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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 transforms sensors that are passively gathered from smartphones into personalized micro-interventions to improve mental health. We examined the most effective-fitting personalized ML models to each subject, using Shapley values to determine their characteristic 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 across the world.1 Yet the majority of people suffering from the condition receive treatment. To improve outcomes, clinicians must be able identify and treat patients most likely to respond to certain treatments.

Personalized depression treatment is one method of doing this. By using sensors on mobile phones, 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 responses to antidepressant medications as well as psychotherapy.

The majority of research to the present has been focused on clinical and sociodemographic characteristics. These include factors that affect the demographics like age, sex and education, clinical characteristics such as symptoms 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 mood can vary significantly between individuals. Therefore, it is critical to create methods that allow the determination of different mood predictors for each person and treatments effects.

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 develop algorithms that can detect different patterns of behavior and emotions that are different between people.

The team also devised an algorithm for machine learning to model dynamic predictors for the mood of each person's depression treatment options. The algorithm blends the individual characteristics to create an individual "digital genotype" for each participant.

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

Predictors of symptoms

Depression is a leading cause of disability around the world, but it is often misdiagnosed and untreated2. Depressive disorders are often not treated due to the stigma associated with them, as well as the lack of effective interventions.

To help with personalized treatment, it is crucial to determine the predictors of symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which is not reliable and only detects a small number of features associated with depression.2

Using machine learning to blend continuous digital behavioral phenotypes captured by smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) together with other predictors of severity of symptoms can increase the accuracy of diagnostics and the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements. They also capture a variety of distinctive behaviors and activity patterns that are difficult to document through interviews.

The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were sent online for assistance or medical care based on the severity of their depression. Patients with a CAT DI score of 35 65 were assigned online support via the help of a peer coach. those with a score of 75 patients were referred for in-person psychotherapy.

Participants were asked a series of questions at the beginning of the study concerning their psychosocial and demographic characteristics as well as their socioeconomic status. These included sex, age and education, as well as work and financial situation; whether they were divorced, married or single; their current suicidal thoughts, intentions or attempts; and the frequency at that they consumed alcohol. The CAT-DI was used for assessing the severity of atypical depression treatment-related symptoms on a scale from 0-100. CAT-DI assessments were conducted every other week for the participants that received online support, and weekly for those receiving in-person care.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic, and many studies aim to identify predictors that help clinicians determine the most effective medication to treat anxiety and depression 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 medications that are likely to be most effective for each patient, while minimizing the time and effort required in trial-and-error procedures and eliminating any side effects that could otherwise hinder progress.

Another approach that is promising is to build prediction models that combine the clinical data with neural imaging data. These models can be used to determine the most appropriate combination of variables predictive of a particular outcome, such as whether or not a medication is likely to improve mood and symptoms. These models can be used to predict the patient's response to a treatment, allowing doctors maximize the effectiveness.

A new type of research uses machine learning methods like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables and increase predictive accuracy. These models have been proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to be the norm in future clinical practice.

In addition to the ML-based prediction models research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is linked to the dysfunctions of specific neural networks. This suggests that an the treatment for depression will be individualized based on targeted therapies that target these circuits to restore normal functioning.

Internet-based-based therapies can be an effective method to accomplish this. They can provide more customized and personalized experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality of life for MDD patients. Furthermore, a randomized controlled study of a personalised approach to treating depression showed an improvement in symptoms and fewer side effects in a significant proportion of participants.

Predictors of Side Effects

In the treatment of depression, a major challenge is predicting and determining the antidepressant that will cause no or minimal negative side effects. Many patients are prescribed a variety of medications before settling on a treatment that is effective and tolerated. Pharmacogenetics offers a fascinating new avenue for a more effective and precise approach to choosing antidepressant medications.

There are many predictors that can be used to determine the antidepressant to be prescribed, such as gene variations, phenotypes of the patient such as gender or ethnicity, and co-morbidities. However, identifying the most reliable and reliable predictors for a particular treatment is likely to require controlled, randomized trials with much larger samples than those that are typically part of clinical trials. This is because it could be more difficult to identify interactions or moderators in trials that only include one episode per participant instead of multiple episodes spread over a long period of time.

Furthermore, the prediction of a patient's response to a particular medication will likely also require information about comorbidities and symptom profiles, as well as the patient's previous experience of its tolerability and effectiveness. At present, only a few easily identifiable sociodemographic and clinical variables are believed to be correlated with the response to MDD like age, gender race/ethnicity, SES, BMI, the presence of alexithymia and the severity of depressive symptoms.

Many issues remain to be resolved when it comes to the use of pharmacogenetics in the treatment of morning depression treatment. It is crucial to have a clear understanding and definition of the genetic factors that cause depression, and an accurate definition of a reliable predictor of treatment response. In addition, ethical issues, such as privacy and the ethical use of personal genetic information must be carefully considered. The use of pharmacogenetics may be able to, over the long term, reduce stigma surrounding mental health treatments and improve treatment outcomes. However, as with any approach to psychiatry careful consideration and planning is necessary. For now, it is best to offer patients an array of depression medications that are effective and encourage them to speak openly with their physicians.