Company Logo

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

Personalized Depression Treatment

For many people gripped by depression, traditional therapy and medications are not effective. The individual approach to treatment could be the answer.

Cue is an intervention platform 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 understand their predictors of feature and reveal distinct characteristics that can be used to predict changes in mood as time passes.

Predictors of Mood

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

A customized depression treatment 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 specific treatments. They are using mobile phone sensors and a voice assistant incorporating artificial intelligence, and other digital tools. With two grants totaling over $10 million, they will employ these techniques to determine biological and behavioral predictors of response to antidepressant medications and psychotherapy.

The majority of research conducted to date has focused on clinical and sociodemographic characteristics. These include factors that affect the demographics like age, sex and education, clinical characteristics including symptoms severity and comorbidities and biological markers like neuroimaging and genetic variation.

Very few studies have used longitudinal data in order to predict mood of individuals. Many studies do not take into consideration the fact that mood can be very different between individuals. Therefore, it is important to devise methods that permit the identification and quantification of individual differences between mood predictors treatments, mood predictors, 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 create algorithms that can systematically identify various patterns of behavior and emotion that differ between individuals.

The team also created a machine-learning algorithm that can create dynamic predictors for each person's mood for depression. The algorithm combines these personal differences into a unique "digital phenotype" for each participant.

This digital phenotype was associated with CAT DI scores that are a psychometrically validated symptoms 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 across individuals.

Predictors of symptoms

Depression is among the leading causes of disability1, but it is often untreated and not diagnosed. In addition an absence of effective treatments and stigmatization associated with depressive disorders prevent many from seeking treatment.

To assist in individualized treatment, it is crucial to identify predictors of symptoms. However, the methods used to predict symptoms depend on the clinical interview which has poor reliability and only detects a limited variety of characteristics associated with depression treatment in uk.2

Using machine learning to blend continuous digital behavioral phenotypes of a person captured by sensors on smartphones and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) together with other predictors of severity of symptoms has the potential to improve the accuracy of diagnosis and the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements. They also capture a variety of unique behaviors and activity patterns that are difficult to capture using interviews.

The study included University of California Los Angeles students with mild to severe depression symptoms who were taking part in the Screening and alternative treatment for depression And anxiety for Anxiety and Depression program29 that was developed as part of the UCLA pregnancy depression treatment Grand Challenge. Participants were directed to online support or in-person clinical what treatment is there for depression depending on their depression severity. Patients with a CAT DI score of 35 or 65 were assigned to online support with an online peer coach, whereas those who scored 75 patients were referred to clinics in-person for psychotherapy.

At baseline, participants provided the answers to a series of questions concerning their personal characteristics and psychosocial traits. The questions covered age, sex, and education, financial status, marital status as well as whether they divorced or not, current suicidal thoughts, intentions or attempts, as well as how often they drank. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. The CAT-DI assessment was conducted every two weeks for participants who received online support and weekly for those who received in-person support.

Predictors of the Reaction to Treatment

Research is focusing on personalization of depression treatment. Many studies are aimed at finding predictors that can help clinicians identify the most effective medications to treat each patient. In particular, pharmacogenetics identifies genetic variations that affect the way that the body processes antidepressants. This enables doctors to choose the medications that are most likely to work best for each patient, minimizing the time and effort involved in trial-and-error treatments and avoid any adverse effects that could otherwise hinder progress.

Another promising method is to construct models of prediction using a variety of data sources, combining data from clinical studies and neural imaging data. These models can be used to determine the best combination of variables that are predictive of a particular outcome, like whether or not a particular medication will improve the mood and symptoms. These models can be used to determine the response of a patient to treatment, allowing doctors to maximize the effectiveness.

A new generation uses machine learning techniques like the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of several variables and increase the accuracy of predictions. These models have been demonstrated to be effective in predicting outcomes of treatment like the response to antidepressants. These methods are becoming popular in psychiatry, and it is likely that they will become the standard for the future of clinical practice.

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

Internet-delivered interventions can be an option to achieve this. They can provide a more tailored and individualized experience for patients. A study showed that an internet-based program improved symptoms and led to a better quality life for MDD patients. Furthermore, a randomized controlled study of a customized approach to depression treatment showed sustained improvement and reduced adverse effects in a significant number of participants.

Predictors of side effects

In the treatment of depression the biggest challenge is predicting and determining which antidepressant medications will have no or minimal side negative effects. Many patients are prescribed a variety drugs before they find a drug that is safe and effective. Pharmacogenetics provides an exciting new avenue for a more efficient and targeted approach to choosing antidepressant medications.

Many predictors can be used to determine which antidepressant to prescribe, including gene variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. However finding the most reliable and accurate factors that can predict the effectiveness of 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 because the detection of interactions or moderators could be more difficult in trials that only focus on a single instance of treatment per participant instead of multiple sessions of treatment over a period of time.

Additionally 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 few easily identifiable sociodemographic and clinical variables are believed to be reliably associated with the response to MDD like age, gender, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depression symptoms.

Many challenges remain when it comes to the use of pharmacogenetics to treat depression. First, it is important to have a clear understanding and definition of the genetic mechanisms that cause depression, as well as an understanding of an accurate indicator of the response to treatment. Additionally, ethical issues such as privacy and the appropriate use of personal genetic information, must be carefully considered. In the long term pharmacogenetics can offer a chance to lessen the stigma that surrounds mental health treatment and improve treatment for panic attacks and depression outcomes for those struggling with depression. However, as with any other psychiatric treatment, careful consideration and implementation is required. At present, the most effective course of action is to offer patients a variety of effective depression medications and encourage them to talk with their physicians about their experiences and concerns.