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

Personalized Depression Treatment

Traditional treatment and medications don't work for a majority of patients suffering from prenatal depression treatment. A customized treatment may be the solution.

Cue is an intervention platform for digital devices that transforms passively acquired smartphone sensor data into personalized micro-interventions that improve mental health. We parsed 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 with time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only about half of those suffering from the condition receive treatment1. To improve the outcomes, healthcare professionals must be able to recognize and treat patients with the highest probability of responding to certain treatments.

Personalized depression treatment can help. By using sensors on mobile phones, an artificial intelligence voice assistant and other digital tools, researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from the treatments they receive. Two grants totaling more than $10 million will be used to determine biological and behavioral factors that predict response.

To date, the majority of research into predictors of depression non drug treatment for anxiety and depression effectiveness has been focused on clinical and sociodemographic characteristics. These include demographics such as age, gender and education, as well as clinical aspects such as symptom severity and comorbidities, as well as biological markers.

Few studies have used longitudinal data in order to determine mood among individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is critical to develop methods that allow for the recognition of the 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 enables the team to create algorithms that can systematically identify distinct patterns of behavior and emotions that differ between individuals.

In addition to these modalities the team developed a machine-learning algorithm that models the dynamic predictors of each person's depressed mood. The algorithm integrates the individual differences to create an individual "digital genotype" 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 tinny (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is one of the most prevalent causes of disability1, but it is often underdiagnosed and undertreated2. In addition the absence of effective treatments and stigma associated with depressive disorders prevent many people from seeking help.

To aid in the development of a personalized treatment, it is essential to determine the predictors of symptoms. However, current prediction methods rely on clinical interview, which is not reliable and only detects a small number of features related to depression.2

Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavior phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes capture a large number of distinct behaviors and activities that are difficult to record through interviews, and also allow for continuous, high-resolution measurements.

The study involved University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical treatment according to the severity of their depression. Participants with a CAT-DI score of 35 or 65 were assigned to online support with a peer coach, while those who scored 75 patients were referred to in-person psychotherapy.

Participants were asked a series of questions at the beginning of the study about their demographics and psychosocial characteristics. These included age, sex, education, work, and financial status; if they were partnered, divorced, or single; current suicidal ideas, intent or attempts; as well as the frequency at that they consumed alcohol. Participants also rated their level of depression symptom severity on a 0-100 scale using the CAT-DI. The CAT DI assessment was carried out every two weeks for participants who received online support, and weekly for those who received in-person support.

Predictors of Treatment Response

A customized treatment for depression is currently a research priority, and many studies aim at identifying predictors that allow clinicians to identify the most effective drugs for each patient. Particularly, pharmacogenetics can identify genetic variants that influence the way that the body processes antidepressants. This lets doctors choose the medications that are most likely to work for each patient, while minimizing the amount of time and effort required for trial-and-error treatments and avoiding any side effects.

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

A new generation uses 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 treatment outcomes such as the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the standard of future treatment.

In addition to ML-based prediction models The study of the mechanisms that cause depression is continuing. Recent findings suggest that depression is connected to dysfunctions in specific neural networks. This suggests that the treatment for depression will be individualized focused on treatments that target these circuits in order to restore normal function.

One method to achieve this is by using internet-based programs that offer a more individualized and personalized experience for patients. For instance, one study found that a web-based program was more effective than standard treatment in alleviating symptoms and ensuring an improved quality of life for people suffering from MDD. In addition, a controlled randomized trial of a personalized approach to treating depression showed steady improvement and decreased side effects in a significant proportion of participants.

Predictors of adverse effects

A major challenge in personalized depression treatment is predicting the antidepressant medications that will have minimal or no side effects. Many patients are prescribed a variety medications before settling on a treatment that is effective and tolerated. Pharmacogenetics offers a fascinating new method for an efficient and targeted method of selecting antidepressant therapies.

A variety of predictors are available to determine which antidepressant is best to prescribe, including gene variants, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. However it is difficult to determine the most reliable and valid predictors for a particular treatment is likely to require controlled, randomized trials with much larger samples than those typically enrolled in clinical trials. This is because it could be more difficult to determine moderators or interactions in trials that comprise only one episode per person rather than multiple episodes over a period of time.

Additionally to that, predicting a patient's reaction will likely require information on the severity of symptoms, comorbidities and the patient's personal experience of tolerability and effectiveness. Currently, only some easily identifiable sociodemographic and clinical variables seem to be reliably associated with the severity of MDD factors, including age, gender, race/ethnicity and SES, BMI and the presence of alexithymia and the severity of depressive symptoms.

The application of pharmacogenetics to depression drug treatment for depression is still in its beginning stages and there are many hurdles to overcome. First it is necessary to have a clear understanding of the genetic mechanisms is required and an understanding of what treatment for depression is a reliable indicator of treatment response. In addition, ethical issues, such as privacy and the ethical use of personal genetic information, must be carefully considered. In the long run the use of pharmacogenetics could provide an opportunity to reduce the stigma that surrounds mental health care and improve the outcomes of those suffering with depression. Like any other psychiatric treatment, it is important to give careful consideration and implement the plan. In the moment, it's ideal meds to treat anxiety and depression offer patients various depression medications that are effective and encourage patients to openly talk with their physicians.