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 answer.

Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalised micro-interventions for improving mental health. We examined the most effective-fitting personalized ML models for each individual, using Shapley values to determine their feature predictors. This revealed distinct features that were deterministically changing mood over time.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 However, only about half of those who have the disorder receive treatment1. To improve the outcomes, doctors must be able to identify and treat patients who are the most likely to respond to certain treatments.

Personalized depression treatment is one way to do this. Using mobile phone sensors, an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from the treatments they receive. Two grants were awarded that total over $10 million, they will employ these tools to identify the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

The majority of research into predictors of depression treatment effectiveness (read this post from nerdgaming.science) has been focused on clinical and sociodemographic characteristics. These include demographics such as age, gender, and education, as well as clinical aspects like symptom severity, comorbidities and biological markers.

While many of these variables can be predicted from information available in medical records, few studies have utilized longitudinal data to study the causes of mood among individuals. Few studies also take into account the fact that moods 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. The team will then create algorithms to detect patterns of behaviour and emotions that are unique to each individual.

In addition to these methods, the team also developed a machine-learning algorithm that models the dynamic variables that influence each person's mood. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.

The digital phenotype was associated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. The correlation was weak, 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 the most common reason for disability across the world1, but it is often untreated and misdiagnosed. Depression disorders are usually not treated due to the stigma associated with them, as well as the lack of effective interventions.

To allow for individualized treatment, identifying patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few symptoms associated with depression.

Machine learning is used to blend continuous digital behavioral phenotypes of a person captured by sensors on smartphones and a validated online mental health tracker (the Computerized Adaptive Testing postnatal depression treatment Inventory CAT-DI) together with other predictors of symptom severity can increase the accuracy of diagnostics and treatment efficacy for depression. Digital phenotypes can provide continuous, high-resolution measurements as well as capture a wide variety of distinct behaviors and patterns that are difficult to document through interviews.

The study enrolled University of California Los Angeles (UCLA) students with mild to severe depression symptoms. participating 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 assistance or in-person clinics depending on their depression severity. Participants who scored a high on the CAT-DI of 35 65 were allocated online support via an online peer coach, whereas those who scored 75 patients were referred to in-person psychotherapy.

Participants were asked a series questions at the beginning of the study concerning their demographics and psychosocial characteristics. These included sex, age education, work, and financial status; if they were divorced, married, or single; current suicidal ideation, intent or attempts; as well as the frequency with that they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale from 0-100. CAT-DI assessments were conducted every week for those who received online support and weekly for those receiving in-person support.

Predictors of the Reaction to Treatment

Personalized depression treatment is currently a top research topic and a lot of studies are aimed to identify predictors that help clinicians determine the most effective drugs for each patient. In particular, pharmacogenetics identifies genetic variants that determine the way that the body processes antidepressants. This lets doctors choose the medications that will likely work best medication to treat anxiety and depression for each patient, while minimizing the time and effort needed for trial-and-error treatments and eliminating any adverse consequences.

Another promising method is to construct models of prediction using a variety of data sources, including data from clinical studies and neural imaging data. These models can be used to determine which variables are the most likely to predict a specific outcome, like whether a drug will improve mood or symptoms. These models can also be used to predict the response of a patient to treatment that is already in place and help doctors maximize the effectiveness of their treatment currently being administered.

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 multiple variables and increase predictive accuracy. These models have proven to be useful in forecasting treatment outcomes, such as the response to antidepressants. These approaches are gaining popularity in psychiatry and it is likely that they will become the norm for future clinical practice.

Research into the underlying causes of depression continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is associated with dysfunctions in specific neural circuits. This theory suggests that individual depression treatment will be based on targeted treatments that target these neural circuits to restore normal functioning.

One method of doing this is by using internet-based programs that offer a more individualized and personalized experience for patients. A study showed that a web-based program improved symptoms and improved quality life for MDD patients. In addition, a controlled randomized trial of a personalized approach to treating depression showed sustained improvement and reduced side effects in a significant proportion of participants.

Predictors of Side Effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will have minimal or no side effects. Many patients are prescribed a variety of drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant medicines that are more effective and precise.

There are many predictors that can be used to determine the antidepressant to be prescribed, such as gene variations, phenotypes of the patient such as ethnicity or gender, and the presence of comorbidities. To identify the most reliable and accurate predictors for a particular treatment, random controlled trials with larger samples will be required. This is because the identifying of moderators or interaction effects could be more difficult in trials that consider a single episode of treatment per person instead of multiple sessions of treatment over a period of time.

Furthermore the prediction of a patient's response to a specific medication is likely to require information about symptoms and comorbidities as well as the patient's personal experience of its tolerability and effectiveness. At present, only a handful of easily identifiable sociodemographic variables and clinical variables seem to be reliably related to response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

Many challenges remain in the application of pharmacogenetics for depression treatment. First is a thorough understanding of the genetic mechanisms is required, as is a clear definition of what constitutes a reliable predictor for treatment response. In addition, ethical issues such as privacy and the ethical use of personal genetic information, must be carefully considered. In the long run pharmacogenetics can provide an opportunity to reduce the stigma associated with mental health treatment and improve the outcomes of those suffering with depression treatment elderly. But, like all approaches to psychiatry, careful consideration and planning is essential. At present, it's recommended to provide patients with an array of depression medications that are effective and urge them to talk openly with their physicians.