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

Personalized Depression Treatment

For many suffering from depression, traditional therapy and medication are ineffective. A customized treatment may be the answer.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions 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 uncover distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

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

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit most from certain treatments. They are using sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence and other digital tools. Two grants worth more than $10 million will be used to discover biological and behavior predictors of response.

So far, the majority of research on factors that predict depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographic factors such as age, sex and education, clinical characteristics such as the severity of symptoms 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 consider the fact that mood can vary significantly between individuals. Therefore, it is important to devise methods that permit the determination and quantification of the individual differences in mood predictors and treatment effects, for instance.

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 create algorithms that can detect distinct patterns of behavior and emotion that are different between people.

The team also developed an algorithm for machine learning to create dynamic predictors for the mood of each person's depression. The algorithm combines these personal differences into a unique "digital phenotype" for each participant.

This digital phenotype was found to be associated with CAT-DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.

Predictors of symptoms

Depression is the most common reason for disability across the world1, however, it is often misdiagnosed and untreated2. In addition an absence of effective interventions and stigma associated with depressive disorders prevent many people from seeking help.

To assist in individualized treatment, it is important to identify predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which aren't reliable and only detect a few characteristics that are associated with depression.

Machine learning is used to blend continuous digital behavioral phenotypes of a person captured through smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory, CAT-DI) along with other indicators of severity of symptoms can increase the accuracy of diagnostics and treatment efficacy for hormonal depression treatment. Digital phenotypes permit continuous, high-resolution measurements and capture a wide range of distinct behaviors and patterns that are difficult to document through interviews.

The study enrolled University of California Los Angeles (UCLA) students with mild to severe depression symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical treatment in accordance with their severity of depression. Participants who scored a high on the CAT-DI of 35 or 65 students were assigned online support with a coach and those with scores of 75 patients were referred to psychotherapy in-person.

Participants were asked a series questions at the beginning of the study concerning their psychosocial and demographic characteristics as well as their socioeconomic status. The questions asked included age, sex, and education and financial status, marital status, whether they were divorced or not, current suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also scored their level of depression symptom severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted every week for those who received online support and once a week for those receiving in-person support.

Predictors of Treatment Reaction

Research is focusing on personalized treatment for panic attacks and depression for depression. Many studies are focused on finding predictors, which can help clinicians identify the most effective drugs to treat each individual. Particularly, pharmacogenetics can identify genetic variants that influence how to treat depression and anxiety without medication the body metabolizes antidepressants. This lets doctors select the medication that will likely work best for each patient, while minimizing the amount of time and effort required for trial-and error treatments and avoiding any side consequences.

Another approach that is promising is to develop predictive models that incorporate the clinical data with neural imaging data. These models can be used to identify the variables that are most predictive of a specific outcome, like whether a drug will improve symptoms or mood. These models can also be used to predict the response of a patient to a psychological treatment for depression they are currently receiving and help doctors maximize the effectiveness of current treatment.

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

Research into the underlying causes of depression continues, as well as predictive models based on ML. Recent findings suggest that the disorder is linked with neurodegeneration in particular circuits. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.

One method of doing this is by using internet-based programs that can provide a more individualized and tailored experience ect for treatment resistant depression patients. A study showed that a web-based program improved symptoms and provided a better quality of life for MDD patients. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed an improvement in symptoms and fewer adverse effects in a large percentage of participants.

Predictors of adverse effects

A major challenge in personalized depression treatment involves identifying and predicting which antidepressant medications will have very little or no side effects. Many patients are prescribed a variety of drugs before they find a drug that is effective and tolerated. Pharmacogenetics offers a fascinating new method for an efficient and targeted approach to choosing antidepressant medications.

There are several predictors that can be used to determine the antidepressant that should be prescribed, including gene variations, phenotypes of the patient like gender or ethnicity, and co-morbidities. To identify the most reliable and valid predictors for a specific treatment, randomized controlled trials with larger sample sizes will be required. This is because the detection of moderators or interaction effects can be a lot more difficult in trials that only consider a single episode of treatment per participant instead of multiple episodes of treatment over time.

Furthermore, the estimation of a patient's response to a particular medication will also likely require information about comorbidities and symptom profiles, and the patient's personal experience with tolerability and efficacy. At present, only a handful of easily measurable sociodemographic variables as well as clinical variables appear to be reliably related to response to MDD. These include gender, age, race/ethnicity as well as BMI, SES and the presence of alexithymia.

Many issues remain to be resolved when it comes to the use of pharmacogenetics in the treatment of depression. First it is necessary to have a clear understanding of the underlying genetic mechanisms is needed and an understanding of what treatments are available for depression constitutes a reliable predictor for [empty] treatment response. In addition, ethical concerns like privacy and the ethical use of personal genetic information, [Redirect Only] must be carefully considered. The use of pharmacogenetics may be able to, over the long term reduce stigma associated with mental health treatments and improve treatment outcomes. As with all psychiatric approaches it is essential to take your time and carefully implement the plan. For now, it is best to offer patients an array of depression medications that work and encourage patients to openly talk with their physicians.