Personalized Depression Treatment

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

Cue is an intervention platform that converts sensor data collected from smartphones into personalized micro-interventions that improve mental health. We examined the most effective-fitting personalized ML models for each individual using Shapley values to determine their features and predictors. The results revealed distinct characteristics that were deterministically changing mood over time.

Predictors of Mood

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

Personalized depression treatment centre For depression is one method to achieve this. Utilizing sensors on mobile phones as well as 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 totaling more than $10 million, they will use these techniques to determine biological and behavioral predictors of response to antidepressant medications and psychotherapy.

The majority of research conducted to the present has been focused on clinical and sociodemographic characteristics. These include demographics such as gender, age and education as well as clinical characteristics like severity of symptom and comorbidities as well as biological markers.

While many of these factors can be predicted by the information in medical records, few studies have employed longitudinal data to explore predictors of mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is essential to create methods that allow the recognition of individual differences in mood predictors and the effects of treatment.

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 is able to develop algorithms to recognize patterns of behavior and emotions that are unique to each individual.

The team also developed an algorithm for machine learning to model dynamic predictors for each person's depression mood. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.

This digital phenotype was associated with CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was tinny (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.

Predictors of symptoms

depression treatment centres is a leading reason for disability across the world1, but it is often misdiagnosed and untreated2. In addition an absence of effective interventions and stigmatization associated with depression treatment exercise disorders hinder many individuals from seeking help.

To allow for individualized treatment to improve treatment, identifying the patterns that can predict symptoms is essential. However, current prediction methods depend on the clinical interview which has poor reliability and only detects a limited variety of characteristics related to depression.2

Machine learning can increase the accuracy of diagnosis and treatment for untreatable depression by combining continuous digital behavior phenotypes gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to provide a wide range of distinct actions and behaviors that are difficult to capture through interviews, and allow for high-resolution, continuous measurements.

The study included University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment depending on their depression severity. Participants who scored a high on the CAT DI of 35 65 were assigned online support via an online peer coach, whereas those who scored 75 patients were referred for psychotherapy in-person.

Participants were asked a set of questions at the beginning of the study concerning their demographics and psychosocial characteristics. The questions asked included education, age, sex and gender as well as marital status, financial status and whether they were divorced or not, current suicidal ideas, intent or attempts, as well as how often they drank. Participants also rated their level of depression symptom severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted each other week for participants that received online support, and once a week for those receiving in-person care.

Predictors of Treatment Response

A customized treatment for depression is currently a research priority, and many studies aim at identifying predictors that allow clinicians to identify the most effective medications for each patient. Particularly, pharmacogenetics is able to identify genetic variants that determine the way that the body processes antidepressants. This enables doctors to choose drugs that are likely to be most effective for each patient, reducing the time and effort required in trials and errors, while avoid any adverse effects that could otherwise slow the progress of the patient.

Another approach that is promising is to build predictive models that incorporate information from clinical studies and neural imaging data. These models can be used to determine which variables are most predictive of a specific outcome, like whether a drug will improve mood or symptoms. These models can also be used to predict the response of a patient to treatment that is already in place and help doctors maximize the effectiveness of treatment currently being administered.

A new generation of machines employs machine learning techniques like algorithms for classification and supervised learning, regularized logistic regression and tree-based methods to integrate the effects of several variables and increase the accuracy of predictions. These models have shown to be effective in forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the norm for future clinical practice.

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

Internet-based-based therapies can be a way to accomplish this. They can provide more customized and personalized experience for patients. For instance, one study found that a web-based program was more effective than standard care in improving symptoms and providing a better quality of life for those with MDD. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed an improvement in symptoms and fewer adverse effects in a significant percentage of participants.

Predictors of Side Effects

In the treatment of depression, a major challenge is predicting and identifying which antidepressant medication will have minimal or zero adverse negative effects. Many patients are prescribed various medications before settling on a treatment that is effective and tolerated. Pharmacogenetics offers a new and exciting method of selecting antidepressant drugs to treat depression and anxiety that are more effective and specific.

There are many predictors that can be used to determine which antidepressant should be prescribed, including gene variations, patient phenotypes such as gender or ethnicity, and the presence of comorbidities. However finding the most reliable and valid factors that can predict the effectiveness of a particular treatment will probably require controlled, randomized trials with significantly larger numbers of participants than those typically enrolled in clinical trials. This is because it may be more difficult to identify the effects of moderators or interactions in trials that only include one episode per person instead of multiple episodes over time.

In addition to that, predicting a patient's reaction will likely require information on the comorbidities, symptoms profiles and the patient's subjective perception of the effectiveness and tolerability. Presently, only a handful of easily measurable sociodemographic and clinical variables appear to be correlated with the response to MDD, such as gender, age race/ethnicity, SES, BMI, the presence of alexithymia and the severity of depressive symptoms.

The application of pharmacogenetics in treatment for depression is in its early stages and there are many obstacles to overcome. First, it is essential to have a clear understanding and definition of the genetic factors that cause depression, and an accurate definition of an accurate predictor of treatment response. Additionally, ethical issues, such as privacy and the appropriate use of personal genetic information should be considered with care. In the long term the use of pharmacogenetics could offer a chance to lessen the stigma associated with mental health treatment and to improve the outcomes of those suffering with depression. As with any psychiatric approach, it is important to take your time and carefully implement the plan. At present, the most effective method is to offer patients various effective depression medication options and encourage them to speak with their physicians about their experiences and concerns.