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Personalized Depression Treatment

Traditional therapy and medication do not work for many patients suffering from depression. The individual approach to treatment could be the solution.

Cue is an intervention platform for digital devices that translates passively acquired normal sensor data from smartphones into customized micro-interventions designed to improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their feature predictors and reveal distinct features that are able to change mood over time.

Predictors of Mood

Depression is a major cause of mental illness around the world.1 Yet the majority of people with the condition receive treatment. To improve outcomes, healthcare professionals must be able to identify and treat patients with the highest chance of responding to certain treatments.

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

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

Very few studies have used longitudinal data to determine mood among individuals. Few also take into account the fact that mood can vary significantly between individuals. It is therefore important to develop methods which permit the determination and quantification of the personal differences between mood predictors, Electric Shock treatment for depression 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. This allows the team to develop algorithms that can detect various patterns of behavior and emotion that differ between individuals.

The team also devised a machine learning algorithm to create dynamic predictors for each person's mood for depression. The algorithm blends the individual differences to produce an individual "digital genotype" for each participant.

The digital phenotype was associated with CAT-DI scores, which is a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is the leading cause of disability around the world1, but it is often untreated and misdiagnosed. In addition the absence of effective treatments and stigmatization associated with depressive disorders prevent many individuals from seeking help.

To aid in the development of a personalized treatment, it is important to identify the factors that predict symptoms. However, current prediction methods rely on clinical interview, which has poor reliability and only detects a limited number of symptoms related to depression.2

Using machine learning to integrate continuous digital behavioral phenotypes captured by sensors on smartphones and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) with other predictors of severity of symptoms could improve diagnostic accuracy and increase the effectiveness of treatment for depression. Digital phenotypes are able to provide a wide range of unique behaviors and activities that are difficult to capture through interviews, and also allow for continuous, high-resolution measurements.

The study involved University of California Los Angeles students with mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment depending on the degree of their depression treatment tms. Those with a CAT-DI score of 35 65 students were assigned online support by an instructor and those with scores of 75 patients were referred for in-person psychotherapy.

At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal demographics and psychosocial features. These included age, sex education, work, and financial status; if they were divorced, married or single; their current suicidal ideation, intent or attempts; as well as the frequency at the frequency they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale ranging from zero to 100. The CAT-DI assessment was carried out every two weeks for participants who received online support and weekly for those who received in-person assistance.

Predictors of Treatment Response

Research is focusing on personalization of treatment for depression. Many studies are aimed at finding predictors that can help clinicians identify the most effective medications for each person. Particularly, pharmacogenetics can identify genetic variants that influence how the body metabolizes antidepressants. This lets doctors select the medication that are most likely to work for every patient, minimizing the time and effort needed for trial-and error treatments and avoiding any side consequences.

Another approach that is promising is to build prediction models using multiple data sources, including data from clinical studies and neural imaging data. These models can be used to determine the variables that are most likely to predict a specific outcome, such as whether a medication will help with symptoms or mood. These models can also be used to predict the patient's response to a treatment they are currently receiving and help doctors maximize the effectiveness of the treatment currently being administered.

A new era of research utilizes machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and increase predictive accuracy. These models have been proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming more popular in psychiatry and will likely be the norm in future clinical practice.

Research into the underlying causes of depression continues, as do ML-based predictive models. Recent findings suggest that the disorder is associated with dysfunctions in specific neural circuits. This theory suggests that a individualized treatment for depression will be based on targeted therapies that restore normal functioning to these circuits.

Internet-delivered interventions can be an option 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 homeopathic treatment for depression in improving symptoms and providing a better quality of life for those suffering from MDD. A randomized controlled study of an individualized treatment for depression showed that a substantial percentage of participants experienced sustained improvement as well as fewer side effects.

Predictors of adverse effects

In the treatment of depression, the biggest challenge is predicting and determining the antidepressant that will cause minimal or zero side negative effects. Many patients are prescribed various medications before finding a medication that is safe and effective. Pharmacogenetics is an exciting new method for an efficient and targeted method of selecting antidepressant therapies.

Several predictors may be used to determine which antidepressant is best to prescribe, including gene variants, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. However, identifying the most reliable and reliable predictive factors for a specific treatment will probably require controlled, randomized trials with much larger samples than those typically enrolled in clinical trials. This is because the detection of moderators or interaction effects could be more difficult in trials that only consider a single episode of treatment per participant instead of multiple sessions of treatment over a period of time.

Furthermore to that, predicting a patient's reaction will likely require information about the comorbidities, symptoms profiles and the patient's own experience of tolerability and effectiveness. At present, only a handful of easily assessable sociodemographic variables and clinical variables seem to be consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

Many issues remain to be resolved in the application of pharmacogenetics for depression treatment. First, it is essential to have a clear understanding and definition of the genetic factors that cause depression, and an understanding of an accurate indicator of the response to treatment. Ethics such as privacy and the responsible use genetic information must also be considered. In the long-term the use of pharmacogenetics could be a way to lessen the stigma associated with mental health care and improve treatment outcomes for those struggling with depression. As with any psychiatric approach, it is important to take your time and carefully implement the plan. The best method is to offer patients an array of effective medications for depression and encourage them to speak with their physicians about their concerns and experiences.