20 Irrefutable Myths About Personalized Depression Treatment: Busted

Personalized Depression Treatment

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

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into customized micro-interventions to improve mental health. We looked at the best-fitting personal ML models for each individual using Shapley values to discover 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, only half of those suffering from the condition receive treatment. To improve the outcomes, doctors must be able to recognize and treat patients who are most likely to respond to certain treatments.

Personalized depression treatment is one way to do this.  depression treatment for women I Am Psychiatry  at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most from specific treatments. They make use of sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence and other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavior predictors of response.

The majority of research on factors that predict depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographics like 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 in order to determine mood among individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to develop methods that allow for the identification of the 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 detect patterns of behavior and emotions that are unique to each person.

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

This digital phenotype was correlated with CAT DI scores which is a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is a leading cause of disability in the world1, however, it is often untreated and misdiagnosed. Depression disorders are usually not treated because of the stigma attached to them and the lack of effective treatments.

To facilitate personalized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. Current prediction methods rely heavily on clinical interviews, which are not reliable and only identify a handful of features associated with depression.

Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes collected from smartphone sensors with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes capture a large number of unique behaviors and activities that are difficult to record through interviews, and also allow for continuous, high-resolution measurements.

The study involved University of California Los Angeles students with moderate to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment depending on the degree of their depression. Participants with a CAT-DI score of 35 65 were allocated online support with a peer coach, while those with a score of 75 patients were referred to clinics in-person for psychotherapy.

At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal characteristics and psychosocial traits. These included sex, age and education, as well as work and financial situation; whether they were divorced, partnered, or single; current suicidal thoughts, intentions, or attempts; and the frequency at the frequency they consumed alcohol. Participants also rated their level of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI assessment was carried out every two weeks for those who received online support and weekly for those who received in-person support.

Predictors of Treatment Response

A customized treatment for depression is currently a major research area and many studies aim at identifying predictors that will help clinicians determine the most effective drugs for each patient. Particularly, pharmacogenetics is able to identify genetic variants that determine the way that the body processes antidepressants. This allows doctors to select medications that are likely to be most effective for each patient, while minimizing the time and effort in trials and errors, while eliminating any side effects that could otherwise slow the progress of the patient.

Another promising method is to construct models for prediction using multiple data sources, including clinical information 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 symptoms or mood. These models can be used to determine the patient's response to an existing treatment which allows doctors to maximize the effectiveness of the current treatment.

A new era of research utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables and increase predictive accuracy. These models have been shown to be useful in predicting treatment outcomes, such as response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could become the standard of future medical practice.

The study of depression's underlying mechanisms continues, as well as predictive models based on ML. Recent findings suggest that the disorder is associated with neural dysfunctions that affect specific circuits. This theory suggests that an individualized treatment for depression will depend on targeted therapies that restore normal functioning to these circuits.

One way to do this is through internet-delivered interventions that offer a more individualized and tailored experience for patients. One study found that an internet-based program improved symptoms and provided a better quality life for MDD patients. Additionally, a randomized controlled study of a personalised treatment for depression demonstrated an improvement in symptoms and fewer adverse effects in a significant number of participants.

Predictors of side effects

A major challenge in personalized depression treatment is predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics offers a fascinating new method for an efficient and targeted method of selecting antidepressant therapies.

A variety of predictors are available to determine which antidepressant to prescribe, including gene variations, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. However finding the most reliable and reliable predictors for a particular treatment will probably require randomized controlled trials with considerably larger samples than those typically enrolled in clinical trials. This is because the identifying of interaction effects or moderators could be more difficult in trials that focus on a single instance of treatment per patient instead of multiple sessions of treatment over time.

Additionally the estimation of a patient's response to a particular medication will also likely require information about comorbidities and symptom profiles, as well as the patient's previous experience of its tolerability and effectiveness. Currently, only a few easily identifiable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

Many challenges remain when it comes to the use of pharmacogenetics for depression treatment. It is crucial to be able to comprehend and understand the definition of the genetic factors that cause depression, as well as an accurate definition of a reliable predictor of treatment response. Ethics like privacy, and the responsible use of genetic information should also be considered. In the long run, pharmacogenetics may be a way to lessen the stigma that surrounds mental health care and improve treatment outcomes for those struggling with depression. However, as with any other psychiatric treatment, careful consideration and application is necessary. For now, it is recommended to provide patients with an array of depression medications that are effective and encourage patients to openly talk with their physicians.