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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapy and medication are ineffective. The individual approach to treatment could be the answer.

Cue is a digital intervention platform that converts passively collected sensor data from smartphones into personalised micro-interventions to improve mental health. We analyzed the best-fitting personalized ML models to each subject using Shapley values to determine their feature predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 Yet, only half of those who have the condition receive treatment1. To improve the outcomes, clinicians need to be able to identify and treat patients with the highest likelihood of responding to particular treatments.

Personalized depression treatment is one method of doing this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from certain treatments. They make use of mobile phone sensors and a voice assistant incorporating artificial intelligence as well as other digital tools. Two grants worth more than $10 million will be used to determine the biological and behavioral predictors of response.

The majority of research done to the present has been focused on sociodemographic and clinical characteristics. These include demographic variables such as age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological markers like neuroimaging and genetic variation.

While many of these aspects can be predicted from data in medical records, only a few studies have employed longitudinal data to study the causes of mood among individuals. Few studies also take into consideration the fact that mood can vary significantly between individuals. Therefore, it is essential to create methods that allow 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 develop algorithms that can systematically identify different patterns of behavior and emotions that vary between individuals.

The team also devised a machine learning algorithm to model dynamic predictors for the mood of each person's depression. The algorithm blends these individual differences into a unique "digital phenotype" for each participant.

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

Predictors of symptoms

Depression is the most common cause of disability around the world1, however, it is often untreated and misdiagnosed. In addition an absence of effective treatments and stigmatization associated with depressive disorders prevent many from seeking psychological treatment for depression.

To help with personalized 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 not reliable and only detects a small variety of characteristics that are associated with depression.2

Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral patterns gathered from sensors on smartphones with a validated mental health tracker online (the Computerized Adaptive Testing postpartum depression treatment [www.idsys.kr`s blog] Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements. They also capture a wide range of distinctive behaviors and activity patterns that are difficult to capture using interviews.

The study included University of California Los Angeles (UCLA) students experiencing mild to severe depressive symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment depending on the severity of their depression. Participants with a CAT-DI score of 35 or 65 were given online support with an instructor and those with a score 75 were routed to in-person clinics for psychotherapy.

Participants were asked a set of questions at the beginning of the study concerning their demographics and psychosocial traits. The questions asked included education, age, sex and gender and financial status, marital status as well as whether they divorced or not, current suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their level of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI tests were conducted every other week for the participants that received online support, and every week ect for treatment resistant depression those who received in-person support.

Predictors of Treatment Response

A customized electric treatment for depression for depression is currently a research priority, and many studies aim at identifying predictors that will enable clinicians to determine the most effective medication for each patient. Particularly, pharmacogenetics is able to identify genetic variants that influence the way that the body processes antidepressants. This lets doctors choose the medications that are likely to be the most effective for every patient, minimizing the amount of time and effort required for trials and errors, while eliminating any adverse effects.

Another option is to create prediction models combining information from clinical studies and neural imaging data. These models can be used to identify the most effective combination of variables that is predictive of a particular outcome, like whether or not a medication will improve the mood and symptoms. These models can be used to predict the response of a patient to a treatment, allowing doctors to maximize the effectiveness.

A new era of research utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and improve the accuracy of predictive. These models have been proven to be useful in predicting outcomes of treatment like the response to antidepressants. These methods are becoming more popular in psychiatry, and are likely to become the standard of future clinical practice.

Research into the underlying causes of depression continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is associated with neurodegeneration in particular circuits. This theory suggests that the treatment for depression will be individualized built around targeted therapies that target these circuits in order to restore normal functioning.

One way to do this is through internet-delivered interventions which can offer an individualized and tailored experience for patients. For example, one study discovered that a web-based treatment was more effective than standard care in reducing symptoms and ensuring an improved quality of life for patients suffering from MDD. Additionally, a randomized controlled trial of a personalized treatment for depression demonstrated steady improvement and decreased side effects in a significant number of participants.

Predictors of Side Effects

In the treatment of depression, the biggest challenge is predicting and determining which antidepressant medication will have no or minimal adverse negative effects. Many patients take a trial-and-error approach, with several medications prescribed before finding one that is safe and effective. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant drugs that are more effective and precise.

There are many variables that can be used to determine the antidepressant that should be prescribed, including gene variations, patient phenotypes such as gender or ethnicity, and the presence of comorbidities. However, identifying the most reliable and accurate factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with considerably larger samples than those normally enrolled in clinical trials. This is due to the fact that the identification of interactions or moderators could be more difficult in trials that only focus on a single instance of treatment per person, rather than multiple episodes of treatment over time.

Furthermore, predicting a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's subjective perception of the effectiveness and tolerability. Currently, only a few easily measurable sociodemographic variables as well as clinical variables are reliably related to response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics in depression treatment is still in its early stages and there are many hurdles to overcome. First, a clear understanding of the underlying genetic mechanisms is required and an understanding of what is a reliable predictor of treatment response. Ethics, such as privacy, and the responsible use genetic information are also important to consider. Pharmacogenetics could, in the long run, reduce stigma surrounding treatments for mental illness and improve the outcomes of treatment. However, as with all approaches to psychiatry, careful consideration and application is essential. For now, it is recommended to provide patients with an array of depression medications that are effective and urge patients to openly talk with their doctors.