An Intermediate Guide On Personalized Depression Treatment > 자유게시판

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Personalized Depression Treatment

Traditional therapy and medication are not effective for a lot of people suffering from depression. A customized treatment may be the solution.

Cue is an intervention platform that converts sensors that are passively gathered from smartphones into customized micro-interventions that 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 characteristics that can be used to predict changes in mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only half of those suffering from the condition receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients who are the most likely to respond to certain treatments.

The ability to tailor depression treatments is one method of doing this. Using sensors for mobile phones and 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 which treatments. Two grants totaling more than $10 million will be used to identify biological and behavioral indicators of response.

The majority of research on predictors for depression treatment effectiveness has focused on clinical and sociodemographic characteristics. These include demographics such as gender, age and education and clinical characteristics like symptom severity and comorbidities, as well as biological markers.

Very few studies have used longitudinal data to predict mood of individuals. Few studies also take into consideration the fact that mood can be very different between individuals. Therefore, it is essential to create methods that allow the recognition of different mood predictors for each person 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. The team can then develop algorithms to identify patterns of behavior and emotions that are unique to each person.

The team also developed an algorithm for machine learning to create dynamic predictors for the mood of each person's depression. The algorithm blends these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype has been associated with CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was not strong (Pearson's r = 0.08, BH-adjusted P-value of 3.55 1003) and varied widely among individuals.

Predictors of Symptoms

Depression is a leading cause of disability in the world1, but it is often not properly diagnosed and treated. Depressive disorders are often not treated because of the stigma attached to them, as well as the lack of effective treatments.

To assist in individualized alternative treatment for depression and anxiety, it is crucial to identify predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only identify a handful of symptoms associated with depression.

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements as well as capture a variety of distinct behaviors and patterns that are difficult to capture using 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, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment according to the severity of their depression. Participants who scored a high on the CAT DI of 35 65 students were assigned online support with the help of a coach. Those with a score 75 patients were referred for in-person psychotherapy.

At the beginning of the interview, participants were asked an array of questions regarding their personal characteristics and psychosocial traits. These included age, sex and education, as well as work and financial status; whether they were divorced, partnered, or single; current suicidal ideation, intent, or attempts; and the frequency at the frequency they consumed alcohol. Participants also rated their degree of depression severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted every week for those that received online support, and weekly for those receiving in-person homeopathic treatment for depression.

Predictors of Treatment Reaction

Research is focusing on personalization of treatment for depression. Many studies are focused on finding predictors that can aid clinicians in identifying the most effective drugs to treat each individual. In particular, pharmacogenetics identifies genetic variants that determine the way that the body processes antidepressants. This allows doctors select medications that are most likely to work for each patient, while minimizing time and effort spent on trial-and-error treatments and avoiding any side negative effects.

Another approach that is promising is to build prediction models using multiple data sources, including clinical information and neural imaging data. These models can be used to determine the best combination of variables that are predictors of a specific outcome, like whether or not a drug is likely to improve the mood and symptoms. These models can also be used to predict the response of a patient to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of current treatment.

A new type of research employs machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables to improve predictive accuracy. These models have been proven to be effective in predicting outcomes of treatment for example, the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the norm in the future clinical practice.

The study of depression's underlying mechanisms continues, in addition to predictive models based on ML. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This theory suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.

One way to do this is to use internet-based interventions which can offer an individualized and tailored experience for patients. One study found that a program on the internet was more effective than standard treatment in alleviating symptoms and ensuring the best quality of life for people with MDD. In addition, a controlled randomized trial of a personalized treatment for depression demonstrated an improvement in symptoms and fewer adverse effects in a significant number of participants.

Predictors of adverse effects

In the treatment of depression, a major challenge is predicting and identifying which antidepressant medication will have minimal or zero negative side effects. Many patients have a trial-and error approach, with a variety of medications prescribed until they find one that is effective and tolerable. Pharmacogenetics offers a fascinating new avenue for a more efficient and targeted approach to selecting antidepressant treatments.

There are several predictors that can be used to determine the antidepressant that should be prescribed, such as gene variations, patient phenotypes such as gender or ethnicity, and the presence of comorbidities. To identify the most reliable and accurate predictors for a particular treatment, random controlled trials with larger samples will be required. This is because it may be more difficult to determine interactions or moderators in trials that only include one episode per person instead of multiple episodes spread over a period of time.

Additionally to that, predicting a patient's reaction will likely require information on comorbidities, symptom profiles and the patient's own perception of the effectiveness and tolerability. There are currently only a few easily identifiable sociodemographic variables and clinical variables are reliably related to response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

The application of pharmacogenetics in treatment for depression is in its early stages and there are many obstacles to overcome. First it is necessary to have a clear understanding of the genetic mechanisms is essential as well as a clear definition of what is a reliable predictor of treatment response. Ethics, such as privacy, and the ethical use of genetic information are also important to consider. Pharmacogenetics could be able to, over the long term, reduce stigma surrounding treatments for mental illness and improve treatment outcomes. Like any other psychiatric treatment it is crucial to carefully consider and implement the plan. For now, the best treatment for anxiety depression course of action is to offer patients a variety of effective medications for depression and encourage them to speak freely with their doctors about their concerns and experiences.