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Personalized Depression Treatment

For many suffering from depression, traditional therapy and medication isn't effective. A customized treatment may be the answer.

Cue is a digital intervention platform that converts passively collected 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 determine their characteristic predictors. The results revealed distinct characteristics that deterministically changed mood over time.

Predictors of Mood

Depression is the leading cause of mental illness across the world.1 Yet only half of those affected receive treatment. To improve outcomes, healthcare professionals must be able identify and treat patients most likely to benefit from certain treatments.

Personalized depression treatment effectiveness treatment is one method to achieve this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from certain treatments. They are using mobile phone sensors and a voice assistant incorporating artificial intelligence and other digital tools. With two grants awarded totaling over $10 million, they will make use of these technologies to identify biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.

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

While many of these variables can be predicted from the information in medical records, very few studies have used longitudinal data to explore the causes of mood among individuals. Many studies do not take into account the fact that mood can differ significantly between individuals. Therefore, it is crucial to create methods that allow the recognition of the individual differences in mood predictors 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. The team will then create algorithms to identify patterns of behavior and emotions that are unique to each individual.

In addition to these methods, the team created a machine learning algorithm to model the changing predictors of each person's depressed mood. The algorithm blends the individual differences to create a unique "digital genotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, which is a psychometrically validated scale for assessing severity of symptom. However the correlation was tinny (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely among individuals.

Predictors of Symptoms

untreatable depression is the most common cause of disability in the world1, but it is often untreated and misdiagnosed. In addition the absence of effective interventions and stigma associated with depressive disorders prevent many from seeking treatment.

To facilitate personalized treatment in order to provide a more personalized treatment, identifying factors that predict the severity of symptoms is crucial. However, the methods used to predict symptoms depend on the clinical interview which is not reliable and only detects a small number of symptoms that are associated with depression.2

Using machine learning to blend continuous digital behavioral phenotypes of a person captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) with other predictors of symptom severity could increase the accuracy of diagnostics and treatment efficacy for depression. These digital phenotypes are able to capture a variety of unique behaviors and activities, which are difficult to capture through interviews, and also allow for continuous and high-resolution measurements.

The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical care depending on their depression severity. Participants who scored a high on the CAT-DI scale of 35 65 were assigned online support via a peer coach, while those who scored 75 patients were referred to psychotherapy in person.

At baseline, participants provided the answers to a series of questions concerning their personal characteristics and psychosocial traits. These included age, sex education, work, and financial situation; whether they were divorced, partnered or single; the frequency of suicidal ideation, intent, or attempts; and the frequency at that they consumed alcohol. The CAT-DI was used for assessing the severity of depression symptoms on a scale from zero to 100. CAT-DI assessments were conducted each other week for the participants who received online support and weekly for those receiving in-person psychological treatment for depression.

Predictors of the Reaction to Treatment

Research is focusing on personalization of depression treatment. Many studies are aimed at finding predictors, which can aid clinicians in identifying the most effective medications for each person. Pharmacogenetics in particular uncovers genetic variations that affect how the human body metabolizes drugs. This lets doctors choose the medications that are most likely to work for every patient, minimizing the amount of time and effort required for trial-and error treatments and avoid any negative side negative effects.

Another promising method is to construct prediction models using multiple data sources, combining clinical information and neural imaging data. These models can be used to identify the best combination of variables predictors of a specific outcome, like whether or not a drug will improve mood and symptoms. These models can be used to determine a patient's response to treatment that is already in place, allowing doctors to maximize the effectiveness of the current therapy.

A new era of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of many variables and improve the accuracy of predictive. These models have shown to be useful for forecasting treatment outcomes, such as the response to antidepressants. These models are getting more popular in psychiatry and it is expected 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 Depression Treatment History is connected to the dysfunctions of specific neural networks. This suggests that an individualized depression treatment will be focused on treatments that target these circuits in order to restore normal function.

One way to do this is to use internet-based interventions 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 improving symptoms and providing a better quality of life for patients suffering from MDD. In addition, a controlled randomized study of a customized approach to treating depression showed sustained improvement and reduced adverse effects in a large percentage of participants.

Predictors of Side Effects

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

There are many predictors that can be used to determine the antidepressant to be prescribed, including genetic variations, phenotypes of patients such as gender or ethnicity, and comorbidities. However it is difficult to determine the most reliable and valid predictive factors for a specific treatment will probably require randomized controlled trials of much larger samples than those typically enrolled in clinical trials. This is because the identifying of interactions or moderators could be more difficult in trials that consider a single episode of treatment per person instead of multiple episodes of treatment over time.

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

Many issues remain to be resolved in the application of pharmacogenetics for depression treatment. First, it is important to be able to comprehend and understand the definition of the genetic factors that cause depression, as well as a clear definition of an accurate predictor of treatment response. Ethics such as privacy and the responsible use genetic information should also be considered. In the long run pharmacogenetics can provide an opportunity to reduce the stigma associated with mental health treatment and improve the treatment outcomes for patients with depression. Like any other psychiatric treatment it is essential to give careful consideration and implement the plan. For now, it is ideal to offer patients an array of depression medications that are effective and urge them to talk openly with their doctors.