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Personalized Depression Treatment

For a lot of people suffering from private depression treatment, traditional therapies and medication isn't effective. The individual approach to treatment could be the solution.

Cue is an intervention platform for digital devices that translates passively acquired normal sensor data from smartphones into customized micro-interventions that improve mental health. We looked at the best-fitting personal ML models to each person, using Shapley values, in order to understand their features and predictors. The results revealed distinct characteristics that were deterministically changing mood 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, healthcare professionals must be able to recognize and treat patients with the highest probability of responding to specific treatments.

A customized depression treatment is one way to do this. Utilizing 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. Two grants totaling more than $10 million will be used to discover biological and behavioral indicators of response.

The majority of research to the present has been focused on clinical and sociodemographic characteristics. These include demographics such as age, gender and education as well as clinical aspects like symptom severity and comorbidities as well as biological markers.

Few studies have used longitudinal data to determine mood among individuals. Many studies do not consider the fact that moods can differ significantly between individuals. Therefore, it is crucial to develop methods that allow for the determination of the individual differences in mood predictors 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. The team will then create algorithms to detect patterns of behavior and emotions that are unique to each individual.

In addition to these methods, the team created a machine learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype was correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was weak, however (Pearson r = 0,08; BH adjusted P-value 3.55 10 03) and varied widely between individuals.

Predictors of symptoms

depression treatment goals is among the most prevalent causes of disability1, but it is often untreated and not diagnosed. In addition, a lack of effective interventions and stigmatization associated with depression treatment Tms disorders hinder many individuals from seeking help.

To assist in individualized treatment, it is important to identify the factors that predict symptoms. Current prediction methods rely heavily on clinical interviews, which are not reliable and only identify a handful of symptoms associated with depression.

Using machine learning to combine continuous digital behavioral phenotypes that are captured by sensors on smartphones and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory the CAT-DI) with other predictors of symptom severity could improve diagnostic accuracy and increase the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements as well as capture a variety of distinctive behaviors and activity patterns that are difficult to capture using interviews.

The study included University of California Los Angeles students with moderate 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 directed to online assistance or medical care according to the degree of their depression. Participants who scored a high on the CAT DI of 35 or 65 students were assigned online support by a coach and those with scores of 75 patients were referred for psychotherapy in person.

At the beginning, participants answered the answers to a series of questions concerning their personal characteristics and psychosocial traits. These included sex, age education, work, and financial status; whether they were divorced, married, 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 symptoms on a scale of 0-100. The CAT DI assessment was performed every two weeks for participants who received online support, and weekly for those who received in-person care.

Predictors of the Reaction to Treatment

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 patient. Pharmacogenetics, in particular, uncovers genetic variations that affect how the body's metabolism reacts to drugs. This enables doctors to choose the medications that are most likely to work best for each patient, while minimizing the time and effort required in trials and errors, while avoiding side effects that might otherwise hinder progress.

Another option is to build prediction models combining clinical data 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 drug is likely to improve symptoms and mood. These models can be used to determine a patient's response to an existing treatment and help doctors maximize the effectiveness of current therapy.

A new generation uses machine learning techniques like supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects from multiple variables and improve predictive accuracy. These models have shown to be useful in forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming more popular in psychiatry and could be the norm in future medical practice.

The study of depression's underlying mechanisms continues, as do predictive models based on ML. Recent findings suggest that depression is related to the malfunctions of certain neural networks. This theory suggests that an individualized treatment for depression will depend on targeted therapies that restore normal functioning to these circuits.

Internet-based interventions are an effective method to accomplish this. They can provide an individualized and tailored experience for patients. One study found that an internet-based program helped improve symptoms and improved quality of life for MDD patients. Furthermore, a randomized controlled trial of a personalized treatment for mild depression treatment demonstrated an improvement in symptoms and fewer side effects in a significant proportion of participants.

Predictors of Side Effects

A major challenge in personalized depression treatment is predicting which antidepressant medications will have minimal or no side effects. Many patients have a trial-and error approach, with several medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant drugs that are more efficient and targeted.

Many predictors can be used to determine which antidepressant is best to prescribe, including genetic variants, patient phenotypes (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. To determine the most reliable and valid predictors of a specific treatment, randomized controlled trials 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 contain only one episode per participant instead of multiple episodes over a long period of time.

Additionally to that, predicting a patient's reaction will likely require information about the comorbidities, symptoms profiles and the patient's personal perception of effectiveness and tolerability. Presently, only a handful of easily identifiable sociodemographic and clinical variables are believed to be reliable in predicting the severity of MDD like age, gender, race/ethnicity and SES, BMI and the presence of alexithymia, and the severity of depressive symptoms.

The application of pharmacogenetics to depression treatment is still in its infancy, and many challenges remain. First is a thorough understanding of the underlying genetic mechanisms is needed and a clear definition of what is a reliable predictor of treatment response. Ethics like privacy, and the responsible use of genetic information are also important to consider. In the long run pharmacogenetics can provide an opportunity to reduce the stigma that surrounds mental health treatment and improve treatment outcomes for those struggling with depression. But, like any approach to psychiatry careful consideration and application is essential. In the moment, it's best to offer patients various depression medications that work and encourage them to speak openly with their doctors.