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Personalized Depression Treatment

Traditional therapies and medications are not effective for a lot of people suffering from depression. Personalized treatment could be the answer.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct features that deterministically change mood with time.

Predictors of Mood

Depression is the leading cause of mental illness in the world.1 Yet the majority of people with the condition receive treatment. To improve outcomes, clinicians need to be able to identify and treat patients who have the highest likelihood of responding to certain alternative treatments for depression.

A customized depression treatment is one method of doing this. Using mobile phone sensors as well as 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. Two grants were awarded that total over $10 million, they will employ these tools to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.

The majority of research on predictors for depression treatment effectiveness has focused on clinical and sociodemographic characteristics. These include demographics such as gender, age and education, as well as clinical aspects such as symptom severity and comorbidities, as well as biological markers.

While many of these aspects can be predicted by the information available in medical records, few studies have used longitudinal data to explore the causes of mood among individuals. Few studies also consider the fact that mood can differ significantly between individuals. Therefore, it is critical to create methods that allow the identification of individual differences in mood predictors and treatment effects.

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 is able to develop algorithms to identify patterns of behaviour and emotions that are unique to each individual.

In addition to these modalities the team also developed a machine-learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype was associated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, BH-adjusted P-value of 3.55 1003) and varied widely across individuals.

Predictors of symptoms

Depression is among the world's leading causes of disability1 but is often underdiagnosed and undertreated2. In addition an absence of effective interventions and stigma associated with depressive disorders prevent many individuals from seeking help.

To aid in the development of a personalized treatment plan, identifying patterns that can predict symptoms is essential. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only detect a few characteristics that are associated with depression.

Using machine learning to combine continuous digital behavioral phenotypes of a person captured by sensors on smartphones and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of symptom severity can improve the accuracy of diagnosis and treatment efficacy for depression. Digital phenotypes can be used to are able to capture a variety of unique actions and behaviors that are difficult to record through interviews, and allow for continuous, high-resolution measurements.

The study included University of California Los Angeles students who had mild to severe depression treatment without medication symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or medical care depending on the severity of their depression. Participants who scored a high on the CAT-DI scale of 35 or 65 students were assigned online support via a coach and those with a score 75 were routed to in-person clinics for psychotherapy.

At baseline, participants provided a series of questions about their personal demographics and psychosocial characteristics. The questions asked included education, age, sex and gender and financial status, marital status as well as whether they divorced or not, current suicidal ideas, intent or attempts, as well as how often they drank. The CAT-DI was used for assessing the severity of depression-related symptoms on a scale from 100 to. The CAT-DI tests were conducted every other week for the participants who received online support and once a week for those receiving in-person support.

Predictors of Treatment Response

Research is focusing on personalized depression and treatment treatment. Many studies are focused on finding predictors that can help doctors determine the most effective medications to treat each individual. Particularly, pharmacogenetics can identify genetic variations that affect the way that the body processes antidepressants. This lets doctors choose the medications that will likely work best for each patient, reducing time and effort spent on trial-and error treatments and avoid any negative side consequences.

Another promising approach is to create prediction models that combine information from clinical studies and neural imaging data. These models can then be used to determine the most appropriate combination of variables meds that treat depression and anxiety are predictors of a specific outcome, such as whether or not a drug will improve mood and symptoms. These models can also be used to predict a patient's response to an existing treatment and help doctors maximize the effectiveness of the current therapy.

A new generation of studies employs 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 to improve predictive accuracy. These models have proven to be useful in the prediction of treatment outcomes like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and will likely become the standard of future medical practice.

In addition to prediction models based on ML, research into the underlying mechanisms of depression is continuing. Recent research suggests that the disorder is connected with neurodegeneration in particular circuits. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.

Internet-delivered interventions can be a way to accomplish this. They can provide more customized and personalized experience for patients. For example, one study discovered that a web-based treatment was more effective than standard care in reducing symptoms and ensuring the best quality of life for people with MDD. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed steady improvement and decreased side effects in a significant number of participants.

Predictors of Side Effects

In the treatment of depression the biggest challenge is predicting and identifying which antidepressant medication will have very little or no adverse negative effects. Many patients are prescribed a variety medications before settling on a treatment that is effective and tolerated. Pharmacogenetics offers a new and exciting way to select antidepressant drugs that are more efficient and targeted.

There are several predictors that can be used to determine the antidepressant that should be prescribed, such as gene variations, patient phenotypes like gender or ethnicity, and comorbidities. To identify the most reliable and reliable predictors of a specific treatment, controlled trials that are randomized with larger samples will be required. This is due to the fact that it can be more difficult to detect the effects of moderators or interactions in trials that contain only one episode per participant rather than multiple episodes over a long period of time.

Additionally the prediction of a patient's response will likely require information about the severity of symptoms, comorbidities and the patient's personal experience of tolerability and effectiveness. At present, only a few easily measurable sociodemographic and clinical variables appear to be reliably associated with the response to MDD, such as age, gender, race/ethnicity and SES, BMI and the presence of alexithymia, and the severity of depression symptoms.

Many issues remain to be resolved when it comes to the use of pharmacogenetics to treat depression. First, it is important to have a clear understanding and definition of the genetic mechanisms that underlie depression, and an understanding of a reliable predictor of treatment response. Ethics like privacy, and the responsible use genetic information should also be considered. Pharmacogenetics can, in the long run reduce stigma associated with mental health treatment and improve the quality of treatment. As with any psychiatric approach, it is important to give careful consideration and implement the plan. For now, the best course of action is to provide patients with various effective depression medication options and encourage them to speak freely with their doctors about their experiences and concerns.