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Personalized Depression Treatment
Traditional therapies and medications do not work for many people suffering from depression. The individual approach to treatment could be the answer.
Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalised micro-interventions for improving mental health. We examined the most effective-fitting personalized ML models for each individual using Shapley values, in order to understand their feature predictors. This revealed distinct features that were deterministically changing mood over time.
Predictors of Mood
Depression is the leading cause of mental illness around the world.1 Yet only half of those suffering from the condition receive treatment. To improve the outcomes, clinicians need to be able to identify and treat patients with the highest chance of responding to certain treatments.
Personalized depression treatment can help. Using sensors for mobile phones and an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are developing new methods to predict which patients will benefit from which treatments. With two grants awarded totaling over $10 million, they will employ these technologies to identify the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.
The majority of research on factors that predict depression treatment effectiveness – https://morrison-burnette-3.technetbloggers.de/20-insightful-quotes-about-depression-treatment-centers, has been focused on sociodemographic and clinical characteristics. These include factors that affect the demographics such as age, gender and education, clinical characteristics such as the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.
Very few studies have used longitudinal data in order to predict mood of individuals. Few studies also take into consideration the fact that moods can vary significantly between individuals. Therefore, it is important to develop methods which allow for the identification and quantification 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. The team can then develop algorithms to identify patterns of behavior and emotions that are unique to each person.
The team also created a machine-learning algorithm that can identify dynamic predictors of each person’s mood for depression. The algorithm combines the individual characteristics to create a unique “digital genotype” for each participant.
This digital phenotype was found to be associated with CAT-DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson’s r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.
Predictors of symptoms
Depression is one of the world’s leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated because of the stigma that surrounds them and the absence of effective treatments.
To aid in the development of a personalized treatment, it is important to determine the predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which are not reliable and only reveal a few features associated with depression.
Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral patterns gathered from sensors on smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements and capture a wide variety of distinct behaviors and patterns that are difficult to capture using interviews.
The study included University of California Los Angeles students with moderate to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care according to the severity of their depression. Those with a CAT-DI score of 35 65 were assigned to online support with an online peer coach, whereas those who scored 75 were sent to clinics in-person for psychotherapy.
Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial traits. These included age, sex and education, as well as work and financial situation; whether they were partnered, divorced or single; the frequency of suicidal ideation, intent or attempts; as well as the frequency with the frequency they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale of 100 to. CAT-DI assessments were conducted every week for those that received online support, and weekly for those receiving in-person care.
Predictors of Treatment Response
Research is focusing on personalized depression treatment. Many studies are focused on finding predictors that can help doctors determine the most effective drugs for each person. Pharmacogenetics in particular identifies genetic variations that determine how the body’s metabolism reacts to drugs. This enables doctors to choose medications that are likely to be most effective for each patient, while minimizing the time and effort required in trial-and-error treatments and eliminating any side effects that could otherwise hinder advancement.
Another promising method is to construct models of prediction using a variety of data sources, such as the clinical information with neural imaging data. These models can be used to identify the best combination of variables predictive of a particular outcome, like whether or not a particular medication will improve mood and symptoms. These models can be used to predict the response of a patient to treatment, allowing doctors to maximize the effectiveness.
A new generation employs machine learning methods such as 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 proven to be useful for predicting treatment outcomes such as the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the standard of future clinical practice.
In addition to the ML-based prediction models research into the underlying mechanisms of depression continues. Recent research suggests that the disorder is associated with neural dysfunctions that affect specific circuits. This theory suggests that an individualized treatment for depression will depend on targeted therapies that restore normal functioning to these circuits.
Internet-delivered interventions can be a way to accomplish this. They can provide an individualized and tailored experience for patients. One study found that an internet-based program helped improve symptoms and provided a better quality life for MDD patients. In addition, a controlled randomized study of a personalised approach to treating depression showed steady improvement and decreased side effects in a significant number of participants.
Predictors of adverse effects
A major obstacle in individualized depression treatment is predicting which antidepressant medications will cause minimal or no side effects. Many patients experience a trial-and-error approach, using various medications prescribed until they find one that is safe and effective. Pharmacogenetics provides an exciting new method for an efficient and targeted method of selecting antidepressant therapies.
A variety of predictors are available to determine which antidepressant to prescribe, including gene variants, phenotypes of patients (e.g., sex or ethnicity) and comorbidities. To identify the most reliable and valid predictors for a particular treatment for panic attacks and depression, controlled trials that are randomized with larger numbers of participants will be required. This is because the identifying of interaction effects or moderators can be a lot more difficult in trials that focus on a single instance of treatment per person instead of multiple episodes of treatment over time.
Additionally, the prediction of a patient’s response to a specific medication will also likely require information about symptoms and comorbidities in addition to the patient’s previous experience with tolerability and efficacy. Presently, only a handful of easily measurable sociodemographic and clinical variables appear to be correlated with the response to MDD like gender, age, race/ethnicity and SES BMI and the presence of alexithymia, and the severity of depressive symptoms.
The application of pharmacogenetics in treatment for depression is in its early stages, and many challenges remain. First, it is important to have a clear understanding and definition of the genetic mechanisms that underlie depression treatments near me, as well as an accurate definition of an accurate indicator of the response to treatment. Additionally, ethical issues such as privacy and the appropriate use of personal genetic information should be considered with care. The use of pharmacogenetics may eventually help reduce stigma around mental health treatment and improve treatment outcomes. But, like any other psychiatric treatment, careful consideration and application is required. For now, it is recommended holistic ways to treat depression provide patients with an array of depression medications that are effective and urge them to speak openly with their physicians.
