A. The Most Common Personalized Depression Treatment Debate Isn't As Black And White As You Might Think > 자유게시판

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Personalized Depression Treatment

Traditional therapies and medications do not work for many patients suffering from depression. A customized treatment could be the solution.

Cue is a digital intervention platform that converts passively collected sensor data from smartphones into customized micro-interventions designed to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and uncover distinct features that are able to change mood as time passes.

Predictors of Mood

Depression is a leading cause of mental illness around the world.1 Yet the majority of people with the condition receive treatment. In order to improve outcomes, clinicians need to be able to recognize and treat patients with the highest probability of responding to particular treatments.

The treatment of depression can be personalized to help. Utilizing sensors on mobile phones 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 predict which patients will benefit from the treatments they receive. Two grants totaling more than $10 million will be used to identify biological and behavioral indicators of response.

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

While many of these factors can be predicted from the information in medical records, few studies have utilized longitudinal data to determine predictors of mood in individuals. A few studies also take into consideration the fact that mood can differ significantly between individuals. Therefore, it is important to develop methods which permit the identification and quantification of personal differences between 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 enables the team to develop algorithms that can identify various patterns of behavior and emotion that vary between individuals.

In addition to these modalities, the team developed a machine-learning algorithm to model the changing variables that influence each person's mood. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype was linked to CAT DI scores that are a psychometrically validated symptoms severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.

Predictors of symptoms

Depression is the leading cause of disability in the world, but it is often misdiagnosed and untreated2. Depression disorders are usually not treated due to the stigma that surrounds them and the absence of effective treatments.

To allow for individualized treatment in order to provide a more personalized treatment, identifying patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which are not reliable and only detect a few characteristics that are associated with depression.

Using machine learning 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) with other predictors of symptom severity could improve diagnostic accuracy and increase treatment efficacy for depression. Digital phenotypes can provide continuous, high-resolution measurements as well as capture a wide range of distinctive behaviors and activity patterns that are difficult to document with interviews.

The study comprised 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 herbal depression treatments Grand Challenge. Participants were sent online for assistance or medical care depending on the degree of their depression. Participants who scored a high on the CAT-DI of 35 or 65 students were assigned online support by a coach and those with scores of 75 were sent to in-person clinical care for psychotherapy.

At baseline, participants provided a series of questions about their personal demographics and psychosocial features. The questions asked included education, age, sex and gender as well as marital status, financial status, whether they were divorced or not, the frequency of suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale of 0-100. CAT-DI assessments were conducted every week for those that received online support, and weekly for those receiving in-person care.

Predictors of Treatment Reaction

The development of a personalized depression treatment is currently a research priority, and many studies aim at identifying predictors that will allow clinicians to identify the most effective drugs for each individual. Pharmacogenetics, in particular, uncovers genetic variations that affect how the human body metabolizes drugs. 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 avoid any negative side consequences.

Another approach that is promising is to build prediction models using multiple data sources, such as the clinical information with neural imaging data. These models can then be used meds to treat depression identify the most appropriate combination of variables that is predictive of a particular outcome, like whether or not a drug is likely to improve the mood and symptoms. These models can be used to determine the patient's response to treatment that is already in place, allowing doctors to maximize the effectiveness of their current therapy.

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 several variables to improve the accuracy of predictive. These models have proven to be useful for the prediction of treatment outcomes like the response to antidepressants. These methods are becoming popular in psychiatry and it is likely that they will become the norm for the future of clinical practice.

Research into depression's underlying mechanisms continues, as well as predictive models based on ML. Recent research suggests that the disorder is connected with neural dysfunctions that affect specific circuits. This suggests that the treatment for depression will be individualized built around targeted therapies that target these neural circuits to restore normal function.

Internet-based interventions are an option to achieve this. They can provide a more tailored and individualized experience for patients. One study found that a web-based program improved symptoms and improved quality of 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 proportion of participants.

Predictors of Side Effects

In the natural treatment for depression of depression, a major challenge is predicting and determining which antidepressant medication will have very little or no side effects. Many patients are prescribed various medications before settling on a treatment that is both effective and well-tolerated. Pharmacogenetics offers a new and exciting method of selecting antidepressant medications that is more efficient and targeted.

Many predictors can be used to determine the best way to treat depression antidepressant to prescribe, including genetic variants, phenotypes of patients (e.g., sex or ethnicity) and the presence of comorbidities. However finding the most reliable and valid predictors for a particular treatment is likely to require randomized controlled trials with much larger samples than those normally enrolled in clinical trials. This is due to the fact that it can be more difficult to determine the effects of moderators or interactions in trials that only include one episode per person rather than multiple episodes over a period of time.

In addition the prediction of a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's personal experience of tolerability and effectiveness. At present, only a few easily identifiable sociodemographic and clinical variables appear to be reliable in predicting response to MDD factors, including gender, age, race/ethnicity and SES BMI, the presence of alexithymia, and the severity of depressive symptoms.

Many issues remain to be resolved in the application of pharmacogenetics in the treatment of depression. First, it is important to have a clear understanding and definition of the genetic mechanisms that underlie depression treatment plan cbt (written by pediascape.science), and a clear definition of a reliable indicator of the response to treatment. In addition, ethical issues, such as privacy and the responsible use of personal genetic information must be carefully considered. Pharmacogenetics can eventually reduce stigma associated with mental health treatments and improve treatment outcomes. As with any psychiatric approach, it is important to take your time and carefully implement the plan. For now, the best course of action is to provide patients with a variety of effective depression medication options and encourage them to speak with their physicians about their concerns and experiences.