Do Not Forget Personalized Depression Treatment: 10 Reasons That You No Longer Need It > 자유게시판

Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medication are ineffective. A customized treatment could be the solution.

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

Predictors of Mood

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

The ability to tailor depression treatments is one way to do this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit the most from certain treatments. They are using sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence and other digital tools. With two grants awarded totaling over $10 million, they will make use of these techniques to determine the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

The majority of research done to so far has focused on sociodemographic and clinical characteristics. These include demographics such as gender, age and education, and clinical characteristics like symptom severity, comorbidities and biological markers.

A few studies have utilized longitudinal data to predict mood in individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is essential 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 treatment for depression evaluation with the BiAffect app and electroencephalography -- an imaging technique that monitors brain activity. This allows the team to develop algorithms that can systematically identify different patterns of behavior and emotion that are different between people.

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

This digital phenotype was linked to 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 1003) and varied widely among individuals.

Predictors of Symptoms

Depression is one of the leading causes of disability1, but it is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigma associated with depressive disorders prevent many individuals from seeking help.

To aid in the development of a personalized treatment for panic attacks and depression, it is crucial to determine the predictors of symptoms. However, the current methods for predicting symptoms rely on clinical interview, which is unreliable and only detects a tiny number of features that are associated with depression.2

Using machine learning to combine continuous digital behavioral phenotypes captured through smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of severity of symptoms could improve diagnostic accuracy and increase the effectiveness of treatment 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 included 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 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care depending on the degree of their depression. Patients who scored high on the CAT-DI scale of 35 or 65 were allocated online support with an online peer coach, whereas those who scored 75 were routed to in-person clinical care for psychotherapy.

At baseline, participants provided the answers to a series of questions concerning their personal demographics and psychosocial characteristics. These included age, sex education, work, and financial status; whether they were divorced, partnered or single; the frequency of suicidal ideation, intent or attempts; as well as the frequency with the frequency they consumed alcohol. Participants also scored their level of depression severity on a 0-100 scale using the CAT-DI. The CAT-DI test was performed every two weeks for participants who received online support and weekly for those who received in-person care.

Predictors of Treatment Response

Research is focused on individualized therapy treatment for Depression for depression. Many studies are aimed at finding predictors, which can help doctors determine the most effective drugs to treat each patient. Pharmacogenetics, in particular, uncovers genetic variations that affect how the human body metabolizes drugs. This enables doctors to choose the medications that are most likely to be most effective for each patient, minimizing the time and effort required in trials and errors, while eliminating any side effects that could otherwise slow progress.

Another promising approach is building prediction models using multiple data sources, combining the clinical information with neural imaging data. These models can be used to identify the most appropriate combination of variables that is 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 an existing treatment which allows doctors to maximize the effectiveness of their current treatment.

A new type of research uses machine learning methods, 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 demonstrated to be useful in predicting treatment outcomes for example, the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the standard of future non medical treatment for depression practice.

In addition to ML-based prediction models The study of the underlying mechanisms of depression continues. Recent research suggests that depression is connected to the dysfunctions of specific neural networks. This suggests that individual depression treatment will be based on targeted treatments that target these neural circuits to restore normal function.

Internet-delivered interventions can be an option to accomplish this. They can offer an individualized and tailored experience for patients. For example, one study found that a web-based program was more effective than standard treatment in alleviating symptoms and ensuring the best natural treatment for depression quality of life for people with MDD. Additionally, a randomized controlled study of a personalised approach to treating depression showed sustained improvement and reduced side effects in a significant number of participants.

Predictors of side effects

A major issue in personalizing depression treatment is predicting the antidepressant medications that will have minimal or no side effects. Many patients have a trial-and error method, involving various medications being prescribed before settling on one 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 to prescribe, including gene variants, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. However, identifying the most reliable and accurate factors that can predict the effectiveness of a particular treatment will probably require controlled, randomized trials with much larger samples than those normally enrolled in clinical trials. This is because it may be more difficult to determine the effects of moderators or interactions in trials that only include one episode per person instead of multiple episodes spread over time.

Additionally, the estimation of a patient's response to a particular medication will likely also require information about symptoms and comorbidities in addition to the patient's previous experiences with the effectiveness and tolerability of the medication. At present, only a few easily assessable sociodemographic and clinical variables appear to be reliably associated with the severity of MDD, such as age, gender, race/ethnicity and SES, BMI, the presence of alexithymia, and the severity of depression symptoms.

Many challenges remain in 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 accurate definition of a reliable indicator of the response to treatment. Ethics such as privacy and the responsible use genetic information should also be considered. In the long term the use of pharmacogenetics could be a way to lessen the stigma that surrounds mental health treatment and to improve the outcomes of those suffering with depression. However, as with any approach to psychiatry careful consideration and planning is necessary. In the moment, it's best to offer patients various depression medications that work and encourage patients to openly talk with their physicians.