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Personalized Depression Treatment

Traditional treatment and medications are not effective for a lot of patients suffering from depression. Personalized treatment may be the answer.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into personalized micro-interventions that improve 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 with time.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 Yet, only half of those suffering from the disorder receive treatment1. In order to improve outcomes, doctors must be able to recognize and treat patients who have the highest likelihood of responding to certain treatments.

A customized depression treatment is one method of doing this. By using mobile phone sensors, 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. Two grants were awarded that total over $10 million, they will employ these technologies to identify the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

The majority of research into predictors of depression therapy treatment for depression Effectiveness (nerdgaming.Science) has focused on the sociodemographic and clinical aspects. These include demographic variables such as age, sex and educational level, clinical characteristics like symptoms severity and comorbidities and biological markers like neuroimaging and genetic variation.

Few studies have used longitudinal data to predict mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is crucial to create methods that allow the determination of different mood predictors for each person 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. This allows the team to create algorithms that can identify different patterns of behavior and emotions that vary between individuals.

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

This digital phenotype was correlated with CAT DI scores which is a psychometrically validated symptom severity scale. However the correlation was not strong (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely across individuals.

Predictors of symptoms

Depression is a leading cause of disability around the world1, however, it is often not properly diagnosed and treated. In addition an absence of effective interventions and stigma associated with depressive disorders prevent many from seeking treatment.

To assist in individualized treatment, it is crucial to identify the factors that predict symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which is unreliable and only detects a tiny number of features that are associated with depression.2

Machine learning is used to integrate continuous digital behavioral phenotypes that are captured through smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) along with other indicators of symptom severity can improve diagnostic accuracy and increase treatment efficacy for depression. Digital phenotypes are able to are able to capture a variety of distinct behaviors and activities, which are difficult to document through interviews, and also allow for continuous and high-resolution measurements.

The study included University of California Los Angeles (UCLA) students experiencing moderate to severe depressive symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care depending on the severity of their depression. Those with a CAT-DI score of 35 65 were assigned online support via a coach and those with a score 75 were sent to clinics in-person for psychotherapy.

At baseline, participants provided a series of questions about their personal demographics and psychosocial features. These included sex, age and education, as well as work and financial status; if they were divorced, married, or single; current suicidal ideas, intent, or attempts; and the frequency with that they consumed alcohol. Participants also rated their level of depression symptom severity on a scale of 0-100 using the CAT-DI. CAT-DI assessments were conducted each week for those who received online support and weekly for those receiving in-person treatment.

Predictors of Treatment Reaction

The development of a personalized depression treatment is currently a research priority and many studies aim at identifying predictors that will allow clinicians to identify the most effective medication for each patient. In particular, pharmacogenetics identifies genetic variations that affect the way that the body processes antidepressants. This lets doctors choose the medications that are likely to be the most effective for each patient, reducing time and effort spent on trial-and-error treatments and eliminating any adverse consequences.

Another option is to develop predictive models that incorporate the clinical data with neural imaging data. These models can be used to identify the most appropriate combination of variables predictors of a specific outcome, such as whether or not a drug is likely to improve the mood and symptoms. These models can be used to predict the patient's response to a treatment, allowing doctors maximize the effectiveness.

A new generation of machines employs machine learning techniques such as algorithms for classification and supervised learning such as regularized logistic regression, and tree-based methods to integrate the effects from multiple variables to improve the accuracy of predictive. These models have been proven to be effective in predicting the outcome of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry and could become the standard of future clinical practice.

Research into depression's underlying mechanisms continues, as well as ML-based predictive models. Recent research suggests that the disorder is linked with dysfunctions in specific neural circuits. This suggests that an individualized depression treatment will be based on targeted therapies that target these neural circuits to restore normal function.

Internet-delivered interventions can be an option to accomplish this. They can offer more customized and personalized experience for patients. One study found that an internet-based program helped improve symptoms and improved quality of life for MDD patients. A randomized controlled study of a customized treatment for depression showed that a significant percentage of participants experienced sustained improvement and fewer side negative effects.

Predictors of Side Effects

A major issue in personalizing depression treatment is predicting which antidepressant medications will have very little or no side effects. Many patients are prescribed various medications before settling on a treatment that is safe and effective. Pharmacogenetics offers a fascinating new avenue for a more efficient and specific approach to choosing antidepressant medications.

A variety of predictors are available to determine the best antidepressant to prescribe, including gene variants, phenotypes of patients (e.g. gender, sex or ethnicity) and the presence of comorbidities. To identify the most reliable and valid predictors for a specific treatment, controlled trials that are randomized with larger samples will be required. This is due to the fact that the identification of interaction effects or moderators can be a lot more difficult in trials that consider a single episode of treatment per patient instead of multiple sessions of treatment over a period of time.

Additionally the prediction of a patient's reaction to a specific medication will also likely need to incorporate information regarding the symptom profile and comorbidities, and the patient's personal experience with tolerability and efficacy. Presently, only a handful of easily assessable sociodemographic and clinical variables seem to be correlated with response to MDD, such as age, gender, race/ethnicity and SES, BMI, the presence of alexithymia, and the severity of depressive symptoms.

The application of pharmacogenetics to depression treatment is still in its early stages and there are many hurdles to overcome. First is a thorough understanding of the genetic mechanisms is needed, as is an understanding of what is a reliable predictor of treatment response. Ethics like privacy, and the responsible use genetic information must also be considered. Pharmacogenetics could, in the long run reduce stigma associated with treatments for mental illness and improve holistic treatment for depression outcomes. Like any other psychiatric treatment, it is important to carefully consider and implement the plan. At present, it's best antidepressant for treatment resistant depression to offer patients various depression medications that are effective and encourage patients to openly talk with their doctors.