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12 Stats About Personalized Depression Treatment To Make You Look Smart Around Other People
Personalized Depression Treatment

For many suffering from depression, traditional therapy and medications are not effective. Personalized treatment may be the solution.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into customized micro-interventions that improve mental health. We analyzed the best-fitting personalized ML models to each person using Shapley values to discover their features and predictors. The results revealed distinct characteristics that deterministically changed mood over time.

Predictors of Mood

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

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most from specific treatments. They make use of mobile phone sensors, a voice assistant with artificial intelligence as well as other digital tools. With two grants awarded totaling over $10 million, they will employ these tools to identify the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

So far, the majority of research into predictors of depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include factors that affect the demographics such as age, gender and education, clinical characteristics such as symptom severity and comorbidities, and biological markers such as neuroimaging and genetic variation.

While many of these variables can be predicted by the information available in medical records, very few studies have used longitudinal data to explore the causes of mood among individuals. Few also take into account the fact that mood varies significantly between individuals. Therefore, it is essential to develop methods that permit the identification of different mood predictors for each person and treatments 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 will then create algorithms to detect patterns of behavior and emotions that are unique to each person.

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

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

Predictors of symptoms

Depression is one of the leading causes of disability1 yet it is often untreated and not diagnosed. Depression disorders are rarely treated because of the stigma that surrounds them, as well as the lack of effective interventions.

To help with personalized treatment, it is important to identify predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only reveal a few features associated with depression.

Using machine learning to combine continuous digital behavioral phenotypes of a person captured by sensors on smartphones and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of severity of symptoms can increase the accuracy of diagnostics and treatment efficacy for depression. Digital phenotypes permit continuous, high-resolution measurements as well as capture a variety of distinct behaviors and patterns that are difficult to document using interviews.


The study involved University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or medical care depending on the degree of their depression. Those with a score on the CAT-DI scale of 35 65 were given online support by the help of a coach. Those with a score 75 patients were referred for psychotherapy in-person.

At the beginning, participants answered an array of questions regarding their personal demographics and psychosocial features. The questions covered age, sex and education and marital status, financial status as well as whether they divorced or not, their current suicidal thoughts, intentions or attempts, and 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 assessment was carried out every two weeks for those who received online support and weekly for those who received in-person support.

Predictors of Treatment Response

The development of a personalized depression treatment is currently a research priority, and many studies aim at identifying predictors that allow clinicians to identify the most effective medications for each person. Pharmacogenetics, in particular, is a method of identifying 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, minimizing the time and effort required in trial-and-error procedures and avoiding side effects that might otherwise hinder advancement.

Another approach that is promising is to develop predictive models that incorporate clinical data and neural imaging data. These models can be used to determine the variables that are most predictive of a specific outcome, such as whether a medication can help with symptoms or mood. These models can be used to predict the patient's response to treatment, allowing doctors to maximize the effectiveness.

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

Research into the underlying causes of depression continues, as do predictive models based on ML. depression treatment interventions suggests that depression is connected to the dysfunctions of specific neural networks. This theory suggests that a individualized treatment for depression will be based on targeted therapies that restore normal function to these circuits.

Internet-based interventions are an option to achieve this. They can provide more customized and personalized experience for patients. One study found that an internet-based program improved symptoms and improved quality of life for MDD patients. A randomized controlled study of a customized treatment for depression showed that a substantial percentage of participants experienced sustained improvement and fewer side negative effects.

Predictors of Side Effects

In the treatment of depression one of the most difficult aspects is predicting and determining which antidepressant medications will have no or minimal negative side effects. Many patients take a trial-and-error approach, using various medications prescribed before finding one that is safe and effective. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and specific.

There are many predictors that can be used to determine which antidepressant should be prescribed, including gene variations, phenotypes of patients like gender or ethnicity and comorbidities. However, identifying the most reliable and reliable predictors for a particular treatment is likely to require randomized controlled trials with significantly larger numbers of participants than those normally enrolled in clinical trials. This is due to the fact that the identification of moderators or interaction effects may be much more difficult in trials that only focus on a single instance of treatment per person instead of multiple sessions of treatment over time.

In addition to that, predicting a patient's reaction will likely require information about the severity of symptoms, comorbidities and the patient's subjective perception of the effectiveness and tolerability. Currently, only a few easily identifiable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

There are many challenges to overcome when it comes to the use of pharmacogenetics for depression treatment. First, it is important to have a clear understanding and definition of the genetic mechanisms that cause depression, as well as an understanding of a reliable indicator of the response to treatment. Ethics like privacy, and the ethical use of genetic information must also be considered. In the long term pharmacogenetics can provide an opportunity to reduce the stigma associated with mental health treatment and to improve the treatment outcomes for patients with depression. As with any psychiatric approach, it is important to take your time and carefully implement the plan. For now, the best option is to offer patients a variety of effective depression medications and encourage them to speak with their physicians about their concerns and experiences.

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