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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapy and medication isn't effective. Personalized treatment could be the solution.

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. The results revealed distinct characteristics that changed mood in a predictable manner 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 the outcomes, doctors must be able to identify and treat patients most likely to respond to certain treatments.

Personalized depression treatment is one method to achieve this. Utilizing sensors on 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 make use of these techniques to determine biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.

The majority of research conducted to date has focused on sociodemographic and clinical characteristics. These include demographic factors such as age, gender and education, clinical characteristics including symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

Few studies have used longitudinal data to determine mood among individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is crucial to devise methods that permit the identification and quantification 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. This enables the team to develop algorithms that can identify various patterns of behavior and emotions that vary between individuals.

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

The digital phenotype was associated with CAT-DI scores, which is a psychometrically validated severity scale for symptom severity. The correlation was low however (Pearson r = 0,08, P-value adjusted for BH = 3.55 10 03) and varied widely between individuals.

Predictors of symptoms

Depression is a leading cause of disability in the world1, however, it is often not properly diagnosed and treated. In addition, a lack of effective interventions and stigmatization associated with depressive disorders prevent many individuals from seeking help.

To facilitate personalized treatment, identifying factors that predict the severity of symptoms is crucial. However, current prediction methods depend on the clinical interview which is unreliable and only detects a tiny number of symptoms related to depression.2

Using machine learning to blend continuous digital behavioral phenotypes that are captured by smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, CAT-DI) along with other indicators of severity of symptoms could improve diagnostic accuracy and increase the effectiveness of treatment for depression. Digital phenotypes permit continuous, high-resolution measurements and capture a variety of unique behaviors and activity patterns that are difficult to capture with interviews.

The study involved University of California Los Angeles (UCLA) students experiencing moderate to severe depressive symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical treatment according to the severity of their depression. Those with a CAT-DI score of 35 65 were assigned online support with a coach and those with a score 75 were routed to clinics in-person for psychotherapy.

At baseline, participants provided an array of questions regarding their personal demographics and psychosocial characteristics. The questions included age, sex and education and financial status, marital status, whether they were divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. The CAT-DI was used for assessing the severity of depression treatment cbt symptoms on a scale ranging from zero to 100. The CAT-DI assessment was carried out every two weeks for participants who received online support and weekly for those who received in-person assistance.

Predictors of Treatment Reaction

Research is focusing on personalized depression treatment. Many studies are aimed at finding predictors, which can aid clinicians in identifying the most effective drugs for each person. Pharmacogenetics, for instance, is a method of identifying genetic variations that affect the way that our bodies process drugs. 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 trials and errors, while avoiding side effects that might otherwise slow progress.

Another option is to create prediction models that combine information from clinical studies and neural imaging data. These models can be used to determine the most effective combination of variables that are predictors of a specific outcome, like whether or not a drug is likely to improve the mood and symptoms. These models can be used to determine a patient's response to a cbt treatment for depression they are currently receiving which allows doctors to maximize the effectiveness of treatment currently being administered.

A new generation of machines employs machine learning techniques such as algorithms for classification and supervised learning such as regularized logistic regression, and tree-based techniques to combine the effects from multiple variables and increase the accuracy of predictions. These models have been proven to be effective in predicting treatment outcomes, such as response to antidepressants. These methods are becoming popular in psychiatry and it is likely that they will become the standard for the future of clinical practice.

In addition to prediction models based on ML, research into the mechanisms behind depression is continuing. 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 function to these circuits.

One way to do this is by using internet-based programs which can offer an personalized and customized experience for patients. One study discovered that a web-based treatment was more effective than standard care in reducing symptoms and ensuring the best quality of life for people suffering from MDD. A controlled, randomized study of a personalized treatment for depression found that a significant number of participants experienced sustained improvement and fewer side effects.

Predictors of side effects

A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will cause the least amount of side effects or none at all. Many patients experience a trial-and-error approach, using various medications prescribed before finding one that is effective and tolerable. Pharmacogenetics provides an exciting new way to take 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 patients such as ethnicity or gender, and comorbidities. To determine the most reliable and valid predictors for a particular treatment, random controlled trials with larger numbers of participants will be required. This is due to the fact that the identification of interactions or moderators could be more difficult in trials that only take into account a single episode of treatment per person, rather than multiple episodes of treatment over time.

Additionally to that, predicting a patient's reaction will likely require information about the comorbidities, symptoms profiles and the patient's personal experience of tolerability and effectiveness. There are currently only a few easily measurable sociodemographic variables as well as clinical variables appear to be consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

Many challenges remain when it comes to the use of pharmacogenetics for depression treatment. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that underlie untreatable depression, and a clear definition of an accurate indicator of the response to treatment. Ethics such as privacy and the ethical use of genetic information must also be considered. Pharmacogenetics can, in the long run reduce stigma associated with treatments for mental Depression treatment illness and improve the quality of treatment. As with all psychiatric approaches, it is important to give careful consideration and implement the plan. At present, the most effective option is to offer patients an array of effective medications for depression and encourage them to talk openly with their doctors about their experiences and concerns.