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

• 페이스북 공유
• 트위터 공유

Personalized Depression Treatment Refractory Depression

For many people gripped by depression, traditional therapies and medications are not effective. Personalized psychological treatment for depression may be the solution.

Cue is an intervention platform for digital devices that translates passively acquired normal sensor data from smartphones into customized micro-interventions designed to improve mental health. We looked at the best-fitting personal ML models for each individual, using Shapley values to determine their characteristic predictors. The results revealed distinct characteristics that were deterministically changing mood over time.

Predictors of Mood

Depression is a leading cause of mental illness around the world.1 Yet, only half of those with the condition receive treatment. To improve the outcomes, doctors must be able to recognize and treat patients who are the most likely to respond to certain treatments.

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from specific treatments. They make use of sensors for mobile phones, a voice assistant with artificial intelligence as well as other digital tools. With two grants awarded totaling over $10 million, they will use these techniques to determine the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.

The majority of research done to the present has been focused on clinical and sociodemographic characteristics. These include demographics such as gender, age and education and clinical characteristics such as symptom severity, comorbidities and biological markers.

A few studies have utilized longitudinal data in order to predict mood of individuals. They have not taken into account the fact that mood can vary significantly between individuals. It is therefore important to devise methods that permit the determination and quantification of the individual differences in mood predictors, treatment effects, 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 create algorithms that can identify various patterns of behavior and emotions that are different between people.

In addition to these modalities the team also developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm blends these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

depression treatment plan cbt is one of the most prevalent causes of disability1 but is often untreated and not diagnosed. In addition, a lack of effective interventions and stigmatization associated with depression disorders hinder many people from seeking help.

To help with personalized treatment, it is essential to identify predictors of symptoms. However, the current methods for predicting symptoms rely on clinical interview, which is not reliable and only detects a small number of symptoms that are associated with depression.2

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes provide a wide range of distinct behaviors and activities that are difficult to record through interviews, and also allow for high-resolution, continuous measurements.

The study enrolled University of California Los Angeles (UCLA) students who were suffering from mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were routed to online assistance or in-person clinics depending on their depression severity. Patients who scored high on the CAT DI of 35 65 were assigned to online support via an online peer coach, whereas those with a score of 75 patients were referred to in-person clinics for psychotherapy.

Participants were asked a series questions at the beginning of the study regarding their demographics and psychosocial characteristics. These included age, sex education, work, and financial status; whether they were partnered, divorced or single; the frequency of suicidal ideation, intent or attempts; and the frequency at that they consumed alcohol depression treatment. The CAT-DI was used to assess the severity of depression symptoms on a scale of 100 to. CAT-DI assessments were conducted every week for those who received online support and once a week for those receiving in-person care.

Predictors of Treatment Response

Research is focusing on personalized treatment for depression. Many studies are focused on finding predictors that can help clinicians identify the most effective drugs for each person. Particularly, pharmacogenetics is able to identify genetic variants that determine the way that the body processes antidepressants. This enables doctors to choose drugs that are likely to be most effective for each patient, minimizing the time and effort in trials and errors, while avoiding side effects that might otherwise hinder progress.

Another option is to build predictive models that incorporate information from clinical studies and neural imaging data. These models can be used to determine which variables are the most predictive of a particular outcome, like whether a medication will improve mood or symptoms. These models can be used to predict the patient's response to treatment, allowing doctors to maximize the effectiveness of their treatment.

A new type of research utilizes machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to blend the effects of several variables and improve the accuracy of predictive. These models have been proven to be useful for the prediction of treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry and it is expected that they will become the standard for the future of clinical practice.

In addition to ML-based prediction models The study of the mechanisms behind depression is continuing. Recent findings suggest that the disorder is linked with dysfunctions in specific neural circuits. This suggests that individualized depression holistic treatment for anxiety and depression will be built around targeted therapies that target these circuits to restore normal functioning.

Internet-delivered interventions can be a way to achieve this. They can provide more customized and personalized experience for patients. One study found that a web-based program improved symptoms and provided a better quality life for MDD patients. Additionally, a randomized controlled trial of a personalized approach to treating depression showed sustained improvement and reduced adverse effects in a significant number of participants.

Predictors of side effects

In the treatment of depression, a major challenge is predicting and identifying which antidepressant medications will have no or minimal side negative effects. Many patients are prescribed a variety drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics offers a fresh and exciting way to select antidepressant medications that is more effective and precise.

There are several variables that can be used to determine the antidepressant that should be prescribed, including genetic variations, phenotypes of patients such as gender or ethnicity and comorbidities. However it is difficult to determine the most reliable and reliable predictors for a particular treatment will probably require randomized controlled trials of considerably larger samples than those typically 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 comprise only one episode per participant instead of multiple episodes spread over time.

Furthermore, the prediction of a patient's reaction to a particular medication will likely also require information about symptoms and comorbidities and the patient's prior subjective experience with tolerability and efficacy. There are currently only a few easily identifiable sociodemographic variables and clinical variables appear to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

Many issues remain to be resolved in the use of pharmacogenetics in the treatment of depression. First it is necessary to have a clear understanding of the genetic mechanisms is essential and an understanding of what is a reliable predictor of treatment response. Additionally, ethical issues such as privacy and the appropriate use of personal genetic information should be considered with care. In the long run, pharmacogenetics may be a way to lessen the stigma associated with mental health care and improve the treatment outcomes for patients with depression. As with all psychiatric approaches it is crucial to give careful consideration and implement the plan. In the moment, it's ideal to offer patients an array of depression medications that are effective and urge patients to openly talk with their physicians.