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Personalized Depression Treatment

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

Cue is a digital intervention platform that converts passively collected sensor data from smartphones into customized micro-interventions designed to improve mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to understand their feature predictors and uncover distinct features that are able to change mood over time.

Predictors of Mood

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

Personalized 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 specific treatments. They make use of sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence and other digital tools. With two grants totaling over $10 million, they will use these technologies to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.

So far, the majority of research on predictors for depression treatment medicine treatment effectiveness has centered on sociodemographic and clinical characteristics. These include demographics like age, gender and education and clinical characteristics like symptom severity, comorbidities and biological markers.

While many of these variables can be predicted by the information in non medical treatment for depression records, only a few studies have employed longitudinal data to explore predictors of mood in individuals. Few studies also consider the fact that moods can be very different between individuals. Therefore, it is important to develop methods which allow for the determination and quantification of the personal differences between mood predictors treatments, mood predictors, etc.

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 allows the team to develop algorithms that can identify different patterns of behavior and emotions that vary between individuals.

In addition to these methods, the team developed a machine-learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these individual differences into a unique "digital phenotype" for each participant.

This digital phenotype has been associated with CAT DI scores which is a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is one of the leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depressive disorders are often not treated due to the stigma associated with them and the absence of effective treatments.

To assist in individualized treatment, it is essential to identify predictors of symptoms. The current prediction methods rely heavily on clinical interviews, which are not reliable and only identify a handful of characteristics that are associated with depression.

Using machine learning to blend continuous digital behavioral phenotypes captured by smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of severity of symptoms can improve the accuracy of diagnosis and treatment efficacy for depression. These digital phenotypes provide a wide range of unique behaviors and activities, which are difficult to document through interviews and permit continuous, high-resolution measurements.

The study enrolled University of California Los Angeles (UCLA) students with mild 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 to clinical treatment according to the severity of their depression. Participants with a CAT-DI score of 35 65 students were assigned online support by a coach and those with a score 75 patients were referred to psychotherapy in-person.

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial traits. These included age, sex education, work, and financial status; whether they were partnered, divorced or single; the frequency of suicidal ideation, intent or attempts; as well as the frequency at the frequency 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 carried out every two weeks for participants who received online support, and weekly for those who received in-person assistance.

Predictors of Treatment Response

A customized treatment for depression treatment for elderly is currently a research priority and many studies aim at identifying predictors that enable clinicians to determine the most effective medications for each person. Pharmacogenetics, for instance, is a method of identifying genetic variations that affect how the body's metabolism reacts to drugs. This allows doctors select medications that will likely work best for each patient, while minimizing the time and effort needed for trial-and error treatments and eliminating any adverse effects.

Another promising approach is to create prediction models combining information from clinical studies and neural imaging data. These models can be used to identify the most effective combination of variables that is predictors of a specific outcome, such as whether or not a medication will improve symptoms and mood. These models can also be used to predict a patient's response to treatment that is already in place, allowing doctors to maximize the effectiveness of current therapy.

A new generation of machines employs machine learning techniques such as algorithms for classification and supervised learning such as regularized logistic regression, and tree-based techniques to combine the effects of several variables and improve predictive accuracy. These models have been shown to be useful in predicting outcomes of treatment, such as response to antidepressants. These models are getting more popular in psychiatry and it is likely that they will become the standard for the future of clinical practice.

In addition to prediction models based on ML 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 suggests that individual depression treatment will be based on targeted treatments that target these neural circuits to restore normal function.

One method to achieve this is to use internet-based interventions which can offer an individualized and tailored experience for patients. For example, one study discovered that a web-based treatment was more effective than standard treatment in reducing symptoms and ensuring the best quality of life for patients with MDD. Furthermore, a randomized controlled trial of a personalized approach to depression treatment showed an improvement in symptoms and fewer adverse effects in a large percentage of participants.

Predictors of side effects

A major challenge in personalized depression treatment is predicting which antidepressant medications 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 provides an exciting new method for an efficient and specific method of selecting antidepressant therapies.

There are several variables that can be used to determine the antidepressant that should be prescribed, including gene variations, patient phenotypes such as gender or ethnicity and comorbidities. To determine the most reliable and valid predictors of a specific treatment, controlled trials that are randomized with larger numbers of participants will be required. This is due to the fact that the identification of interaction effects or moderators could be more difficult in trials that only consider a single episode of treatment per patient instead of multiple episodes of treatment over time.

Additionally, the prediction of a patient's reaction to a particular medication will also likely require information on comorbidities and symptom profiles, and the patient's personal experience with tolerability and efficacy. Presently, only a handful of easily measurable sociodemographic and clinical variables seem to be reliably associated with the severity of MDD factors, including age, gender race/ethnicity, SES BMI and the presence of alexithymia, and the severity of depressive symptoms.

There are many challenges to overcome in the use of pharmacogenetics for depression treatment. First, it is essential to have a clear understanding and definition of the genetic mechanisms that underlie depression, and an accurate definition of an accurate predictor of treatment response. In addition, ethical concerns, such as privacy and the ethical use of personal genetic information, must be carefully considered. Pharmacogenetics can be able to, over the long term reduce stigma associated with treatments for mental illness and improve the quality of treatment. As with all psychiatric approaches it is crucial to give careful consideration and implement the plan. For now, it is best to offer patients an array of depression medications that are effective treatments for depression and encourage them to talk openly with their doctor.