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Personalized atypical depression treatment Treatment

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

Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalized micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to understand their feature predictors and reveal distinct features that deterministically change mood as time passes.

Predictors of Mood

Depression is one of the leading causes of mental depression treatment illness.1 However, only about half of those suffering from the disorder receive treatment1. To improve outcomes, clinicians must be able to identify and treat patients who are most likely to benefit from certain treatments.

Personalized depression treatment is one way to do this. By using sensors on 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 which treatments. With two grants totaling over $10 million, they will make use of these tools to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

So far, the majority of research into predictors of depression treatment effectiveness has been focused on the sociodemographic and clinical aspects. These include demographic factors such as age, gender and education, clinical characteristics such as symptoms severity and comorbidities and biological indicators such as neuroimaging and genetic variation.

While many of these aspects can be predicted from data in medical records, few studies have utilized longitudinal data to explore the factors that influence mood in people. Many studies do not take into consideration the fact that moods can vary significantly between individuals. It is therefore important to devise methods that permit the analysis and measurement of personal differences between 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. This enables the team to develop algorithms that can detect distinct patterns of behavior and emotion that differ between individuals.

In addition to these modalities the team created a machine learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm integrates the individual differences to produce an individual "digital genotype" for each participant.

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

Predictors of Symptoms

Depression is one of the world's leading causes of disability1 yet it is often underdiagnosed and undertreated2. In addition, a lack of effective treatments and stigma associated with depressive disorders stop many individuals from seeking help.

To help with personalized treatment, it is important to determine the predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which are unreliable and only identify a handful of symptoms associated with depression.

Machine learning can be used to integrate continuous digital behavioral phenotypes that are captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of symptom severity has the potential to improve diagnostic accuracy and increase treatment efficacy for depression. These digital phenotypes provide a wide range of unique actions and behaviors that are difficult to record through interviews and permit continuous, high-resolution measurements.

The study involved University of California Los Angeles students with mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment according to the degree of their depression. Those with a score on the CAT-DI of 35 65 were assigned to online support via the help of a peer coach. those who scored 75 patients were referred for psychotherapy in person.

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial traits. The questions included age, sex, and education, financial status, marital status as well as whether they divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. The CAT DI assessment was performed every two weeks for those who received online support and weekly for those who received in-person assistance.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic, and many studies aim to identify predictors that enable clinicians to determine the most effective drugs for each patient. Particularly, pharmacogenetics can identify genetic variants that determine the way that the body processes antidepressants. This enables doctors to choose drugs that are likely to work best for each patient, reducing the time and effort involved in trial-and-error treatments and avoid any adverse effects that could otherwise hinder the progress of the patient.

Another promising approach is building prediction models using multiple data sources, including clinical information and neural imaging data. These models can then be used to determine the best combination of variables that are predictive of a particular outcome, such as whether or not a medication will improve mood and symptoms. These models can be used to determine the response of a patient to a treatment, allowing doctors to maximize the effectiveness of their treatment.

A new era of research utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to blend the effects of several variables and increase predictive accuracy. These models have shown to be useful for the prediction of treatment outcomes like the response to antidepressants. These approaches are becoming more popular in psychiatry and could become the standard of future medical practice.

Research into depression's underlying mechanisms continues, as well as predictive models based on ML. Recent research suggests that the disorder is linked with neural dysfunctions that affect specific circuits. This suggests that an individualized depression treatment will be built around targeted treatments that target these neural circuits to restore normal function.

One method of doing this is through internet-delivered interventions that offer a more personalized and customized experience for patients. For example, one study found that a web-based program was more effective than standard treatment in alleviating symptoms and ensuring a better quality of life for patients suffering from MDD. Furthermore, a randomized controlled study of a customized approach to depression treatment showed steady improvement and decreased side effects in a significant number of participants.

Predictors of adverse effects

A major obstacle in individualized depression treatment involves identifying and predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed various drugs before they find a drug that is effective and tolerated. Pharmacogenetics offers a new and exciting method of selecting antidepressant drugs that are more efficient and targeted.

Many predictors can be used to determine the best antidepressant to prescribe, such as gene variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. However, identifying the most reliable and valid factors that can predict the effectiveness of a particular treatment is likely to require controlled, randomized trials with considerably larger samples than those typically enrolled in clinical trials. This is because the detection of interactions or moderators could be more difficult in trials that take into account a single episode of treatment per participant instead of multiple episodes of treatment over a period of time.

In addition the prediction of a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's own perception of the effectiveness and tolerability. Currently, only some easily identifiable sociodemographic and clinical variables appear to be reliable in predicting response to MDD, such as 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 elderly treatment is still in its beginning stages, and many challenges remain. First is a thorough understanding of the underlying genetic mechanisms is required and a clear definition of what is a reliable predictor of treatment response. Ethics like privacy, and the responsible use genetic information are also important to consider. Pharmacogenetics could be able to, over the long term help reduce stigma around mental depression treatment health treatment and improve treatment outcomes. As with all psychiatric approaches it is crucial to give careful consideration and implement the plan. At present, the most effective option is to offer patients various effective depression medications and encourage them to speak freely with their doctors about their experiences and concerns.