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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medication isn't effective. A customized treatment could be the answer.

Cue is a digital intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions designed to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and reveal distinct features that deterministically change mood with time.

Predictors of Mood

Depression is a leading cause of mental illness in the world.1 Yet only half of those with the condition receive treatment. In order to improve outcomes, clinicians need to be able to recognize and treat patients with the highest chance of responding to certain treatments.

Personalized depression treatment can help. By 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 predict which patients will benefit from the treatments they receive. With two grants totaling more than $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 done to so far has focused on sociodemographic and clinical characteristics. These include demographic factors like age, sex and education, clinical characteristics including symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

While many of these factors can be predicted from the information available in medical records, only a few studies have employed longitudinal data to study the factors that influence mood in people. Few also take into account the fact that mood can vary significantly between individuals. Therefore, it is critical to create methods that allow the recognition of different mood predictors for each person and the effects of treatment.

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 allows the team to create algorithms that can identify various patterns of behavior and emotion that vary between individuals.

The team also developed a machine learning algorithm to model dynamic predictors for each person's mood for depression. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.

Predictors of Symptoms

Depression is the most common cause of disability in the world1, but it is often misdiagnosed and untreated2. In addition an absence of effective interventions and stigmatization associated with depressive disorders prevent many from seeking treatment.

To aid in the development of a personalized treatment, it is crucial to identify the factors that predict symptoms. However, the current methods for predicting symptoms depend on the clinical interview which is unreliable and only detects a limited number of symptoms that are associated with depression.2

Using machine learning to blend continuous digital behavioral phenotypes of a person captured through smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of severity of symptoms could improve diagnostic accuracy and increase the effectiveness of treatment for depression treatment facility near me. Digital phenotypes can be used to capture a large number of unique behaviors and activities, which are difficult to record through interviews, and allow for high-resolution, continuous measurements.

The study involved University of California Los Angeles students with moderate to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online assistance or medical care depending on the severity of their depression. Those with a CAT-DI score of 35 or 65 were assigned to online support with a peer coach, while those with a score of 75 were sent to in-person clinics for psychotherapy.

At the beginning, participants answered the answers to a series of questions concerning their personal demographics and psychosocial characteristics. The questions included education, age, sex and gender, financial status, marital status and whether they were divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. The CAT-DI was used to rate the severity of depression symptoms on a scale ranging from zero to 100. CAT-DI assessments were conducted each week for those who received online support and weekly for those receiving in-person support.

Predictors of Treatment Reaction

A customized treatment for depression is currently a research priority, and many studies aim at identifying predictors that help clinicians determine the most effective medications for each individual. Pharmacogenetics, in particular, uncovers genetic variations that affect the way that our bodies process drugs. This lets doctors choose the medications that are likely to be the most effective for each patient, while minimizing the time and effort needed for trial-and error treatments and avoid any negative side effects.

Another approach that is promising is to build models of prediction using a variety of data sources, including the clinical information with neural imaging data. These models can be used to identify the most effective combination of variables predictive of a particular outcome, such as whether or not a particular medication will improve mood and symptoms. These models can be used to determine a patient's response to an existing treatment and help doctors maximize the effectiveness of their current treatment.

A new generation of studies uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to blend the effects of several variables and improve the accuracy of predictive. These models have proven to be useful for the prediction of treatment outcomes like the response to antidepressants. These methods are becoming more popular in psychiatry and will likely be the norm in future treatment.

Research into the underlying causes of depression continues, in addition to ML-based predictive models. Recent findings suggest that depression is connected to the malfunctions of certain neural networks. 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 offer a more individualized and personalized experience for patients. A study showed that an internet-based program helped improve symptoms and improved quality life for MDD patients. In addition, a controlled randomized study of a customized approach to depression treatment showed steady improvement and decreased adverse effects in a significant percentage of participants.

Predictors of adverse effects

A major obstacle in individualized depression treatment is predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant medications that is more effective and precise.

Several predictors may be used to determine which antidepressant to prescribe, including gene variations, phenotypes of patients (e.g., sex or ethnicity) and the presence of comorbidities. To identify the most reliable and accurate predictors for a particular treatment, controlled trials that are randomized with larger samples will be required. This is due to the fact that it can be more difficult to determine interactions or moderators in trials that comprise only a single episode per person rather than multiple episodes over a period of time.

In addition, predicting a patient's response will likely require information on comorbidities, symptom profiles and the patient's own perception of the effectiveness and tolerability. Presently, only a handful of easily measurable sociodemographic and clinical variables seem to be reliable in predicting response to MDD like age, gender, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many obstacles to overcome. First Line Treatment For Depression And Anxiety is a thorough understanding of the genetic mechanisms is essential, as is an understanding of what is a reliable predictor of lithium treatment for depression response. In addition, ethical issues like privacy and the appropriate use of personal genetic information, must be carefully considered. In the long-term the use of pharmacogenetics could be a way to lessen the stigma that surrounds mental health care and improve the treatment outcomes for patients with depression. Like any other psychiatric treatment it is crucial to give careful consideration and implement the plan. The best method is to provide patients with an array of effective depression treatment without meds medication options and encourage them to talk freely with their doctors about their experiences and concerns.