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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapy and medications are not effective. The individual approach to treatment could be the solution.

Cue is an intervention platform that converts passively acquired sensor data from smartphones into personalized micro-interventions to improve mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to understand their feature predictors and uncover distinct characteristics that can be used to predict changes in mood as time passes.

Predictors of Mood

Depression is the leading cause of mental illness around the world.1 Yet, only half of those with the condition receive treatment. In order to improve outcomes, healthcare professionals must be able to recognize and treat patients with the highest probability of responding to particular treatments.

Personalized depression treatment can help. By using sensors for mobile phones, an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from the treatments they receive. With two grants awarded totaling more than $10 million, they will employ these tools to identify the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

So far, the majority of research into predictors of depression treatment effectiveness has been focused on the sociodemographic and clinical aspects. These include demographic variables such as age, gender and education, clinical characteristics such as symptoms severity and comorbidities and biological markers like neuroimaging and genetic variation.

Few studies have used longitudinal data to predict mood in individuals. Few also take into account the fact that mood varies significantly between individuals. It is therefore important to develop methods that permit the determination and quantification of the individual differences in mood predictors, treatment effects, 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 allows the team to create algorithms that can identify distinct patterns of behavior and emotions that are different between people.

In addition to these modalities the team also developed a machine-learning algorithm to model the changing variables that influence each person's mood. The algorithm integrates the individual differences to create an individual "digital genotype" for each participant.

This digital phenotype has been associated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was not strong, however (Pearson r = 0,08; P-value adjusted for BH = 3.55 x 10 03) and varied greatly between individuals.

Predictors of Symptoms

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

To help with personalized treatment, it is crucial to identify predictors of symptoms. However, current prediction methods are based on the clinical interview, which is not reliable and only detects a tiny number of symptoms associated with depression.2

Machine learning is used to integrate continuous digital behavioral phenotypes captured by smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of severity of symptoms can improve the accuracy of diagnosis and the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements and capture a wide variety of distinctive behaviors and activity patterns that are difficult to document with interviews.

The study involved University of California Los Angeles students with moderate to severe depression treatment without drugs symptoms who were participating 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 support or in-person clinical care according to the severity of their depression. Patients who scored high on the CAT DI of 35 or 65 were assigned online support with the help of a peer coach. those with a score of 75 were sent to in-person clinics for psychotherapy.

At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal demographics and psychosocial characteristics. The questions asked included age, sex, and education and marital status, financial status as well as whether they divorced or not, the frequency of suicidal ideas, intent or attempts, and the frequency with which they consumed alcohol. Participants also rated their degree of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI tests were conducted every week for those that received online support, and weekly for those receiving in-person support.

Predictors of Treatment Response

The development of a personalized situational depression treatment treatment is currently a major research area, and many studies aim at identifying predictors that will enable clinicians to determine the most effective medications for each patient. Particularly, pharmacogenetics can identify genetic variants that determine the way that the body processes antidepressants. This allows doctors to select drugs that are likely to work best for each patient, reducing the time and effort involved in trial-and-error treatments and avoid any adverse effects that could otherwise hinder the progress of the patient.

Another promising approach is building models of prediction using a variety of data sources, such as clinical information and neural imaging data. These models can be used to determine the variables that are most predictive of a specific outcome, such as whether a medication can help with symptoms or mood. These models can be used to determine the patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of their current treatment.

A new generation of machines employs machine learning methods such as supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of several variables to improve the accuracy of predictive. These models have been proven to be useful for forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming more popular in psychiatry, and are likely to become the norm in the future medical practice.

In addition to ML-based prediction models The study of the mechanisms that cause depression continues. Recent research suggests that depression is connected to the dysfunctions of specific neural networks. This suggests that individual depression treatment will be focused on treatments that target these circuits to restore normal function.

One method of doing this is through internet-delivered interventions which can offer an personalized and customized experience for patients. One study found that a program on the internet was more effective than standard treatment for depression and anxiety in alleviating symptoms and ensuring the best quality of life for those suffering from MDD. A controlled study that was randomized to a personalized treatment for depression revealed that a significant 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 which antidepressant medications will have no or minimal side negative effects. Many patients are prescribed a variety drugs before they find a drug that is safe and effective. Pharmacogenetics offers a fresh and exciting way to select antidepressant medications that is more effective and precise.

There are a variety of predictors that can be used to determine which antidepressant should be prescribed, such as gene variations, phenotypes of patients such as ethnicity or gender and comorbidities. However it is difficult to determine the most reliable and accurate predictors for a particular treatment is likely to require randomized controlled trials of significantly larger numbers of participants than those that are typically part of clinical trials. This is because the identifying of interactions or moderators can be a lot more difficult in trials that only focus on a single instance of treatment per participant instead of multiple sessions of treatment over a period of time.

In addition the prediction of a patient's response will likely require information about comorbidities, symptom profiles and the patient's personal perception of the effectiveness and tolerability. Presently, only a handful of easily measurable sociodemographic and clinical variables seem to be reliably associated with the response to MDD factors, including age, gender race/ethnicity, SES, BMI and the presence of alexithymia, and the severity of depression symptoms.

The application of pharmacogenetics in depression treatment is still in its infancy and there are many hurdles to overcome. First, it is important to have a clear understanding and definition of the genetic mechanisms that underlie depression, as well as an understanding of an accurate predictor of treatment response. Ethics like privacy, and the responsible use of genetic information are also important to consider. In the long-term pharmacogenetics can provide an opportunity to reduce the stigma that surrounds mental health treatment and to improve the treatment outcomes for patients with depression. However, as with all approaches to psychiatry, careful consideration and planning is necessary. For now, the best course of action is to provide patients with an array of effective depression medication options and encourage them to speak with their physicians about their experiences and concerns.