Watch Out: What Personalized Depression Treatment Is Taking Over And What To Do About It

Personalized Depression Treatment

Traditional therapies and medications are not effective for a lot of patients suffering from depression. Personalized magnetic treatment for depression could be the solution.

Cue is a digital intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions that improve mental health. We analyzed the best-fitting personalized ML models to each person, using Shapley values to discover their characteristic predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is the leading cause of mental illness around the world.1 Yet the majority of people affected receive treatment. To improve outcomes, healthcare professionals must be able to recognize and treat patients who have the highest probability of responding to certain treatments.

Personalized depression treatment is one method to achieve this. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most from specific treatments. They make use of sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence, and other digital tools. Two grants were awarded that total over $10 million, they will make use of these techniques to determine biological and behavioral predictors of the response to antidepressant medication and psychotherapy.

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

While many of these variables can be predicted from the information in medical records, very few studies have used longitudinal data to determine the factors that influence mood in people. Many studies do not take into consideration the fact that mood varies significantly between individuals. It is therefore important to devise methods that permit the analysis and measurement of personal 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 is able to develop algorithms to recognize patterns of behaviour and emotions that are unique to each individual.

The team also devised an algorithm for machine learning to create dynamic predictors for each person's depression mood. The algorithm combines the individual differences to produce an individual "digital genotype" for each participant.

This digital phenotype was found to be associated with CAT DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was not strong, however (Pearson r = 0,08; BH adjusted P-value 3.55 10 03) and varied greatly among individuals.

Predictors of Symptoms

Depression is the most common reason for disability across the world, but it is often untreated and misdiagnosed. In addition an absence of effective treatments and stigmatization associated with depression disorders hinder many people from seeking help.

To help with personalized treatment, it is crucial to identify predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which aren't reliable and only reveal a few symptoms associated with deep depression treatment.

Machine learning is used to integrate continuous digital behavioral phenotypes of a person captured by smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory CAT-DI) along with other indicators of severity of symptoms can increase the accuracy of diagnostics and treatment efficacy for depression. Digital phenotypes can be used to are able to capture a variety of unique actions and behaviors that are difficult to record through interviews, and allow for continuous, high-resolution measurements.

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 treatment drugs (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment in accordance with their severity of depression and treatment. Patients with a CAT DI score of 35 or 65 were assigned online support by a coach and those with a score 75 were routed to in-person clinical care for psychotherapy.

Participants were asked a series of questions at the beginning of the study regarding their psychosocial and demographic characteristics as well as their socioeconomic status. The questions covered education, age, sex and gender and marital status, financial status, whether they were divorced or not, their current suicidal ideas, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also scored their level of depression symptom 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 every week for those who received in-person care.

Predictors of Treatment Response

Personalized depression treatment no medication treatment is currently a research priority, and many studies aim at identifying predictors that will help clinicians determine the most effective drugs for each person. Particularly, pharmacogenetics can identify genetic variants that influence the way that the body processes antidepressants. This lets doctors select the medication that will likely work best for every patient, minimizing time and effort spent on trial-and error treatments and eliminating any adverse negative effects.

Another promising method is to construct prediction models using multiple data sources, such as clinical information and neural imaging data. These models can be used to determine the best combination of variables predictive of a particular outcome, such as whether or not a medication will improve the mood and symptoms. These models can also be used to predict the patient's response to an existing treatment, allowing doctors to maximize the effectiveness of their treatment currently being administered.

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

The study of depression's underlying mechanisms continues, as well as ML-based predictive models. Recent research suggests that the disorder is associated with neurodegeneration in particular circuits. This theory suggests that an individualized treatment for depression will be based on targeted therapies that restore normal function to these circuits.

One way to do this is through internet-delivered interventions that can provide a more individualized and personalized experience for patients. For example, one study found that a program on the internet was more effective than standard treatment in reducing symptoms and ensuring a better quality of life for people suffering from MDD. A controlled, randomized study of an individualized treatment for depression showed that a substantial percentage of patients saw improvement over time and fewer side negative effects.

Predictors of side effects

A major challenge in personalized depression treatment is predicting which antidepressant medications will have very little or no side effects. Many patients have a trial-and error method, involving a variety of medications prescribed until they find one that is safe and effective. Pharmacogenetics offers a fresh and exciting method to choose antidepressant medicines that are more effective and specific.

There are several variables that can be used to determine the antidepressant that should be prescribed, including gene variations, phenotypes of the patient such as ethnicity or gender, and co-morbidities. To identify the most reliable and reliable predictors of a specific treatment, random controlled trials with larger numbers of participants will be required. This is due to the fact that it can be more difficult to identify moderators or interactions in trials that contain only one episode per person instead of multiple episodes spread over a long period of time.

Additionally the estimation of a patient's response to a particular medication is likely to require information on symptoms and comorbidities in addition to the patient's personal experience of its tolerability and effectiveness. There are currently only a few easily measurable sociodemographic variables as well as clinical variables are reliably related to response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

Many issues remain to be resolved in the application of pharmacogenetics for depression treatment. First, it is essential to be able to comprehend and understand the definition of the genetic factors that cause depression, and a clear definition of an accurate indicator of the response to treatment. In addition, ethical concerns 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 treatment outcomes. However, as with all approaches to psychiatry, careful consideration and implementation is required. At present, it's best to offer patients various depression medications that are effective and urge patients to openly talk with their physicians.