talk openly with their doctor.