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

Personalized Depression Treatment

Traditional treatment and medications do not work for many patients suffering from depression. The individual approach to treatment could be the answer.

Cue is an intervention platform for digital devices that transforms passively acquired smartphone sensor data into personalized micro-interventions to improve mental health. We looked at the best-fitting personal ML models for each individual using Shapley values, in order to understand their features and predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is one of the world's leading causes of mental illness.1 However, only about half of those suffering from the condition receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat patients who are most likely to benefit from certain treatments.

A customized depression treatment is one method to achieve this. Utilizing mobile phone sensors and an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from the treatments they receive. Two grants worth more than $10 million will be used to discover biological and behavior predictors of response.

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

A few studies have utilized longitudinal data to determine 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 allow for the identification 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 recognize patterns of behavior and emotions that are unique to each person.

In addition to these modalities the team developed a machine-learning algorithm that models the dynamic factors that determine a person's depressed mood. The algorithm combines these individual characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype has been correlated with CAT DI scores which is a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.

Predictors of symptoms

Depression is a leading cause of disability around the world1, however, it is often not properly diagnosed and treated. In addition the absence of effective interventions and stigmatization associated with depressive disorders stop many from seeking treatment.

To facilitate personalized treatment in order to provide a more personalized treatment, identifying patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which aren't reliable and only identify a handful of features associated with depression.

Using machine learning to integrate continuous digital behavioral phenotypes of a person captured by smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing depression treatment tms Inventory, CAT-DI) together with other predictors of symptom severity can increase the accuracy of diagnostics and treatment efficacy for depression. Digital phenotypes can be used to provide a wide range of distinct behaviors and activities, which are difficult to record through interviews, and allow for high-resolution, continuous measurements.

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

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial traits. These included age, sex education, work, and financial status; whether they were divorced, married or single; the frequency of suicidal ideation, intent, or attempts; and the frequency with that they consumed alcohol. Participants also scored their level of depression severity on a scale of 0-100 using the CAT-DI. CAT-DI assessments were conducted every other week for the participants that received online support, and every week for those who received in-person care.

Predictors of Treatment Reaction

Research is focusing on personalization of depression treatment. Many studies are aimed at finding predictors, which can aid clinicians in identifying the most effective drugs to treat each patient. Pharmacogenetics, for instance, identifies genetic variations that determine how depression is treated the human body metabolizes drugs. This allows doctors to select the medications that are most likely to work best for each patient, while minimizing the time and effort involved in trial-and-error procedures and avoid any adverse effects that could otherwise slow advancement.

Another approach that is promising is to build prediction models using multiple data sources, combining the clinical information with neural imaging data. These models can be used to identify the best combination of variables that is predictors of a specific outcome, such as whether or not a medication will improve mood and symptoms. These models can be used to predict the patient's response to a treatment, allowing doctors maximize the effectiveness.

A new era of research uses machine learning methods, such as 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 shown to be effective in predicting the outcome of treatment like 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 The study of the mechanisms behind depression continues. Recent findings suggest that depression is connected to the dysfunctions of specific neural networks. This suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.

One method to achieve this is through internet-delivered interventions that can provide a more personalized and customized experience for patients. For example, one study found that a program on the internet was more effective than standard care in alleviating symptoms and ensuring an improved quality of life for people with MDD. Additionally, a randomized controlled trial of a personalized approach to treating depression showed steady improvement and decreased side effects in a significant percentage of participants.

Predictors of side effects

In the treatment of depression, one of the most difficult aspects is predicting and determining which antidepressant medication will have no or minimal adverse effects. Many patients are prescribed various drugs before they find a drug that is effective and tolerated. Pharmacogenetics provides an exciting new avenue for a more efficient and targeted approach to choosing antidepressant medications.

A variety of predictors are available to determine which antidepressant to prescribe, such as gene variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. However finding the most reliable and reliable predictive factors for a specific treatment will probably require controlled, randomized trials with much larger samples than those typically enrolled in clinical trials. This is due to the fact that it can be more difficult to detect moderators or interactions in trials that only include one episode per participant instead of multiple episodes spread over time.

Additionally, the estimation of a patient's response to a specific medication will likely also need to incorporate information regarding symptoms and comorbidities as well as the patient's prior subjective experience of its tolerability and effectiveness. 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 gender, age, race/ethnicity, BMI, SES and the presence of alexithymia.

The application of pharmacogenetics in depression treatment is still in its infancy and there are many obstacles to overcome. First it is necessary to have a clear understanding of the underlying genetic mechanisms is essential, as is a clear definition of what is a reliable predictor of treatment response. In addition, ethical concerns such as privacy and the ethical use of personal genetic information must be carefully considered. The use of pharmacogenetics may, in the long run, reduce stigma surrounding mental health treatment and improve the quality of treatment. As with any psychiatric approach it is essential to take your time and carefully implement the plan. At present, the most effective course of action is to provide patients with a variety of effective medications for depression and encourage them to talk openly with their doctors about their experiences and concerns.