10 Essentials About Personalized Depression Treatment You Didn't Learn In The Classroom > 자유게시판

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Personalized Depression Treatment

For many people gripped by depression, traditional therapies and medication are ineffective. The individual approach to treatment could be the solution.

Cue is a digital intervention platform that converts passively collected smartphone sensor data into personalized micro-interventions designed to improve mental health. We examined the most effective-fitting personalized ML models to each subject using Shapley values to determine their feature predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

recurrent depression treatment is one of the world's leading causes of mental illness.1 Yet, only half of people suffering from the disorder receive treatment1. To improve outcomes, clinicians must be able to identify and treat patients most likely to respond to certain treatments.

A customized depression treatment without medication treatment plan can aid. By using sensors for mobile phones, an artificial intelligence voice assistant and other digital tools, researchers at the University of Illinois Chicago (UIC) are developing new methods to predict which patients will benefit from the treatments they receive. Two grants worth more than $10 million will be used to determine biological and behavior indicators of response.

The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include demographics like 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. A few studies also take into account the fact that moods can vary significantly between individuals. Therefore, it is crucial to develop methods that allow for the identification of different mood predictors for each person and treatments 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 allows the team to develop algorithms that can systematically identify distinct patterns of behavior and emotions that vary between individuals.

In addition to these methods, the team created a machine learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines the individual differences to produce a unique "digital genotype" for each participant.

This digital phenotype was linked to CAT DI scores, a psychometrically validated symptom severity scale. The correlation was not strong, however (Pearson r = 0,08, P-value adjusted for BH = 3.55 10 03) and varied widely among individuals.

Predictors of Symptoms

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

To facilitate personalized treatment to improve treatment, identifying the predictors of symptoms is important. However, the current methods for predicting symptoms are based on the clinical interview, which is unreliable and only detects a small number of symptoms that are associated with depression.2

Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors with a valid 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, which are difficult to document through interviews and permit continuous and high-resolution measurements.

The study included University of California Los Angeles students with moderate 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 sent online for support or to clinical treatment according to the degree of their depression. Participants with a CAT-DI score of 35 65 were assigned online support with a coach and those with scores of 75 patients were referred to psychotherapy in person.

At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal demographics and psychosocial characteristics. These included age, sex and education, as well as work and financial status; if they were divorced, partnered, or single; current suicidal thoughts, intentions, or attempts; and the frequency with which they drank alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale of 100 to. CAT-DI assessments were conducted each other week for the participants who received online support and every week for those who received in-person support.

Predictors of Treatment Response

The development of a personalized depression treatment is currently a major research area and a lot of studies are aimed at identifying predictors that will allow clinicians to identify the most effective medication for each patient. Particularly, pharmacogenetics is able to identify genetic variants that influence the way that the body processes antidepressants. This enables doctors to choose the medications that are most likely to be most effective for each patient, while minimizing the time and effort involved in trial-and-error procedures and avoid any adverse effects that could otherwise slow the progress of the patient.

Another promising approach is to create prediction models that combine 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 drug will improve mood or symptoms. These models can be used to predict the patient's response to a treatment, allowing doctors to maximize the effectiveness of their treatment.

A new generation of studies utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be useful in predicting treatment options for depression outcomes for example, the response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to become the standard of future clinical practice.

Research into the underlying causes of depression continues, as do predictive models based on ML. Recent research suggests that the disorder is connected with neural dysfunctions that affect specific circuits. This theory suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.

Internet-delivered interventions can be a way to accomplish this. They can provide a more tailored and individualized experience for patients. For example, one study discovered that a web-based treatment was more effective than standard treatment in reducing symptoms and ensuring an improved quality of life for patients with MDD. In addition, a controlled randomized study of a customized approach to treating depression showed sustained improvement and reduced adverse effects in a large number of participants.

Predictors of adverse effects

A major obstacle in individualized depression treatment is predicting which antidepressant medications will cause the least amount of side effects or none at all. Many patients take a trial-and-error method, involving several medications being prescribed before settling on one that is safe and effective. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant medications that is more effective and precise.

Many predictors can be used to determine which antidepressant is best to prescribe, including gene variants, phenotypes of patients (e.g., sex or ethnicity) and the presence of comorbidities. To identify the most reliable and accurate predictors for a particular treatment, controlled trials that are randomized with larger samples will be required. This is due to the fact that the identification of moderators or interaction effects could be more difficult in trials that consider a single episode of treatment per participant instead of multiple episodes of treatment over time.

Additionally, predicting a patient's response will likely require information about the comorbidities, symptoms profiles and the patient's own perception of the effectiveness and tolerability. Currently, only some easily identifiable sociodemographic and clinical variables seem to be reliably associated with the response to MDD, such as gender, age race/ethnicity, BMI and the presence of alexithymia, and the severity of depression symptoms.

The application of pharmacogenetics in treatment for depression is in its infancy and there are many hurdles to overcome. First, it is essential to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, and an understanding of an accurate predictor of treatment response. In addition, ethical issues such as privacy and the ethical use of personal genetic information must be considered carefully. The use of pharmacogenetics may eventually help reduce stigma around mental health treatments and improve the outcomes of treatment resistant depression. However, as with any approach to psychiatry careful consideration and planning is necessary. At present, the most effective option is to provide patients with a variety of effective depression medications and encourage them to talk freely with their doctors about their concerns and experiences.