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Personalized Depression Treatment

For many people gripped by depression, traditional therapies and medications are not effective. A customized treatment could be the solution.

Cue is an intervention platform that converts sensor data collected from smartphones into customized micro-interventions for improving mental health. We looked at the best-fitting personal ML models for each individual using Shapley values, in order to understand their characteristic predictors. The results revealed distinct characteristics that were deterministically changing mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 Yet, only half of those who have the disorder receive treatment1. To improve the outcomes, doctors must be able to identify and treat patients who have the highest likelihood of responding to certain treatments.

The ability to tailor depression treatments 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 make use of sensors on mobile phones, a voice assistant with artificial intelligence as well as other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavior factors that predict response.

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

While many of these factors can be predicted from data in medical records, only a few studies have used longitudinal data to determine the factors that influence mood in people. Few studies also take into consideration the fact that moods can differ significantly between individuals. Therefore, it is critical to develop methods that permit the identification of different mood predictors for each person and treatments 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. The team can then develop algorithms to recognize patterns of behaviour and emotions that are unique to each person.

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 an individual "digital genotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was weak however (Pearson r = 0,08; P-value adjusted for BH = 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. In addition, a lack of effective interventions and stigma associated with depression disorders hinder many people from seeking help.

To allow for individualized treatment to improve treatment, identifying the patterns that can predict symptoms is essential. However, the current methods for predicting symptoms rely on clinical interview, which has poor reliability and only detects a small number of symptoms that are associated with depression.2

Machine learning can increase the accuracy of diagnosis and treatment for 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). Digital phenotypes can be used to are able to capture a variety of distinct actions and behaviors that are difficult to document through interviews and permit continuous and high-resolution measurements.

The study included University of California Los Angeles students with moderate to severe depression treatment history 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 directed to online assistance or in-person clinics in accordance with their severity of depression. Those with a CAT-DI score of 35 65 were given online support with a coach and those with scores of 75 patients were referred for in-person psychotherapy.

At baseline, participants provided a series of questions about their personal demographics and psychosocial characteristics. These included age, sex, education, work, and financial status; whether they were partnered, divorced or single; the frequency of suicidal ideation, intent or attempts; and the frequency with the frequency they consumed alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale from 0-100. The CAT-DI tests were conducted every other week for participants that received online support, and every week for those who received in-person care.

Predictors of the Reaction to Treatment

Research is focused on individualized depression treatment during pregnancy treatment. Many studies are focused on identifying predictors, which will help doctors determine the most effective medications to treat each individual. Particularly, pharmacogenetics is able to identify genetic variations that affect how the body's metabolism reacts to antidepressants. This lets doctors choose the medications that are most likely to work for each patient, reducing the amount of time and effort required for trial-and error treatments and avoiding any side consequences.

Another promising method is to construct models of prediction using a variety of data sources, such as data from clinical studies and neural imaging data. These models can be used to identify which variables are the most predictive of a specific outcome, like whether a drug will improve symptoms or mood. These models can also be used to predict a patient's response to a treatment they are currently receiving and help doctors maximize the effectiveness of current treatment.

A new generation of machines employs machine learning techniques such as the supervised and classification algorithms, 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 the prediction of treatment outcomes like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could become the standard of future clinical practice.

In addition to prediction models based on ML, research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is linked to dysfunctions in specific neural networks. This suggests that individualized recurrent depression treatment (relevant website) treatment will be focused on therapies that target these circuits in order to restore normal functioning.

One way to do this is to use internet-based interventions which can offer an personalized and customized experience for patients. A study showed that a web-based program improved symptoms and improved quality of life for MDD patients. Furthermore, a randomized controlled study of a personalised approach to treating depression showed sustained improvement and reduced side effects in a significant percentage of participants.

Predictors of adverse effects

In the treatment of depression a major challenge is predicting and determining which antidepressant medication will have very little or no adverse negative effects. Many patients have a trial-and error method, involving several medications prescribed until they find one that is effective and tolerable. Pharmacogenetics offers a fascinating new avenue for a more efficient and targeted approach to choosing antidepressant medications.

A variety of predictors are available to determine the best antidepressant to prescribe, including gene variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. To identify the most reliable and valid predictors for a specific treatment, random controlled trials with larger sample sizes will be required. This is because it may be more difficult to identify interactions or moderators in trials that comprise only a single episode per person instead of multiple episodes spread over time.

Additionally to that, predicting a patient's reaction will likely require information on comorbidities, symptom profiles and the patient's subjective perception of the effectiveness and tolerability. Currently, only a few easily assessable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics in residential treatment for depression for depression is in its beginning stages and there are many hurdles to overcome. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, and an understanding of a reliable indicator of the response to treatment. Ethics like privacy, and the responsible use of genetic information must also be considered. In the long term the use of pharmacogenetics could provide an opportunity to reduce the stigma that surrounds mental health care and improve the outcomes of those suffering with depression. However, as with all approaches to psychiatry, careful consideration and implementation is essential. The best method is to provide patients with a variety of effective depression medication options and encourage them to speak freely with their doctors about their experiences and concerns.