From Around The Web The 20 Most Amazing Infographics About Personalized Depression Treatment > 자유게시판

Personalized Depression Treatment

Traditional therapies and medications do not work for many patients suffering from depression. A customized treatment may be the solution.

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 feature predictors and uncover distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

Depression is the leading cause of mental illness around the world.1 Yet the majority of people with the condition receive treatment. In order to improve outcomes, healthcare professionals must be able to identify and treat patients with the highest likelihood of responding to particular treatments.

The treatment of depression can be personalized to help. Utilizing mobile phone sensors and an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from the treatments they receive. With two grants totaling more than $10 million, they will use these technologies to identify biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.

The majority of research conducted to the present has been focused on clinical and sociodemographic characteristics. These include factors that affect the demographics such as age, sex and educational level, clinical characteristics like symptoms severity and comorbidities and biological indicators such as neuroimaging and genetic variation.

A few studies have utilized longitudinal data to predict mood in individuals. Few also take into account the fact that mood varies significantly between individuals. Therefore, it is important to develop methods which allow for the analysis and measurement of personal 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 enables the team to create algorithms meds that treat depression and anxiety can identify various patterns of behavior and emotions that differ between individuals.

In addition to these methods, the team also developed a machine-learning algorithm to model the dynamic predictors of each person's depressed mood. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was not strong (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely among individuals.

Predictors of symptoms

depression treatment ect is the most common cause of disability in the world, but it is often misdiagnosed and untreated2. In addition, a lack of effective interventions and stigmatization associated with depressive disorders prevent many from seeking treatment.

To allow for individualized treatment, identifying factors that predict the severity of symptoms is crucial. However, the current methods for predicting symptoms are based on the clinical interview, which is not reliable and only detects a small number of features that are associated with depression.2

Using machine learning to combine continuous digital behavioral phenotypes that are captured through smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) together with other predictors of severity of symptoms can improve diagnostic accuracy and increase the effectiveness of treatment resistant depression treatment for depression. Digital phenotypes permit continuous, high-resolution measurements as well as capture a variety of distinctive behaviors and activity patterns that are difficult to record with interviews.

The study included University of California Los Angeles (UCLA) students who were suffering from moderate 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 sent online for assistance or medical care depending on the severity of their depression. Patients who scored high on the CAT-DI scale of 35 or 65 were given online support via the help of a coach. Those with scores of 75 patients were referred to in-person clinics for psychotherapy.

Participants were asked a series of questions at the beginning of the study concerning their demographics and psychosocial characteristics. These included age, sex, education, work, and financial status; whether they were partnered, divorced or single; their current suicidal thoughts, intentions or attempts; as well as the frequency at the frequency they consumed alcohol. Participants also scored their level of depression treatment without medicines symptom severity on a scale of 0-100 using the CAT-DI. The CAT-DI assessment was performed every two weeks for participants who received online support and weekly for those who received in-person care.

Predictors of the Reaction to Treatment

Research is focusing on personalization of treatment for depression. Many studies are focused on finding predictors that can help clinicians identify the most effective medications to treat each patient. Particularly, pharmacogenetics is able to identify genetic variations that affect how the body metabolizes antidepressants. This allows doctors select medications that are most likely to work for each patient, reducing time and effort spent on trial-and error treatments and avoid any negative side negative effects.

Another promising method is to construct prediction models using multiple data sources, including clinical information and neural imaging data. These models can be used to determine the most appropriate combination of variables that are predictive of a particular outcome, such as whether or not a drug is likely to improve mood and symptoms. These models can also be used to predict the response of a patient to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of their current alternative treatment for depression and anxiety.

A new type of research employs machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been demonstrated to be effective in predicting the outcome of treatment like the response to antidepressants. These methods are becoming more popular in psychiatry and could become the norm in the future medical practice.

Research into the underlying causes of depression continues, as well as ML-based predictive models. Recent research suggests that depression is connected to dysfunctions in specific neural networks. This theory suggests that individual depression treatment will be focused on treatments that target these circuits to restore normal function.

One method of doing this is through internet-delivered interventions that offer a more individualized and tailored experience for patients. For instance, one study found that a web-based program was more effective than standard treatment in reducing symptoms and ensuring the best quality of life for those with MDD. Additionally, a randomized controlled study of a customized treatment for depression demonstrated steady improvement and decreased adverse effects in a significant percentage of participants.

Predictors of side effects

A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will cause minimal or no side effects. Many patients are prescribed a variety drugs before they find a drug that is effective and tolerated. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant medicines that are more efficient and targeted.

There are a variety of predictors that can be used to determine the antidepressant to be prescribed, including genetic variations, phenotypes of patients such as ethnicity or gender and comorbidities. However finding the most reliable and accurate factors that can predict the effectiveness of a particular treatment will probably require controlled, randomized trials with much larger samples than those that are typically part of clinical trials. This is because the identifying of moderators or interaction effects could be more difficult in trials that only take into account a single episode of treatment per participant, rather than multiple episodes of treatment over a period of time.

Additionally, predicting a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's own perception of effectiveness and tolerability. Currently, only a few easily measurable sociodemographic variables as well as clinical variables appear to be reliably related to response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

There are many challenges to overcome in the application of pharmacogenetics in the treatment of depression. First is a thorough understanding of the underlying genetic mechanisms is required as well as an understanding of what is a reliable indicator of ect treatment for depression response. Additionally, ethical issues such as privacy and the ethical use of personal genetic information, must be carefully considered. The use of pharmacogenetics may, in the long run help reduce stigma around mental health treatment and improve the outcomes of treatment. As with any psychiatric approach it is essential to carefully consider and implement the plan. For now, the best method is to offer patients various effective medications for depression and encourage them to speak with their physicians about their experiences and concerns.