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

Personalized Depression Treatment

For many suffering from depression, traditional therapy and medication are ineffective. A customized treatment could be the solution.

Cue is a digital intervention platform that translates passively acquired normal smartphone sensor data into personalized micro-interventions to improve mental health. We looked at the best-fitting personal ML models to each subject, using Shapley values to discover their features and predictors. The results revealed distinct characteristics that were deterministically changing mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 Yet, only half of those suffering from the condition receive treatment1. To improve the outcomes, clinicians need to be able to identify and treat patients with the highest probability of responding to particular treatments.

The ability to tailor depression treatments is one way to do this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit the most from certain treatments. They make use of sensors for mobile phones and a voice assistant incorporating artificial intelligence, and other digital tools. Two grants totaling more than $10 million will be used to determine biological and behavioral factors that predict response.

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

While many of these aspects can be predicted by the information available in medical records, very few studies have used longitudinal data to determine the factors that influence mood in people. Many studies do not take into consideration the fact that moods can differ significantly between individuals. Therefore, it is crucial to develop methods that permit the determination and quantification of the individual differences between mood predictors treatments, mood predictors, 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 develop algorithms that can systematically identify distinct patterns of behavior and emotions that vary between individuals.

The team also created a machine-learning algorithm that can model dynamic predictors for the mood of each person's depression. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype was found to be associated with CAT-DI scores, which is a psychometrically validated severity scale for symptom severity. The correlation was low, however (Pearson r = 0,08, BH adjusted P-value 3.55 x 10 03) and varied greatly among individuals.

Predictors of symptoms

Depression is among the most prevalent causes of disability1, but it is often untreated and not diagnosed. Depressive disorders are often not treated because of the stigma that surrounds them, as well as the lack of effective treatments.

To assist in individualized treatment, depression treatment effectiveness it is important to identify the factors that predict symptoms. However, the current methods for predicting symptoms depend on the clinical interview which is not reliable and only detects a small variety of characteristics associated with depression.2

Machine learning is used to combine continuous digital behavioral phenotypes captured by smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory the CAT-DI) with other predictors of symptom severity can improve the accuracy of diagnosis and treatment efficacy for depression. These digital phenotypes provide a wide range of unique actions and behaviors that are difficult to record through interviews, and also allow for continuous and high-resolution measurements.

The study enrolled University of California Los Angeles (UCLA) students who were suffering from moderate depression treatment to severe depressive symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA depression during pregnancy treatment Grand Challenge. Participants were routed to online support or in-person clinical care according to the severity of their depression. Those with a score on the CAT-DI of 35 65 were allocated online support with an online peer coach, whereas those with a score of 75 were sent to in-person clinical care for psychotherapy.

Participants were asked a series questions at the beginning of the study concerning their demographics and psychosocial traits. These included age, sex, education, work, and financial situation; whether they were partnered, divorced, or single; current suicidal ideas, intent or attempts; as well as the frequency at that they consumed alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale of 100 to. CAT-DI assessments were conducted each other week for the participants that received online support, and once a week for those receiving in-person treatment.

Predictors of Treatment Reaction

Research is focusing on personalization of depression treatment. Many studies are aimed at identifying predictors, which will help doctors determine the most effective drugs to treat each individual. Particularly, pharmacogenetics can identify genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors select medications that are likely to be the most effective for each patient, while minimizing the time and effort needed for trial-and error treatments and avoiding any side negative effects.

Another promising approach is to create prediction models that combine information from clinical studies and neural imaging data. These models can be used to identify which variables are the most predictive of a specific outcome, such as whether a medication will improve symptoms or mood. These models can be used to predict the patient's response to a treatment, which will help doctors to maximize the effectiveness of their treatment.

A new generation uses machine learning techniques such as supervised and classification algorithms, regularized logistic regression and tree-based techniques to combine the effects from multiple variables to improve the accuracy of predictive. These models have been demonstrated to be effective in predicting treatment outcomes, such as response to antidepressants. These methods are becoming more popular in psychiatry and will likely be the norm in future medical practice.

The study of depression's underlying mechanisms continues, as do predictive models based on ML. Recent research suggests that the disorder is linked with dysfunctions in specific neural circuits. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.

Internet-based-based therapies can be an effective method to accomplish this. They can provide an individualized and tailored experience for patients. For example, one study found that a web-based program was more effective than standard treatment for depression uk in improving symptoms and providing the best quality of life for patients with MDD. In addition, a controlled randomized study of a customized approach to post natal depression treatment treatment showed steady improvement and decreased side effects in a significant number of participants.

Predictors of side effects

A major issue in personalizing depression treatment is predicting which antidepressant medications will cause the least amount of side effects or none at all. Many patients are prescribed a variety of medications before finding a medication that is effective and tolerated. Pharmacogenetics provides a novel and exciting method of selecting antidepressant medications that is more effective and specific.

There are a variety of variables that can be used to determine the antidepressant that should be prescribed, including gene variations, phenotypes of the patient such as ethnicity or gender, and the presence of comorbidities. To determine the most reliable and accurate predictors of a specific treatment, randomized controlled trials with larger sample sizes will be required. This is because it may be more difficult to identify interactions or moderators in trials that only include one episode per participant rather than multiple episodes over a period of time.

Furthermore to that, predicting a patient's reaction will likely require information about comorbidities, symptom profiles and the patient's personal perception of effectiveness and tolerability. Currently, only some easily identifiable sociodemographic and clinical variables are believed to be reliably associated with the severity of MDD like gender, age, race/ethnicity and SES BMI, depression treatment effectiveness the presence of alexithymia and the severity of depressive symptoms.

The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many obstacles to overcome. First is a thorough understanding of the underlying genetic mechanisms is required and a clear definition of what is a reliable indicator of private treatment for depression response. Additionally, ethical issues, such as privacy and the ethical use of personal genetic information must be considered carefully. In the long run the use of pharmacogenetics could provide an opportunity to reduce the stigma associated with mental health care and improve the outcomes of those suffering with depression. As with all psychiatric approaches it is crucial to carefully consider and implement the plan. For now, it is best to offer patients various depression medications that are effective and urge them to speak openly with their doctor.