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Personalized Depression Treatment

For many suffering from depression, traditional therapy and medication isn't effective. Personalized treatment may be the answer.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to discover their feature predictors and reveal distinct features that are able to change mood over time.

Predictors of Mood

Depression is a major cause of mental illness around the world.1 Yet the majority of people affected receive treatment. In order to improve outcomes, healthcare professionals must be able to recognize and treat patients who have the highest likelihood of responding to specific treatments.

Personalized depression treatment is one way to do this. Using sensors for mobile phones as well as 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. Two grants totaling more than $10 million will be used to determine the biological and behavioral indicators of response.

The majority of research done to so far has focused on sociodemographic and clinical characteristics. These include factors that affect the demographics such as age, sex and education, clinical characteristics including symptoms severity and comorbidities and biological indicators such as neuroimaging and genetic variation.

Very few studies have used longitudinal data in order to predict mood in individuals. Many studies do not take into consideration the fact that moods can be very different between individuals. It is therefore important to develop methods that allow for the determination and quantification of the individual differences in mood predictors and treatment effects, for instance.

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 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 depression mood. The algorithm combines the individual differences to produce an individual "digital genotype" for each participant.

This digital phenotype has been associated with CAT DI scores which is a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is the most common cause of disability in the world1, however, it is often untreated and misdiagnosed. Depression disorders are rarely treated due to the stigma that surrounds them and the lack of effective treatments.

To aid in the development of a personalized treatment, it is essential to determine the predictors of symptoms. However, the current methods for predicting symptoms rely on clinical interview, which has poor reliability and only detects a small number of symptoms associated with depression.2

Machine learning can increase 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 can provide continuous, high-resolution measurements as well as capture a wide variety of unique behaviors and activity patterns that are difficult to record with interviews.

The study enrolled University of California Los Angeles (UCLA) students experiencing moderate to severe depression treatment depressive symptoms. participating in the Screening and Treatment for Anxiety and mild Depression Treatments (STAND) program29 developed under the UCLA post pregnancy depression treatment Grand Challenge. Participants were routed to online assistance or in-person clinics according to the severity of their depression. Participants with a CAT-DI score of 35 65 were given online support with an instructor and those with a score 75 patients were referred for in-person 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, marital status, financial status, whether they were divorced or not, their current suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used to assess the severity of depression symptoms on a scale of 0-100. The CAT DI assessment was carried out every two weeks for those who received online support and weekly for those who received in-person support.

Predictors of Treatment Reaction

A customized treatment for depression is currently a top research topic, and many studies aim at identifying predictors that will enable clinicians to determine the most effective drugs for each individual. Pharmacogenetics in particular uncovers genetic variations that affect the way that our bodies process drugs. This allows doctors select medications that are most likely to work for every patient, minimizing the time and effort needed for trial-and error treatments and avoid any negative side consequences.

Another promising method is to construct models for prediction using multiple data sources, including clinical information and neural imaging data. These models can be used to identify which variables are most predictive of a specific outcome, such as whether a medication can help with symptoms or mood. These models can be used to predict the patient's response to a treatment, which will help doctors to maximize the effectiveness of their treatment.

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

The study of depression's underlying mechanisms continues, in addition to predictive models based on ML. Recent findings suggest that the disorder is associated with neural dysfunctions that affect specific circuits. This theory suggests that a individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.

Internet-based interventions are a way to achieve this. They can provide a more tailored and individualized experience for patients. For example, one study found that a web-based program was more effective than standard care in reducing symptoms and ensuring an improved quality of life for patients suffering from MDD. Furthermore, a randomized controlled study of a personalised treatment for depression demonstrated steady improvement and decreased adverse effects in a significant percentage of participants.

Predictors of Side Effects

A major challenge in personalized depression treatment involves identifying and predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed a variety of drugs before they find a drug that is effective and tolerated. Pharmacogenetics offers a fresh and exciting way to select antidepressant medicines that are more effective and specific.

There are a variety of predictors that can be used to determine the antidepressant to be prescribed, including gene variations, patient phenotypes like gender or ethnicity and comorbidities. However it is difficult to determine the most reliable and reliable factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials living with treatment resistant depression much larger samples than those typically enrolled in clinical trials. This is due to the fact that the identification of interactions or moderators could be more difficult in trials that only consider a single episode of treatment per person, rather than multiple episodes of treatment over time.

Additionally the prediction of a patient's reaction to a specific medication to treat anxiety and depression is likely to require information about the symptom profile and comorbidities, as well as the patient's previous experience of its tolerability and effectiveness. Currently, only a few easily identifiable sociodemographic variables and clinical variables seem to be reliably related to response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

Many issues remain to be resolved in the application of pharmacogenetics in the treatment of depression. First, it is important to have a clear understanding and definition of the genetic mechanisms that underlie depression, as well as an accurate definition of an accurate predictor of treatment response. In addition, ethical concerns such as privacy and the responsible use of personal genetic information, must be carefully considered. Pharmacogenetics could, in the long run help reduce stigma around treatments for mental illness and improve the quality of treatment. However, as with any other psychiatric treatment, careful consideration and application is essential. For now, the best course of action is to offer patients an array of effective depression medication options and encourage them to speak freely with their doctors about their concerns and experiences.