Be On The Lookout For: How Personalized Depression Treatment Is Taking Over And What You Can Do About It > 자유게시판

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Personalized Depression Treatment

Traditional therapy and medication do not work for many patients suffering from depression. The individual approach to treatment could be the solution.

Cue is an intervention platform that transforms sensor data collected from smartphones into personalized micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to discover their predictors of feature and reveal distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

depression treatment in islam is the leading cause of mental illness around the world.1 Yet, only half of those suffering from the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat patients most likely to respond to specific treatments.

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

The majority of research to so far has focused on sociodemographic and clinical characteristics. These include factors that affect the demographics such as age, sex and educational level, clinical characteristics like symptoms severity and comorbidities and biological indicators such as neuroimaging and genetic variation.

Few studies have used longitudinal data to predict mood of individuals. Few also take into account the fact that mood varies significantly between individuals. It is therefore important to devise methods that permit the analysis and measurement of individual differences in 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 develop algorithms that can identify distinct patterns of behavior and emotions that are different between people.

The team also developed a machine-learning algorithm that can model dynamic predictors for each person's mood for depression. The algorithm integrates the individual characteristics to create an individual "digital genotype" for each participant.

This digital phenotype was correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was low, however (Pearson r = 0,08; P-value adjusted by BH 3.55 x 10 03) and varied widely between individuals.

Predictors of symptoms

Depression is the leading cause of disability in the world, but it is often not properly diagnosed and treated. In addition, a lack of effective interventions and stigmatization associated with depressive disorders stop many individuals from seeking help.

To facilitate personalized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. However, the current methods for predicting symptoms rely on clinical interview, which has poor reliability and only detects a limited number of features associated with depression.2

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

The study included University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for anxiety depression treatment and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical care according to the severity of their depression. Those with a CAT-DI score of 35 65 were given online support by an instructor and those with scores of 75 patients were referred to psychotherapy in-person.

At the beginning, participants answered an array of questions regarding their personal demographics and psychosocial features. These included sex, age and education, as well as work and financial situation; whether they were divorced, married, or single; current suicidal ideas, intent or attempts; as well as the frequency with the frequency they consumed alcohol depression treatment. The CAT-DI was used to rate the severity of depression symptoms on a scale from zero to 100. CAT-DI assessments were conducted each other week for participants that received online support, and every week for those who received in-person care.

Predictors of Treatment Response

Research is focusing on personalization of treatment for depression. Many studies are aimed at finding predictors that can aid clinicians in identifying the most effective medications to treat each patient. Particularly, pharmacogenetics can identify genetic variations that affect the way that the body processes antidepressants. This lets doctors select the medication that will likely work best for each patient, reducing the time and effort needed for trial-and error treatments and eliminating any adverse effects.

Another approach that is promising is to develop predictive models that incorporate clinical data and neural imaging data. These models can be used to determine which variables are most predictive of a specific outcome, such as whether a medication will help with symptoms or mood. These models can also be used to predict the response of a patient to an existing treatment which allows doctors to maximize the effectiveness of their current therapy.

A new generation uses machine learning techniques like algorithms for classification and supervised learning such as regularized logistic regression, and tree-based methods to combine the effects from multiple variables and improve predictive accuracy. These models have been proven to be useful in predicting treatment outcomes for example, the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could be the norm in future medical practice.

In addition to prediction models based on ML, research into the underlying mechanisms of depression is continuing. Recent research suggests that the disorder is connected with dysfunctions in specific neural circuits. This theory suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.

Internet-based interventions are an option to achieve this. They can offer more customized and personalized experience for patients. A study showed that an internet-based program helped improve symptoms and led to a better quality life for MDD patients. In addition, a controlled randomized study of a personalised treatment for depression demonstrated steady improvement and decreased adverse effects in a significant percentage of participants.

Predictors of Side Effects

In the treatment of depression pharmacological treatment one of the most difficult aspects is predicting and determining which antidepressant medication will have no or minimal side negative effects. Many patients have a trial-and error method, involving a variety of medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics is an exciting new way to take an efficient and targeted approach to selecting antidepressant treatments.

A variety of predictors are available to determine which antidepressant to prescribe, including genetic variants, patient phenotypes (e.g. gender, sex or ethnicity) and comorbidities. To determine the most reliable and valid predictors of a specific treatment, randomized controlled trials with larger samples will be required. This is because the detection of moderators or interaction effects can be a lot more difficult in trials that only take into account a single episode of treatment per person, rather than multiple episodes of treatment over a period of time.

Furthermore, the prediction of a patient's response to a particular medication will also likely require information about symptoms and comorbidities as well as the patient's prior subjective experiences with the effectiveness and tolerability of the medication. Currently, only some easily measurable sociodemographic and clinical variables are believed to be reliably associated with the response to MDD, such as gender, age race/ethnicity, SES BMI, the presence of alexithymia, and the severity of depression symptoms.

The application of pharmacogenetics in treatment for depression is in its beginning stages and there are many hurdles to overcome. First, a clear understanding of the genetic mechanisms is required as well as a clear definition of what is a reliable indicator of treatment response. Ethics like privacy, and the responsible use of genetic information should also be considered. In the long term, pharmacogenetics may offer a chance to lessen the stigma that surrounds mental health treatment and to improve the outcomes of those suffering with depression. But, like any approach to psychiatry careful consideration and planning is required. The best option is to offer patients an array of effective depression medication options and encourage them to talk with their physicians about their experiences and concerns.