Personalized Depression Treatment

Traditional treatment and medications are not effective for a lot of people suffering from depression. A customized treatment could be the solution.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions to improve mental health. We analyzed the best-fitting personalized ML models to each person, using Shapley values to determine their feature predictors. The results revealed distinct characteristics that were deterministically changing mood over time.

Predictors of Mood

Depression is the leading cause of mental illness across the world.1 Yet the majority of people with the condition receive treatment. In order to improve outcomes, healthcare professionals must be able to recognize and treat patients who have the highest chance of responding to specific treatments.

Personalized depression treatment can help. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from specific treatments. They make use of mobile phone sensors, a voice assistant with artificial intelligence and other digital tools. With two grants awarded totaling more than $10 million, they will use these technologies to identify the biological and behavioral factors that determine 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 demographic variables like age, sex and education, clinical characteristics such as symptom severity and comorbidities, and biological markers such as neuroimaging and genetic variation.

A few studies have utilized longitudinal data in order to predict mood in individuals. Few studies also take into account the fact that mood can be very different between individuals. Therefore, it is crucial to devise methods that permit the identification and quantification of personal differences between mood predictors, treatment effects, etc.

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 detect patterns of behavior and emotions that are unique to each person.

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

This digital phenotype was correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is one of the leading causes of disability1 yet it is often underdiagnosed and undertreated2. residential depression treatment uk [click the following webpage] disorders are rarely treated due to the stigma that surrounds them, as well as the lack of effective interventions.

To allow for individualized treatment, identifying predictors of symptoms is important. However, the methods used to predict symptoms rely on clinical interview, which is not reliable and only detects a tiny number of features that are associated with depression.2

Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can provide continuous, high-resolution measurements as well as capture a wide range of distinctive behaviors and activity patterns that are difficult to document with interviews.

The study included University of California Los Angeles students with moderate to severe depression symptoms who were enrolled 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 to clinical treatment based on the severity of their depression. Patients who scored high on the CAT-DI scale of 35 65 students were assigned online support living with treatment resistant depression the help of a coach. Those with scores of 75 patients were referred to psychotherapy in-person.

At the beginning of the interview, participants were asked an array of questions regarding their personal demographics and psychosocial features. These included sex, age, education, work, and financial status; if they were divorced, married, or single; current suicidal ideas, intent or attempts; as well as the frequency with which they drank alcohol. Participants also scored their level of depression severity on a 0-100 scale using the CAT-DI. The CAT DI assessment was conducted every two weeks for those who received online support and weekly for those who received in-person assistance.

Predictors of the Reaction to Treatment

Research is focusing on personalized depression treatment. Many studies are aimed at finding predictors that can help clinicians identify the most effective drugs to treat each individual. Particularly, pharmacogenetics is able to identify genetic variants that determine how the body's metabolism reacts to antidepressants. This lets doctors select the medication that are most likely to work for each patient, reducing time and effort spent on trial-and error treatments and eliminating any adverse effects.

Another approach that is promising is to build prediction models using multiple data sources, combining data from clinical studies and neural imaging data. These models can then be used to identify the most appropriate combination of variables predictive of a particular outcome, like whether or not a medication is likely to improve symptoms and mood. These models can also be used to predict the response of a patient to a treatment they are currently receiving and help doctors maximize the effectiveness of current treatment.

A new generation employs machine learning techniques like algorithms for classification and supervised learning such as regularized logistic regression, and tree-based methods to integrate the effects of several variables and improve predictive accuracy. These models have been shown to be effective in predicting treatment outcomes for example, the response to antidepressants. These approaches are gaining popularity in psychiatry and it is expected that they will become the norm for future clinical practice.

In addition to the ML-based prediction models, research into the underlying mechanisms of depression treatment types is continuing. Recent findings suggest that depression is connected to the malfunctions of certain neural networks. This theory suggests that individualized depression treatment will be built around targeted therapies that target these neural circuits to restore normal functioning.

Internet-delivered interventions can be an option to achieve this. They can provide an individualized and tailored experience for patients. For example, one study found that a web-based program was more effective than standard care in alleviating symptoms and ensuring an improved quality of life for people suffering from MDD. A controlled, randomized study of a customized treatment for depression found that a significant number of patients saw improvement over time and fewer side negative effects.

Predictors of Side Effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will cause minimal or no side effects. Many patients are prescribed a variety medications before finding a medication that is both effective and well-tolerated. Pharmacogenetics provides a novel and exciting method of selecting antidepressant drugs that are more effective and specific.

Many predictors can be used to determine which antidepressant to prescribe, including gene variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and comorbidities. However it is difficult to determine the most reliable and accurate factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with considerably larger samples than those normally enrolled in clinical trials. This is due to the fact that it can be more difficult to identify the effects of moderators or interactions in trials that contain only one episode per participant instead of multiple episodes spread over a period of time.

Furthermore, the prediction of a patient's reaction to a specific medication will likely also require information on symptoms and comorbidities in addition to the patient's personal experience with tolerability and efficacy. There are currently only a few easily assessable sociodemographic variables and clinical variables appear to be consistently associated with 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 line treatment for anxiety and depression is a thorough understanding of the underlying genetic mechanisms is essential as well as a clear definition of what treatment for depression is a reliable predictor of treatment response. In addition, ethical issues, such as privacy and the appropriate use of personal genetic information must be considered carefully. The use of pharmacogenetics may eventually reduce stigma associated with mental health treatments and improve the quality of treatment. However, as with all approaches to psychiatry, careful consideration and application is required. At present, it's best to offer patients a variety of medications for depression that are effective and encourage them to talk openly with their doctors.