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Personalized Depression Treatment

For many people gripped by depression, traditional therapy and medications are not effective. The individual approach to treatment could be the answer.

Cue is an intervention platform that transforms passively acquired sensor data 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 features and predictors. The results revealed distinct characteristics 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 people suffering from the disorder receive treatment1. In order to improve outcomes, clinicians need to be able to recognize and treat patients who have the highest likelihood of responding to particular treatments.

The treatment of depression can be personalized to help. By using mobile phone sensors and 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 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 predictors for depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographic variables such as age, sex and educational level, clinical characteristics like the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.

While many of these variables can be predicted from the information in medical records, very few studies have utilized longitudinal data to study predictors of mood in individuals. Many studies do not take into consideration the fact that moods vary significantly between individuals. Therefore, it is crucial to develop methods that permit the identification 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 enables the team to create algorithms that can systematically identify various patterns of behavior and emotions that are different between people.

The team also created a machine-learning algorithm that can identify dynamic predictors of each person's depression mood. The algorithm blends the individual characteristics to create an individual "digital genotype" for each participant.

This digital phenotype was linked to CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.

Predictors of symptoms

depression treatment medicine is among the most prevalent causes of disability1, but it is often not properly diagnosed and treated. In addition the absence of effective treatments and stigmatization associated with depressive disorders stop many individuals 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 are unreliable and only reveal a few symptoms associated with depression.

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). 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 included University of California Los Angeles students with mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment according to the degree of their depression. Participants who scored a high on the CAT DI of 35 or 65 were assigned online support by an instructor and those with a score 75 were sent to in-person clinics for psychotherapy.

At the beginning, participants answered a series of questions about their personal demographics and psychosocial characteristics. These included age, sex and education, as well as work and financial status; whether they were divorced, married or single; their current suicidal ideas, intent, or attempts; and the frequency with that they consumed alcohol. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. The CAT-DI test was conducted every two weeks for participants who received online support, and weekly for those who received in-person support.

Predictors of the Reaction to Treatment

The development of a personalized depression treatment is currently a research priority and a lot of studies are aimed at identifying predictors that allow clinicians to identify the most effective medications for each person. Particularly, pharmacogenetics is able to identify genetic variants that influence the way that the body processes antidepressants. This allows doctors to select medications that are likely to work best for each patient, minimizing the time and effort involved in trial-and-error treatments and avoiding side effects that might otherwise hinder advancement.

Another option is to develop prediction models that combine clinical data and neural imaging data. These models can be used to determine the variables that are most predictive of a specific outcome, like whether a drug will improve symptoms or mood. These models can be used to predict the patient's response to a treatment, allowing doctors maximize the effectiveness.

A new generation uses machine learning methods such as supervised and classification algorithms such as regularized logistic regression, and tree-based methods to combine the effects of several variables and increase the accuracy of predictions. These models have proven to be effective in predicting treatment outcomes such as the response to antidepressants. These methods are becoming more popular in psychiatry and will likely become the norm in the future clinical practice.

In addition to the ML-based prediction models research into the mechanisms that cause depression continues. Recent research suggests that the disorder is associated with neurodegeneration in particular circuits. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.

One method to achieve this is to use internet-based interventions that offer a more individualized and tailored experience for patients. A study showed that an internet-based program helped improve symptoms and improved quality life for MDD patients. Furthermore, a randomized controlled trial of a personalized approach to depression treatment showed steady improvement and decreased 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 minimal or no side effects. Many patients take a trial-and-error method, involving a variety of medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics provides a novel and exciting method of selecting antidepressant drugs that are more effective and precise.

Several predictors may be used to determine the Best Treatment For Severe Depression (Https://Pattern-Wiki.Win/) antidepressant to prescribe, including gene variations, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. However, identifying the most reliable and accurate predictors for a particular treatment is likely to require randomized controlled trials with significantly larger numbers of participants than those that are typically part of clinical trials. This is because it could be more difficult to detect moderators or interactions in trials that contain only one episode per person instead of multiple episodes spread over time.

Furthermore, predicting a patient's response will likely require information on comorbidities, symptom profiles and the patient's personal experience of tolerability and effectiveness. At present, only a handful of easily identifiable sociodemographic variables and clinical variables are reliably related to response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

The application of pharmacogenetics to treatment for manic depression for depression is in its beginning stages and there are many obstacles to overcome. It is crucial to have a clear understanding and definition of the genetic mechanisms that underlie depression, as well as an accurate definition of a reliable indicator of the response to treatment. Additionally, ethical issues, such as privacy and the ethical use of personal genetic information should be considered with care. Pharmacogenetics could be able to, over the long term reduce stigma associated with mental health treatment and improve treatment outcomes. Like any other psychiatric treatment, it is important to carefully consider and implement the plan. At present, it's best to offer patients an array of depression medications that work and encourage them to speak openly with their doctor.