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Personalized Depression Treatment

For a lot of people suffering from depression treatment effectiveness, traditional therapies and medications are not effective. The individual approach to treatment could be the solution.

Cue is an intervention platform for digital devices that converts passively collected smartphone sensor data into personalized micro-interventions to improve mental health. We looked at the best treatment for severe depression-fitting personal ML models to each subject using Shapley values to determine their feature predictors. The results revealed distinct characteristics that deterministically changed mood over time.

Predictors of Mood

Depression is the leading cause of mental illness in the world.1 Yet only half of those with the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat patients who are most likely to benefit from certain treatments.

A customized depression treatment is one method to achieve this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from certain treatments. They are using sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence and other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavior indicators of response.

The majority of research conducted to date has focused on sociodemographic and clinical characteristics. These include demographics such as gender, age and education as well as clinical aspects like symptom severity, comorbidities and biological markers.

A few studies have utilized longitudinal data in order to determine mood among individuals. Few studies also take into consideration the fact that mood can be very different between individuals. Therefore, it is critical to develop methods that allow for 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 enables the team to develop algorithms that can detect distinct patterns of behavior and emotions that are different between people.

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

This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was weak, however (Pearson r = 0,08; BH adjusted P-value 3.55 10 03) and varied greatly between individuals.

Predictors of symptoms

Depression is one of the world's leading causes of disability1 yet it is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigma associated with depression treatment cbt disorders hinder many people from seeking help.

To assist in individualized treatment, it is crucial to identify the factors that predict symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which is unreliable and only detects a limited number of features related to depression.2

Machine learning is used to combine continuous digital behavioral phenotypes of a person captured by sensors on smartphones and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory, CAT-DI) together with other predictors of severity of symptoms could improve the accuracy of diagnosis and the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements. They also capture a wide range of distinctive behaviors and activity patterns that are difficult to capture with interviews.

The study enrolled University of California Los Angeles (UCLA) students who were suffering from moderate to severe depressive symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical care according to the severity of their depression. Those with a score on the CAT DI of 35 or 65 were allocated online support with a peer coach, while those who scored 75 were sent to in-person clinics for psychotherapy.

At the beginning, participants answered the answers to a series of questions concerning their personal demographics and psychosocial characteristics. The questions included age, sex, and education, marital status, financial status as well as whether they divorced or not, their current suicidal ideas, intent or attempts, and the frequency with which they consumed alcohol. Participants also rated their degree of depression severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted every other week for the participants who received online support and once a week for those receiving in-person support.

Predictors of Treatment Reaction

Research is focusing on personalized treatment for depression. Many studies are focused on identifying predictors, which will help clinicians identify the most effective medications to treat each patient. In particular, pharmacogenetics identifies genetic variations that affect the way that the body processes antidepressants. This lets doctors choose the medications that are likely to be the most effective for every patient, minimizing time and effort spent on trial-and-error treatments and avoid any negative side effects.

Another promising method is to construct models for prediction using multiple data sources, combining data from clinical studies and neural imaging data. These models can be used to determine the variables that are most likely to predict a specific outcome, like whether a drug will help with symptoms or mood. These models can be used to determine the patient's response to a treatment, which will help doctors to maximize the effectiveness of their treatment.

A new type of research employs machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables and improve the accuracy of predictive. These models have been demonstrated to be effective in predicting treatment outcomes for example, the response to antidepressants. These approaches are becoming more popular in psychiatry and could become the standard of future treatment.

In addition to the ML-based prediction models, research into the mechanisms behind depression is continuing. Recent findings suggest that depression is linked to the dysfunctions of specific neural networks. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.

Internet-based interventions are an option to accomplish this. They can provide a more tailored and individualized experience for patients. One study found that a web-based program improved symptoms and provided a better quality life for MDD patients. A controlled study that was randomized to an individualized treatment for depression showed that a substantial percentage of patients saw improvement over time and had fewer adverse consequences.

Predictors of side effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will have very little or no side effects. Many patients have a trial-and error approach, using various medications prescribed until they find one that is effective and tolerable. Pharmacogenetics offers a new and exciting way to select antidepressant medicines that are more effective and specific.

There are several variables that can be used to determine which antidepressant should be prescribed, including genetic variations, phenotypes of the patient such as ethnicity or gender, and comorbidities. To identify the most reliable and valid predictors for a specific treatment, random controlled trials with larger samples will be required. This is because it may be more difficult to detect interactions or moderators in trials that contain only one episode per person rather than multiple episodes over a period of time.

Additionally, the prediction of a patient's reaction to a particular medication will also likely require information on comorbidities and symptom profiles, in addition to the patient's prior subjective experience of its tolerability and effectiveness. At present, only a few easily identifiable sociodemographic and clinical variables appear to be reliably associated with the severity of MDD factors, including age, gender, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics to depression treatment is still in its early stages, and many challenges remain. First is a thorough understanding of the genetic mechanisms is required, as is a clear definition of what is a reliable predictor of treatment response. Additionally, ethical issues like privacy and the responsible use of personal genetic information must be carefully considered. In the long run, pharmacogenetics may provide an opportunity to reduce the stigma associated with mental health care and improve the outcomes of those suffering with depression treatment medications (Dokuwiki`s statement on its official blog). But, like any other psychiatric treatment, careful consideration and application is necessary. At present, it's recommended to provide patients with a variety of medications for depression that work and encourage patients to openly talk with their physicians.