Personalized Depression Treatment

For many people gripped by depression, traditional therapies and medication isn't effective. A customized treatment could be the answer.

Cue is an intervention platform that converts sensors that are passively gathered from smartphones into customized micro-interventions for improving mental health. We looked at the best-fitting personal ML models to each subject, 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 a leading cause of mental illness around the world.1 Yet, only half of those suffering from the condition receive treatment. To improve outcomes, clinicians need to be able to identify and treat patients with the highest probability of responding to certain treatments.

Personalized depression treatment can help. Using mobile phone sensors, 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 discover biological and behavioral indicators of response.

The majority of research to date has focused on sociodemographic and clinical characteristics. These include demographics like age, gender and education and clinical characteristics such as symptom severity, comorbidities and biological markers.

While many of these factors can be predicted from the information available in medical treatment for depression records, very few studies have used longitudinal data to explore predictors of mood in individuals. Many studies do not take into account the fact that moods can vary significantly between individuals. Therefore, it is crucial to develop methods that permit the determination and quantification of the 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 will then create algorithms to detect patterns of behaviour and emotions that are unique to each individual.

The team also created a machine learning algorithm to model dynamic predictors for the mood of each person's depression. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.

This digital phenotype has been associated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was not strong, however (Pearson r = 0,08; P-value adjusted for BH = 3.55 x 10 03) and varied widely among individuals.

Predictors of symptoms

Depression is the most common cause of disability around the world1, but it is often not properly diagnosed and treated. Depression disorders are usually not treated because of the stigma attached to them and the absence of effective interventions.

To aid in the development of a personalized treatment, it is crucial to identify predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which are not reliable and only identify a handful of symptoms associated with depression.

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

The study comprised University of California Los Angeles students with mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care based on the severity of their depression. Patients with a CAT DI score of 35 or 65 were allocated online support via an online peer coach, whereas those who scored 75 patients were referred to psychotherapy in person.

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 status; whether they were divorced, married or single; the frequency of suicidal ideation, intent, or attempts; and the frequency with that they consumed alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale from 0-100. The CAT-DI tests were conducted every other week for the participants that received online support, and every week for those who received in-person support.

Predictors of Treatment Response

Research is focusing on personalized depression treatment history (slot-Thrane-2.blogbright.net) shock treatment for depression. Many studies are aimed at identifying predictors, which will help doctors determine the most effective drugs to treat each patient. Pharmacogenetics, for instance, is a method of identifying genetic variations that affect how the body's metabolism reacts to drugs. This lets doctors select the medication that will likely work best for each patient, reducing time and effort spent on trial-and-error treatments and eliminating any adverse negative effects.

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

A new generation of machines employs machine learning methods such as algorithms for classification and supervised learning, regularized logistic regression and tree-based techniques to combine the effects of several variables and increase the accuracy of predictions. These models have shown to be useful for 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 standard for the future of clinical practice.

The study of depression's underlying mechanisms continues, as do predictive models based on ML. Recent findings suggest that depression is connected to the malfunctions of certain neural networks. This theory suggests that a individualized treatment for depression will depend on targeted therapies that restore normal functioning to these circuits.

One method to achieve this is through internet-delivered interventions which can offer an individualized and personalized experience for patients. A study showed that an internet-based program helped improve symptoms and provided a better quality of life for MDD patients. Additionally, a randomized controlled study of a personalised treatment for depression demonstrated sustained improvement and reduced side effects in a significant proportion of participants.

Predictors of Side Effects

In the treatment of depression one of the most difficult aspects is predicting and determining which antidepressant medication will have very little or no side negative effects. Many patients take a trial-and-error approach, with several medications being prescribed before settling on one that is safe and effective. Pharmacogenetics offers a new and exciting way to select antidepressant drugs that are more effective and precise.

A variety of predictors are available to determine the best antidepressant to prescribe, including gene variants, phenotypes of patients (e.g., sex or ethnicity) and the presence of comorbidities. To determine the most reliable and valid predictors for a specific treatment, randomized controlled trials with larger numbers of participants will be required. This is due to the fact that it can be more difficult to determine moderators or interactions in trials that only include a single episode per person rather than multiple episodes over time.

Additionally, the prediction of a patient's reaction to a particular medication will likely also need to incorporate information regarding the symptom profile and comorbidities, in addition to the patient's previous experience of its tolerability and effectiveness. Presently, only a handful of easily measurable sociodemographic and clinical variables appear to be reliably associated with response to MDD factors, including gender, age race/ethnicity, BMI and the presence of alexithymia, and the severity of depression symptoms.

The application of pharmacogenetics to treatment for depression is in its beginning stages, and many challenges remain. First, it is important to be able to comprehend and understand the definition of the genetic mechanisms meds that treat depression and anxiety underlie depression, as well as a clear definition of a reliable predictor of treatment response. Ethics such as privacy and the responsible use of genetic information must also be considered. In the long term, pharmacogenetics may offer a chance to lessen the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. But, like any approach to psychiatry careful consideration and planning is essential. In the moment, it's recommended to provide patients with various depression medications that are effective and urge patients to openly talk with their physicians.