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Personalized Depression Treatment

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

Cue is an intervention platform for digital devices that translates passively acquired normal smartphone sensor data into personalized micro-interventions to improve mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to discover their predictors of feature and reveal distinct features that are able to change mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only about half of those who have the condition receive treatment1. To improve the outcomes, doctors must be able to recognize and treat patients with the highest likelihood of responding to particular 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 the most from specific treatments. They are using sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. Two grants were awarded that total over $10 million, they will use these technologies to identify the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.

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

Very few studies have used longitudinal data to predict mood in individuals. A few studies also take into consideration the fact that mood can vary significantly between individuals. Therefore, it is essential to develop methods that allow for the determination of the individual differences in mood predictors and treatment 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 detect patterns of behavior and emotions that are unique to each individual.

In addition to these modalities, the team created a machine learning algorithm to model the changing predictors of each person's depressed mood. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.

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

Predictors of Symptoms

Depression is the leading reason for disability across the world1, but it is often misdiagnosed and untreated2. Depressive disorders are often not treated due to the stigma that surrounds them and the absence of effective treatments.

To aid in the development of a personalized treatment, it is important to identify the factors that predict symptoms. The current prediction methods rely heavily on clinical interviews, which aren't reliable and only identify a handful of symptoms associated with depression.

Machine learning can be used to combine continuous digital behavioral phenotypes of a person captured through smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) together with other predictors of severity of symptoms could increase the accuracy of diagnostics and the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements and capture a wide range of unique behaviors and activity patterns that are difficult to capture through interviews.

The study involved University of California Los Angeles (UCLA) students experiencing mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were sent online for assistance or medical care depending on the severity of their depression. Participants with a CAT-DI score of 35 or 65 were assigned online support with an online peer coach, whereas those who scored 75 were routed to in-person clinical care for psychotherapy.

At baseline, participants provided an array of questions regarding their personal demographics and psychosocial characteristics. These included sex, age and education, as well as work and financial status; if they were divorced, partnered or single; the frequency of suicidal ideas, intent or attempts; and the frequency at that they consumed alcohol. Participants also rated their degree of depression symptom severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted every other week for participants who received online support and once a week for those receiving in-person treatment.

Predictors of Treatment Response

Personalized depression treatment is currently a research priority, and many studies aim at identifying predictors that will enable clinicians to determine the most effective medication for each patient. Particularly, pharmacogenetics can identify genetic variations that affect how the body metabolizes antidepressants. This allows doctors to select medications that are likely to work best way to treat depression for each patient, minimizing the time and effort required in trials and errors, while avoiding side effects that might otherwise hinder progress.

Another promising approach is building models for prediction using multiple data sources, combining data from clinical studies and neural imaging data. These models can then be used to determine the most appropriate combination of variables predictive of a particular outcome, like whether or not a particular medication will improve mood and symptoms. These models can also be used to predict a patient's response to an existing treatment which allows doctors to maximize the effectiveness of their current therapy.

A new era of research uses machine learning methods 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 been demonstrated to be useful in predicting the outcome of treatment like the response to antidepressants. These approaches are gaining popularity in psychiatry, and it is expected that they will become the norm for future clinical practice.

In addition to ML-based prediction models The study of the mechanisms that cause depression can be treated is continuing. Recent findings suggest that the disorder is linked with dysfunctions in specific neural circuits. This suggests that an individual depression treatment london treatment will be focused on treatments that target these neural circuits to restore normal function.

One method of doing this is through internet-delivered interventions that offer a more individualized and tailored experience for patients. One study found that an internet-based program improved symptoms and provided a better quality of life for MDD patients. A controlled, randomized study of an individualized treatment for depression found that a significant percentage of patients saw improvement over time as well as fewer side consequences.

Predictors of Side Effects

A major issue in personalizing depression treatment is predicting the antidepressant medications that will have very little or no side effects. Many patients take a trial-and-error method, involving a variety of medications prescribed before finding one that is safe and effective. Pharmacogenetics offers a new and exciting method to choose antidepressant drugs that are more efficient and targeted.

Several predictors may be used to determine which antidepressant to prescribe, including gene variants, patient phenotypes (e.g. sexual orientation, gender or ethnicity) and comorbidities. However it is difficult to determine the most reliable and accurate 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 due to the fact that the identification of interaction effects or moderators can be a lot more difficult in trials that only focus on a single instance of treatment per person instead of multiple episodes of treatment over a period of time.

Furthermore to that, predicting a patient's reaction will likely require information about comorbidities, symptom profiles and the patient's personal perception of effectiveness and tolerability. Currently, only a few easily assessable sociodemographic variables and clinical variables seem to be reliably related to response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

The application of pharmacogenetics to treatment for dementia depression treatment is in its beginning stages and there are many obstacles to overcome. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, as well as an accurate definition of an accurate predictor of treatment response. In addition, ethical issues like 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 mental health treatments and improve the outcomes of treatment. But, like any approach to psychiatry careful consideration and implementation is required. At present, it's best to offer patients various depression medications that are effective and encourage them to speak openly with their doctors.