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Personalized Depression Treatment

For many suffering from depression, traditional therapy and medication are ineffective. Personalized treatment could be the solution.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into personalised micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to understand their feature predictors and reveal distinct characteristics that can be used to predict changes in mood over time.

Predictors of Mood

depression treatment psychology is a leading cause of mental illness in the world.1 Yet the majority of people suffering from the condition receive treatment. To improve outcomes, healthcare professionals must be able to identify and treat patients who are the most likely to benefit from certain treatments.

The ability to tailor depression treatments is one method of doing this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from certain treatments. They use sensors on mobile phones and a voice assistant incorporating artificial intelligence as well as other digital tools. Two grants worth more than $10 million will be used to discover biological and behavior indicators of response.

To date, the majority of research on predictors for depression treatment effectiveness has focused on sociodemographic and clinical characteristics. These include demographic factors like age, sex and education, clinical characteristics including symptoms severity and comorbidities and biological indicators such as neuroimaging and genetic variation.

Very few studies have used longitudinal data to predict mood in individuals. Few also take into account the fact that mood varies significantly between individuals. Therefore, it is essential to develop methods that allow for the recognition 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. This enables the team to create algorithms that can systematically identify various patterns of behavior and emotions that vary between individuals.

In addition to these modalities the team developed a machine-learning algorithm to model the dynamic predictors of each person's depressed mood. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

The digital phenotype was associated 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 10 03) and varied significantly among individuals.

Predictors of symptoms

Depression is among the leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated because of the stigma associated with them and the absence of effective interventions.

To assist in individualized treatment, it is important to identify predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which aren't reliable and only detect a few characteristics that are associated with depression.

Machine learning can be used to combine continuous digital behavioral phenotypes of a person captured through smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing depression and alcohol treatment Inventory the CAT-DI) together with other predictors of severity of symptoms can improve the accuracy of diagnosis and treatment efficacy for depression. Digital phenotypes permit continuous, high-resolution measurements and capture a wide variety of unique behaviors and activity patterns that are difficult to record through interviews.

The study included University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or medical care according to the degree of their depression. Those with a CAT-DI score of 35 or 65 were given online support via an instructor and those with a score 75 were sent to in-person clinics for psychotherapy.

At the beginning of the interview, participants were asked a series of questions about their personal demographics and psychosocial features. The questions asked included age, sex and education as well as marital status, financial status, whether they were divorced or not, their current suicidal thoughts, intent or attempts, and the frequency with which they consumed alcohol. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted each week for those who received online support and every week for those who received in-person support.

Predictors of Treatment Reaction

Personalized depression treatment is currently a research priority and a lot of studies are aimed at identifying predictors that will enable clinicians to determine the most effective medication for each patient. In particular, pharmacogenetics identifies genetic variants that influence how the body's metabolism reacts to antidepressants. This enables doctors to choose the medications that are most likely to work best for each patient, while minimizing the time and effort in trial-and-error treatments and eliminating any side effects that could otherwise slow advancement.

Another approach that is promising is to create prediction models combining the clinical data with neural imaging data. These models can be used to identify the variables that are most likely to predict a specific outcome, such as whether a drug will help with symptoms or mood. These models can be used to determine a patient's response to an existing treatment, allowing doctors to maximize the effectiveness of the current therapy.

A new generation uses machine learning techniques such as supervised and classification algorithms, regularized logistic regression and tree-based methods to combine the effects of multiple variables and improve predictive accuracy. These models have shown to be useful in forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming more popular in psychiatry and will likely be the norm in future clinical practice.

Research into depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is associated with dysfunctions in specific neural circuits. This suggests that an the treatment for depression will be individualized focused on treatments that target these circuits to restore normal function.

One method to achieve this is to use internet-based interventions which can offer an personalized and customized experience for patients. One study found that an internet-based program improved symptoms and improved quality of life for MDD patients. A randomized controlled study of a customized treatment for depression revealed that a substantial percentage of patients experienced sustained improvement as well as fewer side effects.

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 experience a trial-and-error approach, with a variety of medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting method to choose antidepressant medications that is more effective and specific.

There are several predictors that can be used to determine which antidepressant should be prescribed, such as gene variations, phenotypes of patients such as ethnicity or gender, and the presence of comorbidities. To determine the most reliable and valid predictors for a particular treatment, randomized controlled trials with larger samples will be required. This is due to the fact that it can be more difficult to detect the effects of moderators or interactions in trials that comprise only a single episode per person instead of multiple episodes over a period of time.

Additionally the prediction of a patient's response will likely require information about comorbidities, symptom profiles and the patient's subjective perception of effectiveness and tolerability. Currently, only a few easily identifiable sociodemographic variables and clinical variables appear to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

Many issues remain to be resolved when it comes to the use of pharmacogenetics in the treatment of depression treatment guidelines. First, it is important to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, and an understanding of an accurate indicator of the response to treatment resistant Depression treatment. Additionally, ethical issues, Treatment Resistant Depression Treatment such as privacy and the ethical use of personal genetic information must be considered carefully. Pharmacogenetics could, in the long run help reduce stigma around mental health treatments and improve the outcomes of treatment. As with any psychiatric approach, it is important to carefully consider and implement the plan. In the moment, it's best to offer patients an array of depression medications that are effective and urge them to talk openly with their doctor.