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

Personalized depression treatment goals Treatment

Traditional therapies and medications are not effective for a lot of people suffering from depression. The individual approach to treatment could be the answer.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into personalized micro-interventions to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to understand their predictors of feature and reveal distinct characteristics that can be used to predict changes in mood as time passes.

Predictors of Mood

Depression is one of the leading causes of mental illness.1 Yet, only half of people suffering from the condition receive treatment1. In order to improve outcomes, doctors must be able to recognize and treat patients who have the highest likelihood of responding to specific treatments.

A customized depression treatment plan can aid. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit most from certain treatments for depression. They make use of sensors on mobile phones, a voice assistant with artificial intelligence as well as other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavior indicators of response.

The majority of research to so far has focused on clinical and sociodemographic characteristics. These include demographics like age, gender and education and clinical characteristics like symptom severity, comorbidities and biological markers.

While many of these variables can be predicted by the information in medical records, very few studies have used longitudinal data to determine the causes of mood among individuals. Few studies also take into account the fact that moods can be very different between individuals. Therefore, it is crucial to develop methods which 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. The team will then create algorithms to identify patterns of behaviour and emotions that are unique to each individual.

The team also developed a machine learning algorithm to identify dynamic predictors of each person's mood for depression. The algorithm blends the individual differences to create an individual "digital genotype" for each participant.

This digital phenotype was found to be associated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. 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 a leading cause of disability around the world1, but it is often misdiagnosed and untreated2. Depression disorders are usually not treated because of the stigma attached to them, as well as the lack of effective treatments.

To help with personalized treatment, it is important to determine the predictors of symptoms. However, the methods used to predict symptoms are based on the clinical interview, which has poor reliability and only detects a small number of symptoms associated with depression.2

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements as well as capture a wide range of distinctive behaviors and activity patterns that are difficult to record using interviews.

The study involved University of California Los Angeles students who had mild to severe Depression Treatment History symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment according to the severity of their depression. Patients who scored high on the CAT-DI scale of 35 or 65 were assigned to online support with an online peer coach, whereas those who scored 75 patients were referred for in-person psychotherapy.

At baseline, participants provided the answers to a series of questions concerning their personal demographics and psychosocial features. The questions asked included age, sex and education and financial status, marital status and whether they were divorced or not, the frequency of suicidal ideas, intent or attempts, and how often they drank. Participants also rated their level of depression symptom severity on a scale of 0-100 using the CAT-DI. CAT-DI assessments were conducted every week for those who received online support and every week for those who received in-person support.

Predictors of Treatment Response

Research is focused on individualized depression treatment. Many studies are aimed at finding predictors that can help clinicians identify the most effective drugs for each person. Pharmacogenetics, in particular, uncovers genetic variations that affect how the human body metabolizes drugs. This allows doctors select medications that are most likely to work for each patient, reducing the time and effort needed for trial-and-error treatments and avoid any negative side consequences.

Another approach that is promising is to build models for prediction using multiple data sources, such as clinical information and neural imaging data. These models can be used to determine which variables are most predictive of a particular outcome, such as whether a drug will improve symptoms or mood. These models can be used to determine the response of a patient to a treatment, which will help doctors to maximize the effectiveness of their treatment.

A new era of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables and increase predictive accuracy. These models have proven to be effective in forecasting treatment outcomes, such as 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.

In addition to the ML-based prediction models research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is connected to the malfunctions of certain neural networks. This theory suggests that individual depression treatment will be based on targeted therapies that target these circuits in order to restore normal functioning.

Internet-based interventions are a way to achieve this. They can offer a more tailored and individualized experience for patients. One study found that an internet-based program helped improve symptoms and improved quality of life for MDD patients. In addition, a controlled randomized study of a personalised approach to treating depression showed an improvement in symptoms and fewer adverse effects in a large percentage of participants.

Predictors of adverse effects

A major issue in personalizing depression treatment is predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed a variety of drugs before they find a drug that is safe and effective. Pharmacogenetics offers a fascinating new method for an effective and precise method of selecting antidepressant therapies.

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 identify the most reliable and reliable predictors for a specific treatment, randomized controlled trials with larger sample sizes will be required. This is because the identifying of interactions or moderators could be more difficult in trials that only focus on a single instance of treatment per patient instead of multiple sessions of treatment over a period of time.

Additionally the prediction of a patient's response to a specific medication is likely to require information about the symptom profile and comorbidities, and the patient's personal experience with tolerability and efficacy. Currently, only some easily assessable sociodemographic and clinical variables are believed medicines to treat depression be reliably associated with response to MDD factors, including age, gender, race/ethnicity and SES, BMI and the presence of alexithymia, and the severity of depressive symptoms.

The application of pharmacogenetics to extreme depression treatment treatment is still in its beginning stages and there are many hurdles to overcome. First, it is important to have a clear understanding and definition of the genetic mechanisms that cause depression, and a clear definition of a reliable predictor of treatment response. Ethics such as privacy and the responsible use of genetic information are also important to consider. In the long term pharmacogenetics can be a way to lessen the stigma that surrounds mental health treatment and improve the treatment outcomes for patients with depression. But, like any other psychiatric treatment, careful consideration and implementation is necessary. At present, it's ideal to offer patients a variety of medications for depression that are effective and urge patients to openly talk with their physicians.