30 Inspirational Quotes On Personalized Depression Treatment > 자유게시판

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

Personalized Depression Treatment

Traditional treatment and medications do not work for many people who are depressed. A customized treatment could be the answer.

Cue is an intervention platform for digital devices that transforms passively acquired smartphone sensor data into personalized micro-interventions to improve mental health. We analyzed the best-fitting personalized ML models to each subject, using Shapley values to determine their characteristic predictors. This revealed distinct features that deterministically changed mood over 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 disorder receive treatment1. To improve outcomes, doctors must be able to recognize and treat patients with the highest probability of responding to specific treatments.

A customized depression treatment plan can aid. Utilizing mobile phone sensors and 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 which treatments. Two grants worth more than $10 million will be used to identify biological and behavior indicators of response.

The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include demographic variables like age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.

While many of these factors can be predicted from information available in medical records, very few studies have employed longitudinal data to study the factors that influence mood in people. A few studies also take into consideration the fact that mood can vary significantly between individuals. It is therefore important to devise methods that allow for the determination and quantification of the individual differences in mood predictors treatments for depression, 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. The team can then develop algorithms to recognize patterns of behaviour and emotions that are unique to each person.

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

The digital phenotype was associated with CAT-DI scores, which is a psychometrically validated severity scale for symptom severity. The correlation was not strong however (Pearson r = 0,08; BH adjusted P-value 3.55 10 03) and varied widely between individuals.

Predictors of Symptoms

Depression is among the world's leading causes of disability1 but is often not properly diagnosed and treated. Depression disorders are rarely treated because of the stigma that surrounds them and the lack of effective treatments.

To aid in the development of a personalized treatment, it is important to identify predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which aren't reliable and only detect a few features associated with depression.

Machine learning can be used to integrate continuous digital behavioral phenotypes of a person captured by smartphone sensors 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 diagnostic accuracy and increase the effectiveness of treatment for depression. Digital phenotypes can be used to capture a large number of unique behaviors and activities that are difficult to record through interviews, and also allow for high-resolution, continuous measurements.

The study involved University of California Los Angeles students with moderate to severe depression symptoms who were taking part 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 based on the degree of their depression. Participants who scored a high on the CAT DI of 35 or 65 students were assigned online support with an instructor and those with a score 75 were routed to in-person clinical care for psychotherapy.

At baseline, participants provided the answers to a series of questions concerning their personal characteristics and psychosocial traits. The questions asked included age, sex and education and financial status, marital status, whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. Participants also scored their level of depression treatment free symptom severity on a 0-100 scale using the CAT-DI. The CAT-DI test was performed every two weeks for those who received online support, and weekly for those who received in-person support.

Predictors of Treatment Response

Research is focusing on personalized depression treatment. Many studies are focused on identifying predictors, which will help clinicians identify the most effective medications to treat each patient. Pharmacogenetics, in particular, identifies genetic variations that determine the way that our bodies process drugs. This lets doctors select the medication 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 consequences.

Another promising method is to construct prediction models using multiple data sources, combining the clinical information with neural imaging data. These models can be used to identify which variables are the 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 to maximize the effectiveness.

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

Research into depression's underlying mechanisms continues, as well as ML-based predictive models. Recent research suggests that depression is linked to the dysfunctions of specific neural networks. This suggests that an individualized what treatment is there for depression for depression will be based on targeted therapies that restore normal functioning to these circuits.

One way to do this is through internet-delivered interventions which can offer an personalized and customized experience for patients. A study showed that an internet-based program helped improve symptoms and led to a better quality of life for MDD patients. In addition, a controlled randomized trial of a personalized approach to treating depression showed sustained improvement and reduced side effects in a significant proportion of participants.

Predictors of adverse effects

In the treatment of depression a major challenge is predicting and identifying the antidepressant that will cause very little or no adverse effects. Many patients are prescribed a variety of medications before finding a medication that is effective and tolerated. Pharmacogenetics offers a fresh and exciting method to choose antidepressant medications that is more effective and specific.

Several predictors may be used to determine the best antidepressant to prescribe, such as gene variations, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. To determine the most reliable and accurate predictors for a particular treatment, randomized controlled trials with larger samples will be required. This is because the detection of interaction effects or moderators could be more difficult in trials that only consider a single episode of treatment per person instead of multiple sessions of treatment over time.

Furthermore to that, predicting a patient's reaction will likely require information about the severity of symptoms, comorbidities and the patient's personal perception of effectiveness and tolerability. At present, only a few easily identifiable sociodemographic and clinical variables appear to be reliable in predicting the response to MDD, such as age, gender race/ethnicity, SES BMI and the presence of alexithymia, and the severity of depressive symptoms.

There are many challenges to overcome in the use of pharmacogenetics for depression treatment. First, it is essential to have a clear understanding and definition of the genetic mechanisms that cause postpartum depression treatment, as well as an accurate definition of an accurate predictor of treatment response. In addition, ethical issues, such as privacy and the ethical use of personal genetic information should be considered with care. In the long run, pharmacogenetics may be a way to lessen the stigma that surrounds mental health treatment and to improve the treatment outcomes for patients with depression. But, like all approaches to psychiatry, careful consideration and implementation is necessary. At present, the most effective option is to provide patients with an array of effective depression medication options and encourage them to speak openly with their doctors about their experiences and concerns.