• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

Personalized Depression Treatment

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

Cue is an intervention platform that converts 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 to discover their characteristic predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

Depression is the leading cause of mental illness in the world.1 Yet only half of those with the condition receive treatment. In order to improve outcomes, clinicians need to be able to recognize and treat patients who have the highest probability of responding to certain treatments.

Personalized depression treatment without meds treatment can help. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from certain treatments. They use sensors on mobile phones, a voice assistant with artificial intelligence and other digital tools. Two grants worth more than $10 million will be used to determine biological and behavioral factors that predict response.

The majority of research to the present has been focused on clinical and sociodemographic characteristics. These include demographic variables such as age, gender and educational level, clinical characteristics like symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

Few studies have used longitudinal data in order to predict mood in individuals. A few studies also take into account the fact that mood can differ significantly between individuals. Therefore, it is crucial to develop methods which permit the analysis and measurement of individual 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 behaviour and emotions that are unique to each person.

The team also developed a machine-learning algorithm that can 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.

This digital phenotype was found to be associated with CAT-DI scores, which is a psychometrically validated severity scale for symptom severity. However the correlation was tinny (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 treatment without medication - linked site - is a leading cause of disability around the world1, but it is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigma associated with depression treatment psychology disorders hinder many individuals from seeking help.

To allow for individualized treatment to improve treatment, identifying the patterns that can predict symptoms is essential. The current prediction methods rely heavily on clinical interviews, which aren't reliable and only identify a handful of characteristics that are associated with depression.

Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to provide a wide range of unique actions and behaviors that are difficult to record through interviews and permit continuous, high-resolution measurements.

The study included University of California Los Angeles (UCLA) students with mild to severe depression symptoms. enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were directed to online assistance or medical care based on the severity of their depression. Patients who scored high on the CAT-DI of 35 or 65 were assigned to online support with a peer coach, while those who scored 75 patients were referred to psychotherapy in-person.

At baseline, participants provided the answers to a series of questions concerning their personal demographics and psychosocial features. The questions asked included age, sex, and education, marital status, financial status and whether they were divorced or not, the frequency of suicidal thoughts, intent or attempts, and the frequency with which they consumed alcohol. The CAT-DI was used meds to treat anxiety and depression assess the severity of depression-related symptoms on a scale ranging from 0-100. The CAT-DI test was carried out every two weeks for participants who received online support, and weekly for those who received in-person care.

Predictors of Treatment Response

Research is focusing on personalized treatment for depression. Many studies are focused on finding predictors that can help doctors determine the most effective medications to treat each individual. Particularly, pharmacogenetics can identify genetic variations that affect how the body's metabolism reacts to antidepressants. This enables doctors to choose drugs that are likely to work best for each patient, while minimizing the time and effort involved in trial-and-error procedures and avoid any adverse effects that could otherwise slow the progress of the patient.

Another approach that is promising is to develop prediction models that combine clinical data and neural imaging data. These models can be used to determine which variables are most predictive of a specific outcome, like whether a drug will improve mood or symptoms. These models can be used to predict the patient's response to treatment, allowing doctors maximize the effectiveness.

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

In addition to the ML-based prediction models, research into the mechanisms that cause depression continues. 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 upon targeted therapies that restore normal functioning to these circuits.

One method to achieve this is to use internet-based interventions which can offer an individualized and personalized experience for patients. One study found that an internet-based program helped improve symptoms and improved quality of life for MDD patients. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed steady improvement and decreased adverse effects in a significant percentage of participants.

Predictors of adverse effects

A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed a variety medications before finding a medication that is both effective and well-tolerated. Pharmacogenetics offers a new and exciting method to choose antidepressant medications that is more efficient and targeted.

There are several predictors that can be used to determine which antidepressant should be prescribed, including genetic variations, patient phenotypes like gender or ethnicity and comorbidities. To determine the most reliable and accurate predictors for a particular treatment, controlled trials that are randomized with larger samples will be required. This is because the detection of interaction effects or moderators can be a lot more difficult in trials that only take into account a single episode of treatment per person instead of multiple sessions of treatment over time.

Furthermore the estimation of a patient's response to a specific medication will likely also require information about comorbidities and symptom profiles, as well as the patient's personal experience with tolerability and efficacy. At present, only a few easily assessable sociodemographic and clinical variables are believed to be reliably associated with the response to MDD like age, gender, race/ethnicity and SES BMI and the presence of alexithymia, and the severity of depressive symptoms.

The application of pharmacogenetics to depression treatment is still in its early stages and there are many obstacles to overcome. First, it is essential to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, and an understanding of an accurate predictor of treatment response. Ethics such as privacy and the responsible use genetic information should also be considered. Pharmacogenetics could, in the long run reduce stigma associated with mental health treatment and improve treatment outcomes. Like any other psychiatric treatment it is crucial to take your time and carefully implement the plan. The best option is to offer patients an array of effective depression medications and encourage them to talk openly with their doctors about their experiences and concerns.