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Personalized Depression Treatment

Traditional therapies and medications do not work for many people suffering from depression. A customized treatment may be the answer.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions for improving mental health. We examined the most effective treatments for depression-fitting personalized ML models to each person, using Shapley values, in order to understand their feature predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 Yet, only half of people suffering from the disorder receive treatment1. In order to improve outcomes, clinicians need to be able to recognize and treat patients who have the highest likelihood of responding to certain treatments.

A customized depression treatment is one method to achieve this. Utilizing mobile phone sensors as well as an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from which treatments. With two grants awarded totaling more than $10 million, they will make use of these tools to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.

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

While many of these variables can be predicted by the data in medical records, only a few studies have used longitudinal data to explore predictors of mood in individuals. A few studies also consider the fact that moods can be very different between individuals. It is therefore important to devise methods that allow for the analysis and measurement of individual differences between mood predictors, treatment effects, 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 enables the team to create algorithms that can systematically identify distinct patterns of behavior and emotion that vary between individuals.

In addition to these modalities the team created a machine learning algorithm that models the dynamic variables that influence each person's mood. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.

This digital phenotype has been associated with CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was not strong (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely among individuals.

Predictors of symptoms

Depression is one of the most prevalent causes of disability1 but is often untreated and not diagnosed. In addition, a lack of effective interventions and stigma associated with depressive disorders prevent many individuals from seeking help.

To facilitate personalized treatment to improve non pharmacological treatment for depression, identifying the patterns that can predict symptoms is essential. The current prediction methods rely heavily on clinical interviews, which aren't reliable and only detect a few features associated with depression.

Machine learning can improve the accuracy of the diagnosis and treatment of 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 provide continuous, high-resolution measurements and capture a variety of distinctive behaviors and activity patterns that are difficult to document through interviews.

The study comprised University of California Los Angeles students who had mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were sent online for assistance or medical care based on the degree of their depression. Those with a score on the CAT-DI of 35 65 were given online support by a coach and those with a score 75 were sent to in-person clinical care for psychotherapy.

Participants were asked a series questions at the beginning of the study regarding their psychosocial and demographic characteristics as well as their socioeconomic status. The questions covered age, sex and education, financial status, marital status as well as whether they divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. Participants also scored their level of depression severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted each other week for participants who received online support and weekly for those receiving in-person support.

Predictors of Treatment Reaction

Research is focusing on personalized treatment for depression. Many studies are aimed at finding predictors that can help doctors determine the most effective medications to treat each patient. Particularly, pharmacogenetics can identify genetic variants that determine how the body's metabolism reacts to antidepressants. This enables doctors to choose drugs that are likely to be most effective for each patient, minimizing the time and effort required in trial-and-error treatments and avoiding side effects that might otherwise slow the progress of the patient.

Another approach that is promising is to build prediction models that combine clinical data and neural imaging data. These models can then be used to identify the best combination of variables that is predictors of a specific outcome, such as whether or not a particular medication will improve symptoms and mood. These models can be used to predict the patient's response to a treatment, allowing doctors to maximize the effectiveness.

A new generation employs machine learning methods such as the supervised and classification algorithms such as regularized logistic regression, and tree-based methods to integrate the effects of several variables to improve the accuracy of predictive. These models have been proven to be useful in predicting treatment outcomes such as the response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to become the standard of future clinical practice.

In addition to the ML-based prediction models, research into the mechanisms that cause depression continues. Recent findings suggest that the disorder is associated with neural dysfunctions that affect specific circuits. This suggests that individual depression treatment will be built around targeted treatments that target these circuits in order to restore normal functioning.

One method to achieve this is by using internet-based programs 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 treatment in improving symptoms and providing a better quality of life for people with MDD. A controlled study that was randomized to a customized treatment for depression showed that a significant number of participants experienced sustained improvement and fewer side effects.

Predictors of Side Effects

In the treatment of depression, one of the most difficult aspects is predicting and identifying which antidepressant medications will have no or minimal adverse effects. Many patients have a trial-and error approach, with several medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics is an exciting new method for an efficient and specific approach to selecting antidepressant treatments.

There are many variables that can be used to determine which antidepressant should be prescribed, such as gene variations, phenotypes of the patient such as gender or ethnicity, and co-morbidities. To identify the most reliable and reliable predictors of a specific treatment, random controlled trials with larger sample sizes will be required. This is because it could be more difficult to detect interactions or moderators in trials that only include one episode per participant instead of multiple episodes spread over time.

In addition to that, predicting a patient's reaction will likely require information on the severity of symptoms, comorbidities and the patient's subjective perception of the effectiveness and tolerability. At present, only a few easily identifiable sociodemographic and clinical variables seem to be correlated with the severity of MDD factors, including age, gender race/ethnicity, BMI and the presence of alexithymia and the severity of depression treatment without antidepressants symptoms.

The application of pharmacogenetics to depression treatment is still in its infancy and there are many hurdles to overcome. first line treatment for anxiety and depression, it is essential to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, and an understanding of a reliable predictor of treatment response. Additionally, ethical issues such as privacy and the responsible use of personal genetic information, should be considered with care. In the long run pharmacogenetics can be a way to lessen the stigma that surrounds mental health treatment and improve treatment outcomes for those struggling with depression. But, like any other psychiatric treatment, careful consideration and application is necessary. At present, it's best to offer patients various depression medications that work and encourage them to talk openly with their doctors.