You've Forgotten Personalized Depression Treatment: 10 Reasons Why You Don't Have It > 자유게시판

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Personalized Depression Treatment

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

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions for improving mental health. We analyzed the best-fitting personalized ML models to each subject, using Shapley values, in order to understand their characteristic predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

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

A customized depression treatment plan can aid. Utilizing sensors for mobile phones 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. Two grants worth more than $10 million will be used to determine the biological and behavioral indicators of response.

The majority of research on predictors for depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographic variables such as age, gender and educational level, clinical characteristics like symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

While many of these factors can be predicted from information available in medical records, very few studies have utilized longitudinal data to determine the causes of mood among individuals. Few also take into account the fact that mood varies significantly between individuals. Therefore, it is essential to create methods that allow the determination of the 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. The team can then develop algorithms to detect patterns of behavior and emotions that are unique to each person.

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

This digital phenotype has been associated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was weak, however (Pearson r = 0,08; BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals.

Predictors of symptoms

Depression is the leading reason for disability across the world1, however, it is often misdiagnosed and untreated2. In addition an absence of effective interventions and stigmatization associated with depressive disorders prevent many people from seeking help.

To assist in individualized treatment, it is important to determine the predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which aren't reliable and only reveal a few characteristics that are associated with depression.

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing pregnancy depression treatment Inventory CAT-DI). These digital phenotypes provide a wide range of distinct behaviors and activities, which are difficult to document through interviews, and allow for continuous and high-resolution measurements.

The study involved University of California Los Angeles students who had mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care based on the degree of their depression. Those with a score on the CAT-DI scale of 35 65 were allocated online support with a peer coach, while those who scored 75 were sent to in-person clinics for psychotherapy.

At baseline, participants provided an array of questions regarding their personal characteristics and psychosocial traits. The questions asked included age, sex, and education, financial status, marital status, whether they were divorced or not, their current suicidal thoughts, intent or attempts, and the frequency with which they consumed alcohol. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted every other week for the participants who received online support and every week for those who received in-person support.

Predictors of Treatment Response

Research is focusing on personalized depression treatment. Many studies are aimed at finding predictors, which can help doctors determine the most effective medications for each person. In particular, pharmacogenetics identifies genetic variants that influence the way that the body processes antidepressants. This allows doctors select medications that will likely work best for every patient, minimizing time and effort spent on trial-and error treatments and eliminating any adverse consequences.

Another approach that is promising is to build prediction models 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, like whether a medication can improve mood or symptoms. These models can be used to determine a patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of their treatment currently being administered.

A new generation of machines employs machine learning techniques such as supervised and classification algorithms, regularized logistic regression and tree-based methods to combine the effects from multiple variables to improve the accuracy of predictive. These models have been proven to be effective in the prediction of treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry, and it is expected that they will become the norm for the future of clinical practice.

In addition to prediction models based on ML, research into the underlying mechanisms of depression continues. Recent findings suggest that depression is linked to the malfunctions of certain neural networks. This theory suggests that a individualized treatment for depression treatment facility will depend on targeted therapies that restore normal functioning to these circuits.

Internet-based-based therapies can be an option to accomplish this. They can offer a more tailored and individualized experience for patients. One study found that an internet-based program improved symptoms and led to a better quality life for MDD patients. Additionally, a randomized controlled study of a personalised treatment for depression demonstrated sustained improvement and reduced side effects in a significant number of participants.

Predictors of adverse effects

A major issue in personalizing depression treatment is predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed a variety drugs before they find a drug that is effective and tolerated. Pharmacogenetics offers a new and exciting way to select antidepressant medications that is more effective and specific.

A variety of predictors are available to determine the best antidepressant to prescribe, including genetic variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. However it is difficult to determine the most reliable and accurate predictive factors for a specific treatment will probably require controlled, randomized trials with much larger samples than those normally enrolled in clinical trials. This is because it could be more difficult to detect interactions or moderators in trials that contain only a single episode per person instead of multiple episodes spread over time.

Additionally, the estimation of a patient's response to a specific medication is likely to need to incorporate information regarding symptoms and comorbidities as well as the patient's personal experience with tolerability and efficacy. At present, only a few easily measurable sociodemographic and clinical variables are believed to be reliable in predicting the response how to treat Depression and anxiety without Medication MDD, such as gender, age race/ethnicity, BMI and the presence of alexithymia, and the severity of depression symptoms.

Many issues remain to be resolved in the use of pharmacogenetics in the treatment resistant bipolar depression of depression. First, it is important to have a clear understanding and definition of the genetic mechanisms that cause treating depression, and an accurate definition of a reliable indicator of the response to treatment. Additionally, ethical issues like privacy and the ethical use of personal genetic information must be carefully considered. Pharmacogenetics can be able to, over the long term help reduce stigma around mental health treatments and improve the quality of treatment. However, as with any approach to psychiatry careful consideration and planning is essential. In the moment, it's recommended to provide patients with an array of psychotic depression treatment medications that work and encourage patients to openly talk with their physicians.