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Personalized Depression Treatment

Traditional therapies and medications are not effective for a lot of people who are depressed. The individual approach to treatment depression could be the solution.

Cue is an intervention platform for digital devices that converts passively collected smartphone sensor data into personalized micro-interventions designed to improve mental health. We looked at the best-fitting personal ML models for each individual, 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 in the world.1 Yet the majority of people with the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat patients most likely to benefit from certain treatments.

The treatment of depression can be personalized to help. 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 awarded totaling over $10 million, they will employ these tools to identify the biological Epilepsy and depression treatment behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

The majority of research into predictors of depression treatment effectiveness has been focused on sociodemographic and clinical characteristics. These include demographic variables such as age, sex and education, clinical characteristics including symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

While many of these aspects can be predicted from the information in medical records, only a few studies have used longitudinal data to determine predictors of mood in individuals. Many studies do not take into consideration the fact that mood can be very different between individuals. Therefore, it is crucial 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. This allows the team to create algorithms that can identify various patterns of behavior and emotions that differ between individuals.

In addition to these modalities the team developed a machine-learning algorithm meds that treat depression and anxiety models the dynamic factors that determine a person's depressed mood. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

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

Predictors of symptoms

Depression is the leading cause of disability around the world1, however, 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 allow for individualized treatment in order to provide a more personalized treatment, identifying factors that predict the severity of symptoms is crucial. The current methods for predicting symptoms rely heavily on clinical interviews, which aren't reliable and only detect a few features associated with depression.

Machine learning can increase the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements and capture a variety of distinct behaviors and patterns that are difficult to capture through interviews.

The study included University of California Los Angeles students with mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online assistance or in-person clinics in accordance with their severity of depression. Participants who scored a high on the CAT-DI scale of 35 or 65 were assigned to online support with the help of a peer coach. those who scored 75 were routed to in-person clinics for psychotherapy.

At the beginning of the interview, participants were asked a series of questions about their personal characteristics and psychosocial traits. These included sex, age education, work, and financial situation; whether they were divorced, partnered, or single; current suicidal thoughts, intentions or attempts; as well as the frequency with which they drank alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale of 100 to. CAT-DI assessments were conducted every other week for the participants who received online support and once a week for those receiving in-person care.

Predictors of Treatment Response

A customized treatment for depression is currently a research priority and a lot of studies are aimed at identifying predictors that enable clinicians to determine the most effective drugs for each patient. Pharmacogenetics in particular identifies genetic variations that determine how the human body metabolizes drugs. This enables doctors to choose the medications that are most likely to be most effective for each patient, minimizing the time and effort in trials and errors, while avoiding side effects that might otherwise hinder advancement.

Another approach that is promising is to build models of prediction using a variety of data sources, such as data from clinical studies and neural imaging data. These models can be used to identify the most effective combination of variables predictors of a specific outcome, such as whether or not a particular medication will improve mood and symptoms. These models can be used to predict the response of a patient to treatment, allowing doctors to maximize the effectiveness of their treatment.

A new era of research uses machine learning methods such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and increase predictive accuracy. These models have been demonstrated to be effective in predicting treatment depression outcomes, such as response to antidepressants. These techniques are becoming increasingly popular in psychiatry and will likely be the norm in future treatment.

In addition to the ML-based prediction models The study of the mechanisms behind depression is continuing. Recent findings suggest that depression is linked to dysfunctions in specific neural networks. This theory suggests that an individualized treatment for depression will depend on targeted therapies that restore normal function to these circuits.

Internet-based-based therapies can be an option to achieve this. They can offer a more tailored and individualized experience for patients. For instance, one study found that a web-based program was more effective than standard care in alleviating symptoms and ensuring a better quality of life for those suffering from MDD. Furthermore, a randomized controlled study of a customized approach to depression treatment showed steady improvement and decreased adverse effects in a significant number of participants.

Predictors of Side Effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will cause minimal or no side effects. Many patients experience a trial-and-error method, involving a variety of medications prescribed before finding one that is safe and effective. Pharmacogenetics is an exciting new method for an efficient and specific method of selecting antidepressant therapies.

There are many variables that can be used to determine which antidepressant should be prescribed, such as gene variations, phenotypes of the patient such as gender or ethnicity and comorbidities. However it is difficult to determine the most reliable and valid predictive factors for a specific treatment is likely to require controlled, randomized trials with significantly larger numbers of participants than those that are typically part of clinical trials. This is because the identifying of interactions or moderators can be a lot more difficult in trials that only take into account a single episode of treatment per patient instead of multiple episodes of treatment over a period of time.

Additionally the estimation of a patient's response to a particular medication will likely also require information on symptoms and comorbidities in addition to the patient's personal experience of its tolerability and effectiveness. At present, only a few easily measurable sociodemographic and clinical variables are believed to be reliable in predicting the response to MDD like gender, age race/ethnicity, SES, BMI and the presence of alexithymia and the severity of depressive symptoms.

Many issues remain to be resolved in the application of pharmacogenetics to treat depression. First is a thorough understanding of the genetic mechanisms is needed and an understanding of what is a reliable indicator of treatment response. Ethics, such as privacy, and the responsible use of genetic information are also important to consider. In the long run, pharmacogenetics may offer a chance to lessen the stigma that surrounds mental health care and improve treatment outcomes for those struggling with depression. As with all psychiatric approaches, it is important to carefully consider and implement the plan. For now, the best option is to provide patients with a variety of effective depression medication options and encourage them to speak openly with their doctors about their experiences and concerns.