Personalized depression treatment centre Treatment

Traditional treatment and medications don't work for a majority of patients suffering from depression. Personalized treatment could be the answer.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into personalised micro-interventions for improving mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and reveal distinct features that deterministically change mood as time passes.

Predictors of Mood

Depression is a leading cause of mental illness in the world.1 Yet, only half of those with the condition receive treatment. To improve outcomes, healthcare professionals must be able identify and treat patients most likely to respond to specific 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 developing new methods to predict which patients will benefit from the treatments they receive. With two grants awarded totaling over $10 million, they will make use of these technologies to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

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

Very few studies have used longitudinal data to predict mood of individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to devise methods that permit the identification and quantification of 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 enables the team to develop algorithms that can detect various patterns of behavior and emotions that are different between people.

The team also devised a machine learning algorithm to create dynamic predictors for each person's depression mood. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.

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

Predictors of Symptoms

Depression is among the leading causes of disability1 but is often not properly diagnosed and treated. Depression disorders are rarely treated due to the stigma attached to them, as well as the lack of effective interventions.

To assist in individualized treatment, it is important to determine the predictors of symptoms. The current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few symptoms associated with depression.

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes gathered from smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can provide continuous, high-resolution measurements and capture a variety of distinctive behaviors and activity patterns that are difficult to document through interviews.

The study comprised University of California Los Angeles students with mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or medical care according to the severity of their depression. Patients with a CAT DI score of 35 or 65 were assigned online support via an online peer coach, whereas those who scored 75 patients were referred for in-person psychotherapy.

Participants were asked a series questions at the beginning of the study about their psychosocial and demographic characteristics as well as their socioeconomic status. The questions covered age, sex and education, financial status, marital status and whether they were divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. The CAT-DI test was performed every two weeks for participants who received online support and weekly lithium for treatment resistant depression those who received in-person assistance.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic and many studies aim at identifying predictors that will allow clinicians to identify the most effective drugs for each individual. In particular, pharmacogenetics identifies genetic variants that influence how the body's metabolism reacts to antidepressants. This enables doctors to choose the medications that are most likely to work best antidepressant for treatment resistant depression for each patient, while minimizing the time and effort involved in trial-and-error treatments and avoid any adverse effects that could otherwise slow the progress of the patient.

Another promising approach is to build prediction models combining the clinical data with neural imaging data. These models can be used to determine the variables that are most predictive of a particular outcome, such as whether a drug will help with symptoms or mood. These models can be used to predict the patient's response to a treatment, allowing doctors to maximize the effectiveness of their treatment.

A new generation of machines employs machine learning methods such as algorithms for classification and supervised learning such as 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 forecasting treatment outcomes, such as the response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to be the norm in future clinical practice.

The study of depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is connected with neurodegeneration in particular circuits. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.

One way to do this is to use internet-based interventions that offer a more individualized and tailored 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 an improved quality of life for those suffering from MDD. A randomized controlled study of a personalized treatment for depression revealed that a substantial percentage of patients experienced sustained improvement as well as fewer side negative effects.

Predictors of side effects

In the treatment of depression the biggest challenge is predicting and identifying the antidepressant that will cause minimal or zero side effects. Many patients experience a trial-and-error method, involving several medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics provides an exciting new way to take an efficient and targeted approach to selecting antidepressant treatments.

Several predictors may be used to determine the best antidepressant to prescribe, including gene variants, patient phenotypes (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. To identify the most reliable and accurate predictors for a particular treatment, controlled trials that are randomized with larger numbers of participants will be required. This is because it could be more difficult to determine interactions or moderators in trials that comprise only one episode per person instead of multiple episodes over a period of time.

Furthermore the prediction of a patient's response will likely require information about comorbidities, symptom profiles and the patient's own experience of tolerability and effectiveness. Currently, only some easily assessable sociodemographic and clinical variables are believed to be correlated with the severity of MDD factors, including gender, age, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depressive symptoms.

Many challenges remain when it comes to the use of pharmacogenetics in the treatment of depression (coachformat8.bravejournal.net). First, a clear understanding of the genetic mechanisms is needed and an understanding of what treatment for depression constitutes a reliable predictor for treatment response. In addition, ethical concerns such as privacy and the appropriate use of personal genetic information must be considered carefully. In the long-term, pharmacogenetics may be a way to lessen the stigma associated with mental health care and improve treatment outcomes for those struggling with depression. However, as with all approaches to psychiatry, careful consideration and application is required. At present, the most effective method is to provide patients with an array of effective depression medications and encourage them to talk with their physicians about their experiences and concerns.