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Personalized Depression Treatment

Traditional treatment and medications are not effective for a lot of people suffering from depression. The individual approach to treatment could be the answer.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into personalized micro-interventions for improving mental health. We looked at the best-fitting personal ML models to each person using Shapley values to discover their features and predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is one of the most prevalent causes of mental illness.1 Yet, only half of people suffering from the condition receive treatment1. In order to improve outcomes, healthcare professionals must be able to identify and treat patients with the highest likelihood of responding to certain treatments.

The ability to tailor depression treatments is one method to achieve this. By using 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 determine which patients will benefit from which treatments. With two grants totaling more than $10 million, they will make use of these technologies to identify the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include demographics such as age, gender and education and clinical characteristics such as symptom severity and comorbidities as well as biological markers.

While many of these factors can be predicted from the information in medical records, very few studies have employed longitudinal data to study the factors that influence mood in people. Many studies do not take into consideration the fact that mood varies significantly between individuals. Therefore, it is critical to develop methods that permit 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 develop algorithms that can identify different 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 factors that determine a person's depressed mood. The algorithm blends these individual characteristics into a distinctive "digital phenotype" for each participant.

The digital phenotype was associated with CAT-DI scores, which is a psychometrically validated scale for assessing severity of symptom. However the correlation was not strong (Pearson's r = 0.08, BH-adjusted P-value of 3.55 1003) and varied widely among individuals.

Predictors of Symptoms

untreatable depression is the leading cause of disability around the world1, but it is often not properly diagnosed and treated. Depressive disorders are often not treated because of the stigma attached to them, as well as the lack of effective interventions.

To help with personalized treatment, it is essential to identify predictors of symptoms. However, current prediction methods are based on the clinical interview, which is unreliable and only detects a limited number of features associated with depression.2

Machine learning can be used to integrate continuous digital behavioral phenotypes that are captured through smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory the CAT-DI) with other predictors of symptom severity could increase the accuracy of diagnostics and treatment efficacy for depression. These digital phenotypes are able to capture a variety of unique actions and behaviors that are difficult to record through interviews, and allow for continuous and high-resolution measurements.

The study involved University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were routed to online assistance or in-person clinics in accordance with their severity of depression. Those with a score on the CAT-DI of 35 or 65 were assigned online support with the help of a coach. Those with a score 75 patients were referred to psychotherapy in-person.

At baseline, participants provided the answers to a series of questions concerning their personal demographics and psychosocial features. These included age, sex and education, as well as work and financial status; if they were divorced, married or single; their current suicidal ideas, intent or attempts; as well as the frequency with which they drank alcohol. Participants also rated their level of depression symptom severity on a scale of 0-100 using the CAT-DI. The CAT-DI test was conducted every two weeks for participants who received online support and weekly for those who received in-person assistance.

Predictors of Treatment Reaction

The development of a personalized depression treatment is currently a top research topic and a lot of studies are aimed at identifying predictors that help clinicians determine the most effective drugs for each patient. Pharmacogenetics, in particular, uncovers genetic variations that affect 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 involved in trial-and-error treatments and avoiding side effects that might otherwise hinder advancement.

Another promising method is to construct prediction models using multiple data sources, combining clinical information and neural imaging data. These models can be used to determine which variables are most predictive of a particular outcome, such as whether a drug will improve mood or symptoms. These models can also be used to predict a patient's response to a treatment they are currently receiving and help doctors maximize the effectiveness of their treatment currently being administered.

A new generation employs machine learning techniques such as the supervised and classification algorithms such as regularized logistic regression, and tree-based methods to integrate the effects from multiple variables and improve predictive accuracy. These models have been shown to be useful in predicting outcomes of treatment, such as response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to be the norm in future clinical practice.

Research into depression's underlying mechanisms continues, as do ML-based predictive models. Recent research suggests that depression is linked to the dysfunctions of specific neural networks. This theory suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.

Internet-based interventions are an option to achieve this. They can offer more customized and personalized experience for patients. For instance, one study found that a program on the internet was more effective than standard care in alleviating symptoms and ensuring a better quality of life for patients with MDD. In addition, a controlled randomized study of a customized approach to treating perimenopause depression treatment showed steady improvement and decreased adverse effects in a large percentage of participants.

Predictors of side effects

In the treatment of depression a major depression treatment challenge is predicting and determining which antidepressant medications will have no or minimal side negative effects. Many patients take a trial-and-error approach, with several medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting way to select antidepressant drugs that are more effective and precise.

Many predictors can be used to determine which antidepressant is best to prescribe, such as gene variants, patient phenotypes (e.g., sex or ethnicity) and comorbidities. However finding the most reliable and valid predictive factors for a specific treatment is likely to require randomized controlled trials with considerably larger samples than those that are typically part of clinical trials. This is due to the fact that the identification of interactions or moderators could be more difficult in trials that focus on a single instance of treatment per participant, rather than multiple episodes of treatment over time.

Additionally the prediction of a patient's response to a specific medication will likely also need to incorporate information regarding symptoms and comorbidities and the patient's prior subjective experiences with the effectiveness and tolerability of the medication. Currently, only a few easily assessable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics to treatment for depression is in its beginning stages, and many challenges remain. First, it is essential to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, and an understanding of a reliable indicator of the response to treatment. In addition, ethical concerns, such as privacy and the ethical use of personal genetic information, must be considered carefully. In the long-term the use of pharmacogenetics could be a way to lessen the stigma that surrounds mental health care and improve treatment outcomes for those struggling with Pregnancy depression treatment. But, like any other psychiatric treatment, careful consideration and implementation is necessary. In the moment, it's recommended to provide patients with various depression medications that are effective and encourage them to speak openly with their physicians.