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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 solution.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into customized micro-interventions that improve mental health. We analyzed the best-fitting personalized ML models to each subject, using Shapley values, in order to understand their characteristic predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

Depression is one of the world's leading causes of mental illness.1 However, only half of those who have the condition receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients who are most likely to respond to certain treatments.

A customized depression treatment is one method of doing this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from certain treatments. They make use of sensors on mobile phones, a voice assistant with artificial intelligence as well as other digital tools. Two grants worth more than $10 million will be used to discover the biological and behavioral indicators of response.

To date, the majority of research on predictors for depression treatment effectiveness has focused on the sociodemographic and clinical aspects. These include factors that affect the demographics like age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.

While many of these variables can be predicted from the information in medical records, few studies have used longitudinal data to study the causes of mood among individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to create methods that allow the recognition 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 recognize patterns of behaviour and emotions that are unique to each person.

In addition to these methods, the team also developed a machine-learning algorithm to model the dynamic variables that influence each person's mood. The algorithm integrates the individual characteristics to create an individual "digital genotype" for each participant.

This digital phenotype has been associated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was not strong, however (Pearson r = 0,08, P-value adjusted by BH 3.55 10 03) and varied greatly between individuals.

Predictors of symptoms

Depression is a leading reason for disability across the world1, but it is often not properly diagnosed and treated. Depressive disorders are often not treated due to the stigma associated with them and the absence of effective interventions.

To facilitate personalized treatment to improve treatment, identifying the predictors of symptoms is important. However, the methods used to predict symptoms depend on the clinical interview which is not reliable and only detects a tiny variety of characteristics associated with depression.2

Machine learning can be used to blend continuous digital behavioral phenotypes that are captured through smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing herbal depression treatments Inventory, the CAT-DI) along with other indicators of symptom severity could improve diagnostic accuracy and increase the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements and capture a wide variety of unique behaviors and activity patterns meds that treat anxiety and depression are difficult to record with interviews.

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, which was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical treatment depending on their depression severity. Those with a CAT-DI score of 35 or 65 were assigned online support by the help of a coach. Those with scores of 75 patients were referred to psychotherapy in person.

Participants were asked a series of questions at the beginning of the study concerning their demographics and psychosocial traits. The questions asked included age, sex and education, marital status, financial status as well as whether they divorced or not, their current suicidal thoughts, intentions or attempts, and the frequency with which they consumed Alcohol depression Treatment. Participants also rated their degree of depression symptom severity on a scale ranging from 0-100 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

The development of a personalized depression treatment is currently a major research area and many studies aim at identifying predictors that will enable clinicians to determine the most effective medications for each patient. Pharmacogenetics, for instance, is a method of identifying genetic variations that affect how treat anxiety and depression the body's metabolism reacts to drugs. This enables doctors to choose medications that are likely to be most effective for each patient, while minimizing the time and effort involved in trial-and-error procedures 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, such as clinical information and neural imaging data. These models can be used to identify which variables are the most predictive of a specific outcome, such as whether a medication can improve mood or symptoms. These models can be used to determine the patient's response to a treatment, allowing doctors to maximize the effectiveness of their treatment.

A new generation of studies uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of many variables to improve predictive accuracy. These models have been shown to be effective in predicting treatment outcomes like the response to antidepressants. These approaches are becoming more popular in psychiatry and could become the norm in the future clinical practice.

The study of depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent findings suggest that depression is related to dysfunctions in specific neural networks. This suggests that an individualized treatment for depression will be based upon targeted treatments that restore normal function to these circuits.

One method to achieve this is to use internet-based interventions which can offer an individualized and personalized experience for patients. A study showed that an internet-based program helped improve symptoms and led to a better quality of life for MDD patients. Furthermore, a randomized controlled study of a customized treatment for depression demonstrated steady improvement and decreased adverse effects in a significant percentage of participants.

Predictors of Side Effects

In the treatment of depression the biggest challenge is predicting and determining the antidepressant that will cause very little or no negative side negative effects. Many patients are prescribed a variety drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics offers a fascinating new method for an efficient and targeted approach to selecting antidepressant treatments.

Several predictors may be used to determine the best antidepressant to prescribe, such as gene variations, phenotypes of patients (e.g. gender, sex or ethnicity) and the presence of comorbidities. However it is difficult to determine the most reliable and valid predictive factors for a specific treatment will probably require randomized controlled trials of much larger samples than those normally enrolled in clinical trials. This is because the identifying of interactions or moderators can be a lot more difficult in trials that only consider a single episode of treatment per patient instead of multiple episodes of treatment over a period of time.

Furthermore the estimation of a patient's response to a specific medication will likely also require information on comorbidities and symptom profiles, as well as the patient's personal experiences with the effectiveness and tolerability of the medication. There are currently only a few easily identifiable sociodemographic variables and clinical variables seem to be consistently associated with response to MDD. These include gender, age, race/ethnicity as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many obstacles to overcome. First, it is important to have a clear understanding and definition of the genetic mechanisms that cause depression, as well as an accurate definition of a reliable indicator of the response to treatment. Additionally, ethical issues, such as privacy and the responsible use of personal genetic information, must be carefully considered. In the long term, pharmacogenetics may be a way to lessen the stigma associated with mental health treatment and improve the treatment outcomes for patients with depression. However, as with any approach to psychiatry careful consideration and planning is essential. For now, the best option is to provide patients with a variety of effective depression medications and encourage them to speak openly with their doctors about their concerns and experiences.