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Personalized Depression Treatment

For many people gripped by depression, traditional therapy and medications are not effective. Personalized treatment may be the solution.

Cue is an intervention platform that transforms sensor data collected from smartphones into customized micro-interventions that improve mental health. We looked at the best-fitting personal ML models to each person, using Shapley values to discover their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only half of those suffering from the condition receive treatment1. To improve outcomes, clinicians need to be able to identify and treat patients who have the highest likelihood of responding to specific treatments.

The ability to tailor depression treatments is one method to achieve this. By using sensors on mobile phones and 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. With two grants totaling more than $10 million, they will make use of these techniques to determine biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.

The majority of research into predictors of depression treatment resistant depression effectiveness has focused on sociodemographic and clinical characteristics. These include demographics such as gender, age and education, as well as clinical aspects like symptom severity and comorbidities as well as biological markers.

A few studies have utilized longitudinal data in order to determine mood among individuals. A few studies also take into consideration the fact that moods can vary significantly between individuals. Therefore, it is critical to create methods that allow the recognition 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 identify different patterns of behavior and emotions that differ between individuals.

In addition to these modalities the team created a machine learning algorithm that models the dynamic variables that influence each person's mood. The algorithm combines the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype was found to be associated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. The correlation was low however (Pearson r = 0,08; P-value adjusted for BH = 3.55 x 10 03) and varied greatly among individuals.

Predictors of Symptoms

Depression is the most common cause of disability in the world1, however, it is often not properly diagnosed and treated. In addition an absence of effective treatments and stigma associated with depressive disorders prevent many from seeking treatment.

To aid in the development of a personalized treatment, it is important to identify the factors that predict symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only detect a few symptoms associated with depression.

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements as well as capture a variety of distinct behaviors and patterns that are difficult to record through interviews.

The study involved University of California Los Angeles (UCLA) students with moderate to severe depressive symptoms. who were enrolled in the Screening and Treatment ect for treatment resistant depression Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care based on the degree of their depression. Participants who scored a high on the CAT-DI scale of 35 65 were given online support by the help of a coach. Those with a score 75 patients were referred to psychotherapy in-person.

Participants were asked a series questions at the beginning of the study about their demographics and psychosocial characteristics. These included age, sex education, work, and financial status; if they were partnered, divorced or single; the frequency of suicidal thoughts, intentions or attempts; as well as the frequency with that they consumed alcohol. Participants also rated their degree of depression symptom severity on a 0-100 scale using the CAT-DI. The CAT-DI tests were conducted every other week for participants that received online support, and weekly for those receiving in-person treatment.

Predictors of Treatment Response

A customized treatment for depression is currently a top research topic and a lot of studies are aimed to identify predictors that allow clinicians to identify the most effective drugs for each individual. In particular, pharmacogenetics identifies genetic variants that determine how the body metabolizes antidepressants. This enables doctors to choose drugs that are likely to be most effective for each patient, minimizing the time and effort involved in trial-and-error procedures and avoid any adverse effects that could otherwise hinder progress.

Another promising approach is building models for prediction using multiple data sources, including clinical information and neural imaging data. These models can be used to identify the most effective combination of variables that is predictors of a specific outcome, like whether or not a drug is likely to improve symptoms and mood. 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 the 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 blend the effects of several variables to improve predictive accuracy. 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 are likely to be the norm in future medical practice.

In addition to ML-based prediction models The study of the mechanisms that cause depression is continuing. Recent research suggests that depression is related to the malfunctions of certain neural networks. This theory suggests that an individualized treatment for depression will be based upon targeted treatments that restore normal function to these circuits.

One way to do this is by using internet-based programs which can offer an individualized and tailored experience for patients. One study found that an internet-based program improved symptoms and led to a better quality of life for MDD patients. A controlled study that was randomized to an individualized treatment for depression revealed that a significant number of patients experienced sustained improvement and had fewer adverse consequences.

Predictors of side effects

A major obstacle in individualized depression treatment history (his response) treatment involves identifying and predicting which antidepressant medications will cause the least amount of side effects or none at all. Many patients are prescribed a variety of medications before finding a medication that is both effective and well-tolerated. Pharmacogenetics offers a new and exciting way to select antidepressant medicines that are more effective and specific.

There are a variety of predictors that can be used to determine which antidepressant should be prescribed, including genetic variations, phenotypes of patients such as ethnicity or gender, and co-morbidities. To determine the most reliable and accurate predictors of a specific treatment, randomized controlled trials with larger sample sizes will be required. This is because it could be more difficult to determine the effects of moderators or interactions in trials that comprise only one episode per person rather than multiple episodes over a long period of time.

Furthermore to that, predicting a patient's reaction will likely require information on the severity of symptoms, comorbidities and the patient's own experience of tolerability and effectiveness. At present, only a few easily assessable sociodemographic and clinical variables seem to be reliable in predicting the severity of MDD, such as gender, age, race/ethnicity and SES BMI and the presence of alexithymia, and the severity of depressive symptoms.

The application of pharmacogenetics to treatment for depression is in its beginning stages, and many challenges remain. First, a clear understanding of the underlying genetic mechanisms is required and an understanding of what is a reliable predictor of treatment response. Ethics like privacy, and the ethical use of genetic information must also be considered. The use of pharmacogenetics may be able to, over the long term, reduce stigma surrounding mental health treatments and improve treatment outcomes. But, like all approaches to psychiatry, careful consideration and application is required. For now, it is ideal to offer patients various depression medications that work and encourage them to speak openly with their physicians.