Watch Out: How Personalized Depression Treatment Is Taking Over And What We Can Do About It > 자유게시판

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

Personalized Depression Treatment

For many people gripped by depression, traditional therapies and medications are not effective. The individual approach to treatment could be the answer.

Cue is a digital intervention platform that transforms passively acquired smartphone sensor data into personalized micro-interventions designed to improve mental health. We analysed 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 the leading cause of mental depression treatment illness in the world.1 Yet only half of those affected receive treatment. To improve outcomes, doctors must be able to identify and treat patients with the highest chance of responding to particular treatments.

The treatment of depression can be personalized to help. By using sensors on mobile phones and an artificial intelligence voice assistant and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new holistic ways to treat depression to determine which patients will benefit from which treatments. Two grants worth more than $10 million will be used to determine biological and behavioral predictors of response.

The majority of research conducted to so far has focused on clinical and sociodemographic characteristics. These include demographic factors such as age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these factors can be predicted from information in medical records, only a few studies have employed longitudinal data to study the causes of mood among individuals. Many studies do not take into consideration the fact that moods vary significantly between individuals. Therefore, it is crucial to develop methods that permit the identification of individual differences in mood predictors 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 create algorithms that can identify different patterns of behavior and emotion that differ between individuals.

The team also devised an algorithm for machine learning to identify dynamic predictors of each person's depression mood. The algorithm blends these individual differences into a unique "digital phenotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, which is a psychometrically validated scale for assessing severity of symptom. The correlation was low however (Pearson r = 0,08, P-value adjusted for BH = 3.55 10 03) and varied widely between individuals.

Predictors of Symptoms

Depression is one of the world's leading causes of disability1, but it is often underdiagnosed and undertreated2. Depressive disorders are often not treated because of the stigma that surrounds them and the lack of effective treatments.

To help with personalized treatment, it is essential to identify the factors that predict symptoms. However, the current methods for predicting symptoms depend on the clinical interview which is unreliable and only detects a small variety of characteristics related to depression.2

Machine learning can be used to integrate continuous digital behavioral phenotypes captured through smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory, CAT-DI) along with other indicators of severity of symptoms can improve the accuracy of diagnosis and treatment efficacy for depression. These digital phenotypes capture a large number of distinct behaviors and activities that are difficult to document through interviews and permit continuous, high-resolution measurements.

The study enrolled University of California Los Angeles (UCLA) students experiencing moderate to severe depressive symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical depression treatments treatment according to the severity of their depression. Patients with a CAT DI score of 35 65 were assigned online support via an instructor and those with scores of 75 patients were referred to in-person clinical depression treatments care for psychotherapy.

At baseline, participants provided a series of questions about their personal demographics and psychosocial characteristics. The questions included education, age, sex and gender and financial status, marital status and whether they were divorced or not, current suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale of 100 to. The CAT DI assessment was conducted every two weeks for participants who received online support and weekly for those who received in-person assistance.

Predictors of Treatment Response

Personalized depression treatment is currently a research priority and many studies aim to identify predictors that enable clinicians to determine the most effective medications for each individual. Particularly, pharmacogenetics is able to identify genetic variations that affect the way that the body processes antidepressants. This enables doctors to choose medications that are likely to work best for each patient, while minimizing the time and effort involved in trial-and-error treatments and avoiding side effects that might otherwise hinder the progress of the patient.

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 to identify the most appropriate combination of variables that are predictive of a particular outcome, such as whether or not a particular medication is likely to improve the mood and symptoms. These models can also be used to predict a patient's response to an existing treatment which allows doctors to maximize the effectiveness of their current treatment.

A new generation of studies employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables and improve the accuracy of predictive. These models have shown to be useful in predicting treatment outcomes such as the response to antidepressants. These approaches are becoming more popular in psychiatry and could become the norm in the future non medical treatment for depression practice.

In addition to prediction models based on ML The study of the underlying mechanisms of depression is continuing. Recent findings suggest that depression is related to dysfunctions in specific neural networks. This theory suggests that individualized depression treatment will be based on targeted treatments that target these circuits in order to restore normal function.

Internet-based-based therapies can be an effective method to achieve this. They can offer a more tailored and individualized experience for patients. For example, one study found that a program on the internet was more effective than standard treatment in alleviating symptoms and ensuring a better quality of life for those suffering from MDD. In addition, a controlled randomized trial of a personalized approach to depression treatment showed an improvement in symptoms and fewer adverse effects in a large percentage of participants.

Predictors of Side Effects

A major obstacle in individualized depression treatment is predicting which antidepressant medications will have minimal or no side effects. Many patients take a trial-and-error approach, with a variety of medications prescribed before finding one that is safe and effective. Pharmacogenetics offers a new and exciting way to select antidepressant drugs that are more effective and precise.

There are several variables that can be used to determine which antidepressant should be prescribed, including genetic variations, phenotypes of the patient such as gender or ethnicity, and co-morbidities. To determine the most reliable and accurate predictors for a particular treatment, random controlled trials with larger samples will be required. This is because the detection of interaction effects or moderators may be much more difficult in trials that only consider a single episode of treatment per patient instead of multiple sessions of treatment resistant Bipolar depression over a period of time.

Additionally, the prediction of a patient's response to a particular medication will also likely need to incorporate information regarding the symptom profile and comorbidities, in addition to the patient's previous experience with tolerability and efficacy. Currently, only some easily measurable sociodemographic and clinical variables are believed to be reliably associated with the response to MDD like gender, age, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics to treatment for depression is in its beginning stages, and many challenges remain. First is a thorough understanding of the genetic mechanisms is essential as well as an understanding of what is a reliable indicator of treatment response. In addition, ethical concerns like privacy and the responsible use of personal genetic information, should be considered with care. Pharmacogenetics could, in the long run, reduce stigma surrounding mental health treatments and improve the quality of treatment. But, like all approaches to psychiatry, careful consideration and implementation is necessary. For now, the best option is to provide patients with various effective medications for depression and encourage them to talk freely with their doctors about their concerns and experiences.