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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapy and medications are not effective. A customized treatment may be the solution.

Cue is an intervention platform for digital devices that transforms passively acquired sensor data from smartphones into customized micro-interventions to improve mental health. We examined the most effective-fitting personalized ML models to each subject using Shapley values to determine their feature predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

Depression is one of the leading causes of mental illness.1 However, only half of people suffering from the condition receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat patients most likely to benefit from certain treatments.

A customized depression treatment plan can aid. Utilizing sensors on mobile phones and an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from the treatments they receive. Two grants totaling more than $10 million will be used to identify biological and behavior indicators of response.

The majority of research to date has focused on clinical and sociodemographic characteristics. These include factors that affect the demographics such as age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.

A few studies have utilized longitudinal data to predict mood of individuals. Many studies do not consider the fact that moods can be very different between individuals. Therefore, it is critical to develop methods that allow for the identification of different mood predictors for each person 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. This enables the team to develop algorithms that can detect various patterns of behavior and emotions that vary between individuals.

In addition to these modalities, the team also developed a machine-learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines the individual differences to produce a unique "digital genotype" 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 weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.

Predictors of symptoms

Depression what is the best treatment for anxiety and depression among the most prevalent causes of disability1 but is often underdiagnosed and undertreated2. Depression disorders are usually not treated because of the stigma associated with them, as well as the lack of effective interventions.

To assist in individualized treatment, it is important to identify predictors of symptoms. However, current prediction methods rely on clinical interview, which is not reliable and only detects a limited variety of characteristics related to depression.2

Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral patterns gathered from sensors on smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements as well as capture a wide variety of distinct behaviors and patterns that are difficult to record with interviews.

The study involved University of California Los Angeles students who had mild to severe depression symptoms who were taking part 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 support or clinical care depending on the degree of their depression. Those with a score on the CAT DI of 35 or 65 students were assigned online support via the help of a coach. Those with scores of 75 patients were referred to in-person psychotherapy.

At the beginning, participants answered a series of questions about their personal characteristics and psychosocial traits. The questions asked included education, age, sex and gender as well as financial status, marital status as well as whether they divorced or not, current suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also scored 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 those who received online support, and weekly for those who received in-person assistance.

Predictors of Treatment Reaction

A customized treatment for depression is currently a top research topic, and many studies aim at identifying predictors that help clinicians determine the most effective medication for each individual. In particular, pharmacogenetics identifies genetic variations that affect the way that the body processes antidepressants. This enables doctors to choose the medications that are most likely to work best for each patient, while minimizing the time and effort required in trial-and-error procedures and avoiding side effects that might otherwise hinder the progress of the patient.

Another promising approach is to develop predictive models that incorporate the clinical data with neural imaging data. These models can then be used to identify the most appropriate combination of variables that is predictive of a particular outcome, such as whether or not a particular medication is likely to improve symptoms and mood. These models can be used to determine the patient's response to treatment, allowing doctors to maximize the effectiveness.

A new generation of studies utilizes machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables to improve predictive accuracy. These models have shown to be useful in forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming popular in psychiatry, and it is expected that they will become the norm for future clinical practice.

In addition to the ML-based prediction models research into the mechanisms that cause depression continues. Recent research suggests that the disorder is linked with neurodegeneration in particular circuits. This theory suggests that the treatment for depression will be individualized based on targeted therapies that target these circuits to restore normal function.

Internet-based interventions are a way to achieve this. They can provide an individualized and tailored experience for patients. A study showed that a web-based program improved symptoms and improved quality life for MDD patients. A controlled, randomized study of an individualized treatment for depression treatment without antidepressants showed that a substantial percentage of patients experienced sustained improvement and fewer side consequences.

Predictors of Side Effects

A major obstacle in individualized depression treatment drugs treatment is predicting the antidepressant medications that will have minimal or no side effects. Many patients have a trial-and error approach, with several medications prescribed before finding one that is safe and effective. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and specific.

There are several variables that can be used to determine which antidepressant should be prescribed, such as gene variations, patient phenotypes such as ethnicity or gender, and the presence of comorbidities. To identify the most reliable and accurate predictors for a specific treatment, controlled trials that are randomized with larger numbers of participants will be required. This is because it may be more difficult to determine the effects of moderators or interactions in trials that only include a single episode per person instead of multiple episodes spread over a long period of time.

Additionally, the prediction of a patient's reaction to a particular medication is likely to require information on the symptom profile and comorbidities, in addition to the patient's prior subjective experience with tolerability and efficacy. At present, only a handful of easily measurable sociodemographic variables as well as clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

Many challenges remain when it comes to the use of pharmacogenetics to treat depression. It is crucial to be able to comprehend and understand the definition of the genetic factors that cause 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, should be considered with care. Pharmacogenetics could eventually, reduce stigma surrounding mental health Treatment resistant bipolar Depression and improve the quality of treatment. As with all psychiatric approaches it is essential to give careful consideration and implement the plan. For now, it is ideal to offer patients an array of depression medications that work and encourage them to speak openly with their doctor.