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Personalized Depression Treatment

Traditional treatment and medications don't work for a majority of 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 personalised micro-interventions that improve mental health. We analyzed the best way to treat depression-fitting personalized ML models for each individual, using Shapley values, in order to understand their feature 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 Yet, only half of those who have the condition receive treatment1. To improve outcomes, clinicians must be able to identify and treat patients who are the most likely to respond to certain treatments.

Personalized depression treatment is one way to do this. Utilizing sensors on 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 predict which patients will benefit from which treatments. With two grants awarded totaling over $10 million, they will use these technologies to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.

To date, the majority of research on predictors for depression treatment effectiveness has centered on sociodemographic and clinical characteristics. These include demographic factors such as age, sex and educational level, clinical characteristics like symptom severity and comorbidities, and biological markers like neuroimaging and genetic variation.

Very few studies have used longitudinal data in order to predict mood in individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is critical to develop methods that permit the determination of 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. This allows the team to create algorithms that can systematically identify distinct patterns of behavior and emotion that are different between people.

The team also created an algorithm for machine learning to identify dynamic predictors of the mood of each person's depression. The algorithm integrates the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype has been associated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely across individuals.

Predictors of symptoms

Depression is the most common cause of disability around the world, but it is often misdiagnosed and untreated2. Depressive disorders are often not treated due to the stigma that surrounds them and the absence of effective interventions.

To aid in the development of a personalized treatment, it is important to identify the factors that predict symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which aren't reliable and only detect a few symptoms associated with depression and alcohol treatment.

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

The study comprised University of California Los Angeles students who had mild to Severe depression treatment 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 directed to online support or clinical care depending on the severity of their depression. Participants who scored a high on the CAT-DI of 35 or 65 were assigned online support with a peer coach, while those who scored 75 were routed to clinics in-person for psychotherapy.

At the beginning of the interview, participants were asked an array of questions regarding their personal demographics and psychosocial features. These included age, sex education, work, and financial status; whether they were partnered, divorced, or single; current suicidal thoughts, intentions, or attempts; and the frequency at that they consumed alcohol. Participants also rated their level of depression severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted every week for those who received online support and once a week for those receiving in-person treatment.

Predictors of the Reaction to Treatment

Research is focusing on personalized depression treatment. Many studies are focused on finding predictors that can aid clinicians in identifying the most effective drugs to treat each individual. Pharmacogenetics, for instance, identifies genetic variations that determine how the human body metabolizes drugs. This allows doctors select medications that are most likely to work for each patient, while minimizing time and effort spent on trials and errors, while eliminating any adverse effects.

Another promising approach is building models of prediction using a variety of data sources, such as the clinical information with neural imaging data. These models can be used to determine the best combination of variables that is predictive of a particular outcome, like whether or not a medication is likely to improve the mood and symptoms. These models can also be used to predict the response of a patient to a treatment they are currently receiving which allows doctors to maximize the effectiveness of the current treatment.

A new era of research uses machine learning methods like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables and increase predictive accuracy. These models have been proven to be useful in the prediction of treatment outcomes like the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the standard of future medical practice.

Research into depression's underlying mechanisms continues, as do predictive models based on ML. Recent research suggests that the disorder is connected with dysfunctions in specific neural circuits. This theory suggests that a individualized treatment for depression will be based upon targeted treatments that restore normal function to these circuits.

One way to do this is by using internet-based programs that can provide a more individualized and tailored 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 patients with MDD. A controlled study that was randomized to a customized treatment for depression found that a significant number of participants experienced sustained improvement and fewer side effects.

Predictors of side effects

A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients take a trial-and-error approach, with a variety of medications prescribed before finding one that is effective and tolerable. 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 which antidepressant to prescribe, such as gene variations, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. To identify the most reliable and valid predictors of a specific treatment, random controlled trials with larger numbers of participants will be required. This is due to the fact that it can be more difficult to identify interactions or moderators in trials that contain only one episode per participant instead of multiple episodes over a long period of time.

Furthermore the prediction of a patient's response will likely require information about the comorbidities, symptoms profiles and the patient's own experience of tolerability and effectiveness. Currently, only a few easily identifiable sociodemographic variables and clinical variables are reliably related to response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

The application of pharmacogenetics in depression treatment is still in its early stages, and many challenges remain. First, it is essential to have a clear understanding and definition of the genetic mechanisms that cause depression, and an accurate definition of an accurate indicator of the response to treatment. Ethics, such as privacy, and the ethical use of genetic information are also important to consider. In the long run the use of pharmacogenetics could offer a chance to lessen the stigma that surrounds mental health care and improve the treatment outcomes for patients with atypical depression treatment. As with all psychiatric approaches it is essential to take your time and carefully implement the plan. In the moment, it's ideal to offer patients an array of depression medications that work and encourage patients to openly talk with their doctor.