The Ultimate Glossary Of Terms About Personalized Depression Treatment > 자유게시판

Personalized Depression Treatment

Traditional therapies and medications do not work for many people who are depressed. The individual approach to non drug treatment for anxiety and depression could be the answer.

Cue is an intervention platform for digital devices that transforms passively acquired sensor data from smartphones into personalised micro-interventions to improve mental health. We analysed 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 over time.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 However, only half of those suffering from the condition receive treatment1. To improve the outcomes, healthcare professionals must be able to identify and treat patients who have the highest probability of responding to certain treatments.

The treatment of depression can be personalized to help. Utilizing sensors on mobile phones and an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from the treatments they receive. Two grants totaling more than $10 million will be used to discover biological and behavior predictors of response.

To date, the majority of research on predictors for depression treatment effectiveness has focused on sociodemographic and clinical characteristics. These include demographic factors such as age, sex and education, clinical characteristics including symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

Very few studies have used longitudinal data in order to predict mood of individuals. A few studies also take into account the fact that moods can differ significantly between individuals. Therefore, it is critical to develop methods that permit 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. This allows the team to develop algorithms that can identify different patterns of behavior and emotions that are different between people.

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

This digital phenotype was correlated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was not strong however (Pearson r = 0,08, P-value adjusted by BH 3.55 10 03) and varied significantly among individuals.

Predictors of symptoms

Depression is one of the leading causes of disability1 but is often underdiagnosed and undertreated2. Depression disorders are rarely treated because of the stigma associated with them and the absence of effective treatments.

To assist in individualized treatment, it is crucial to identify the factors that predict symptoms. However, the methods used to predict symptoms are based on the clinical interview, which has poor reliability and only detects a limited number of symptoms related to depression.2

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements and capture a variety of distinctive behaviors and activity patterns that are difficult to record through interviews.

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 that was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online assistance or medical care according to the degree of their depression. Participants who scored a high on the CAT-DI of 35 or 65 were given online support by an instructor and those with scores of 75 patients were referred to in-person psychotherapy.

Participants were asked a series of questions at the beginning of the study about their psychosocial and demographic characteristics as well as their socioeconomic status. The questions asked included age, sex and education and marital status, financial status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as how often they drank. Participants also rated their level of depression symptom severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted every other week for participants who received online support and once a week for those receiving in-person treatment.

Predictors of the Reaction to Treatment

Research is focused on individualized depression treatment. Many studies are focused on finding predictors that can help doctors determine the most effective medications to treat each patient. In particular, pharmacogenetics identifies genetic variations that affect how to treatment Depression the body metabolizes antidepressants. This enables doctors to choose the medications that are most likely to work best for each patient, while minimizing the time and effort in trial-and-error procedures and eliminating any side effects that could otherwise hinder the progress of the patient.

Another option is to build prediction models combining clinical data and neural imaging data. These models can be used to identify the most appropriate combination of variables that is predictive of a particular outcome, such as whether or not a particular medication is likely to improve mood and symptoms. These models can be used to determine the response of a patient to a treatment, allowing doctors maximize the effectiveness.

A new generation of machines employs machine learning techniques like the supervised and classification algorithms, regularized logistic regression and tree-based methods to integrate the effects of multiple variables and improve predictive accuracy. These models have been proven to be effective in predicting outcomes of treatment like the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to become the standard of future treatment.

In addition to ML-based prediction models, research into the underlying mechanisms of depression continues. Recent research suggests that the disorder is linked with dysfunctions in specific neural circuits. This theory suggests that a individualized treatment for depression uk for depression will depend on targeted treatments that restore normal function to these circuits.

One way to do this is by using internet-based programs that offer a more personalized and customized experience for patients. One study found that a program on the internet was more effective than standard care in alleviating symptoms and ensuring the best quality of life for people suffering from MDD. In addition, a controlled randomized study of a customized approach to treating depression showed an improvement in symptoms and fewer adverse effects in a large proportion of participants.

Predictors of Side Effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will have minimal or no side effects. Many patients experience a trial-and-error approach, with a variety of medications being prescribed before settling on one that is safe and effective. Pharmacogenetics is an exciting new avenue for a more efficient and targeted approach to choosing antidepressant medications.

A variety of predictors are available to determine the best antidepressant to prescribe, including gene variants, patient phenotypes (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. However, identifying the most reliable and accurate predictors for a particular treatment will probably require randomized controlled trials of significantly larger numbers of participants than those that are typically part of clinical trials. This is due to the fact that the identification of moderators or interaction effects may be much more difficult in trials that take into account a single episode of treatment per person instead of multiple episodes of treatment over a period of time.

Furthermore, the estimation of a patient's response to a specific medication will also likely require information about symptoms and comorbidities in addition to the patient's previous experiences with the effectiveness and tolerability of the medication. There are currently only a few easily assessable sociodemographic variables and clinical variables are reliably related to response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

The application of pharmacogenetics to depression treatment is still in its beginning stages, and many challenges remain. First, a clear understanding of the genetic mechanisms is needed, as is an understanding of what is a reliable predictor of lithium treatment for depression response. Ethics such as privacy and the responsible use of genetic information should also be considered. Pharmacogenetics can be able to, over the long term reduce stigma associated with mental health treatment and improve treatment outcomes. As with all psychiatric approaches, it is important to give careful consideration and implement the plan. At present, the most effective course of action is to offer patients various effective medications for depression treatment psychology and encourage them to speak openly with their doctors about their experiences and concerns.