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Personalized Depression Treatment

Traditional treatment and medications don't work for a majority of patients suffering from depression. The individual approach to treatment could be the answer.

Cue is a digital intervention platform that translates passively acquired normal sensor data from smartphones into customized micro-interventions that improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and reveal distinct characteristics that can be used to predict changes in mood over time.

Predictors of Mood

Depression is a leading cause of mental illness across the world.1 Yet, only half of those with the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat depression patients who are most likely to benefit from certain treatments.

The treatment of depression can be personalized to help. 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 which treatments. With two grants awarded totaling over $10 million, they will make use of these techniques to determine the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.

The majority of research to the present has been focused on clinical and sociodemographic characteristics. These include demographic factors such as age, gender and education, clinical characteristics such as symptoms severity 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 utilized longitudinal data to determine the factors that influence mood in people. Few also take into account the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods which permit the determination and quantification of the personal differences between mood predictors and treatment effects, for instance.

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 will then create algorithms to detect patterns of behavior and emotions that are unique to each person.

In addition to these methods, the team developed a machine-learning algorithm that models the dynamic variables that influence each person's mood. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype was associated with CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was not strong (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.

Predictors of Symptoms

Depression is among the leading causes of disability1, but it is often underdiagnosed and undertreated2. Depression disorders are rarely treated due to the stigma attached to them and the absence of effective interventions.

To aid in the development of a personalized treatment, it is crucial to determine the predictors of symptoms. However, the current methods for predicting symptoms rely on clinical interview, which is not reliable and only detects a small variety of characteristics associated with depression.2

Machine learning can increase the accuracy of diagnosis and treatment for depression treatment types (Telegra noted) by combining continuous, digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can be used to provide a wide range of distinct behaviors and activities, which are difficult to document through interviews and permit high-resolution, continuous measurements.

The study involved University of California Los Angeles (UCLA) students experiencing mild to severe depression symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment in accordance with their severity of depression. Those with a score on the CAT-DI scale of 35 or 65 were assigned online support via a peer coach, while those who scored 75 were routed to in-person clinical care for psychotherapy.

At the beginning, participants answered the answers to a series of questions concerning their personal demographics and psychosocial features. The questions covered age, sex and education, financial status, marital status, whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as how often they drank. 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 week for those who received online support and once a week for those receiving in-person treatment.

Predictors of the Reaction to Treatment

The development of a personalized depression treatment is currently a major research area, and many studies aim at identifying predictors that will enable clinicians to determine the most effective drugs for each person. Particularly, pharmacogenetics is able to identify genetic variations that affect how the body metabolizes antidepressants. This allows doctors to select the medications that are most likely to work best for each patient, minimizing the time and effort in trial-and-error treatments and avoid any adverse effects that could otherwise slow advancement.

Another promising approach is to create prediction models that combine the clinical data with neural imaging data. These models can be used to determine the best natural treatment for depression combination of variables predictive of a particular outcome, such as whether or not a particular medication will improve mood and symptoms. These models can be used to determine the response of a patient to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of current treatment.

A new type of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have proven to be useful for predicting treatment outcomes such as the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to become the norm in the future non medical treatment for depression practice.

In addition to prediction models based on ML research into the mechanisms that cause depression continues. Recent research suggests that depression is related to the malfunctions of certain neural networks. This theory suggests that an individualized treatment for depression will be based on targeted therapies that restore normal functioning to these circuits.

One method to achieve this is to use internet-based interventions that offer a more individualized and tailored experience for patients. For instance, one study discovered that a web-based treatment was more effective than standard care in reducing symptoms and ensuring the best quality of life for those suffering from MDD. In addition, a controlled randomized study of a customized approach to treating depression showed sustained improvement and reduced adverse effects in a large number of participants.

Predictors of side effects

In the treatment of depression the biggest challenge is predicting and identifying which antidepressant medications will have no or minimal adverse effects. Many patients are prescribed a variety of medications before finding a medication that is safe and effective. Pharmacogenetics is an exciting new avenue for a more efficient and targeted method of selecting antidepressant therapies.

A variety of predictors are available to determine which antidepressant to prescribe, such as gene variants, patient phenotypes (e.g. gender, sex or ethnicity) and co-morbidities. However finding the most reliable and reliable predictors for a particular treatment will probably require controlled, randomized trials with much larger samples than those typically enrolled in clinical trials. This is because it may be more difficult to detect interactions or moderators in trials that comprise only one episode per participant instead of multiple episodes spread over time.

Furthermore the prediction of a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's subjective experience of tolerability and effectiveness. At present, only a handful of easily identifiable sociodemographic variables and clinical variables appear to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

Many issues remain to be resolved in the application of pharmacogenetics for depression treatment. First is a thorough understanding of the underlying genetic mechanisms is essential, as is a clear definition of what is a reliable predictor of treatment response. In addition, ethical issues such as privacy and the appropriate use of personal genetic information, must be considered carefully. Pharmacogenetics could be able to, over the long term, reduce stigma surrounding mental health treatment resistant anxiety and depression and improve the quality of treatment. But, like all approaches to psychiatry, careful consideration and planning is essential. For now, it is ideal to offer patients an array of depression medications that work and encourage them to talk openly with their doctors.