Why We Love Personalized Depression Treatment (And You Should Also!)

Personalized Depression Treatment

For many people gripped by depression, traditional therapy and medication isn't effective. The individual approach to treatment could be the answer.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions designed to improve mental health. We looked at the best-fitting personal ML models for each individual, using Shapley values, in order to understand their feature predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

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

A customized depression treatment is one method of doing this. By using sensors on mobile phones and 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 more than $10 million, they will make use of these techniques to determine biological and behavioral predictors of response to antidepressant medications and psychotherapy.

The majority of research done to date has focused on sociodemographic and clinical characteristics. These include demographic variables such as age, gender and education, clinical characteristics including symptom severity and comorbidities, and biological markers such as neuroimaging and genetic variation.

Few studies have used longitudinal data to determine mood among individuals. Many studies do not consider the fact that mood can be very different between individuals. Therefore, it is critical to develop methods that allow for the determination of the individual differences in mood predictors and treatment 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. The team will then create algorithms to identify patterns of behavior and emotions that are unique to each individual.

In addition to these modalities, the team created a machine learning algorithm to model the changing variables that influence each person's mood. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

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

Predictors of symptoms

Depression is among the leading causes of disability1 yet it is often untreated and not diagnosed. In addition, a lack of effective treatments and stigmatization associated with depressive disorders stop many people from seeking help.

To aid in the development of a personalized treatment, it is essential to identify the factors that predict symptoms. Current prediction methods rely heavily on clinical interviews, which are unreliable and only identify a handful of characteristics that are associated with depression.

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes are able to capture a variety of unique behaviors and activities, which are difficult to record through interviews, and also allow for continuous, high-resolution measurements.

The study included University of California Los Angeles students who had mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which 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. Participants with a CAT-DI score of 35 65 were assigned online support via an online peer coach, whereas those who scored 75 patients were referred to in-person psychotherapy.

Participants were asked a set of questions at the beginning of the study concerning their psychosocial and demographic characteristics as well as their socioeconomic status. These included age, sex, education, work, and financial status; if they were divorced, partnered, or single; current suicidal ideation, intent, or attempts; and the frequency with the frequency they consumed alcohol. The CAT-DI was used for assessing the severity of depression-related symptoms on a scale from zero to 100. The CAT-DI test was carried out every two weeks for those who received online support and weekly for those who received in-person assistance.

Predictors of the Reaction to Treatment

A customized treatment for depression is currently a top research topic and many studies aim at identifying predictors that will help clinicians determine the most effective medication for each individual. Pharmacogenetics, for instance, uncovers genetic variations that affect how the body's metabolism reacts to drugs. This lets doctors choose the medications that will likely work best for every patient, minimizing the time and effort needed for trial-and-error treatments and eliminating any adverse negative effects.

Another option is to build prediction models that combine information from clinical studies and neural imaging data. These models can be used to determine which variables are most likely to predict a specific outcome, such as whether a medication will improve symptoms or mood. These models can be used to predict the patient's response to a treatment, which will help doctors to maximize the effectiveness of their treatment.

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

Research into the underlying causes of depression continues, as well as predictive models based on ML. Recent findings suggest that depression is linked to dysfunctions in specific neural networks. This theory suggests that a individualized treatment for depression will depend on targeted therapies that restore normal functioning to these circuits.

One method of doing this is by using internet-based programs which can offer an individualized and tailored experience for patients. A study showed that an internet-based program helped improve symptoms and improved quality life for MDD patients. A controlled, randomized study of a personalized treatment for depression revealed that a substantial percentage of patients saw improvement over time and had fewer adverse negative effects.

Predictors of Side Effects

In the treatment of depression a major challenge is predicting and identifying the antidepressant that will cause minimal or zero adverse effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and precise.

There are a variety of predictors that can be used to determine which antidepressant should be prescribed, including gene variations, phenotypes of the patient like gender or ethnicity and co-morbidities. However finding the most reliable and reliable predictors for a particular treatment will probably require controlled, randomized trials with considerably larger samples than those that are typically part of clinical trials. This is because it may be more difficult to identify interactions or moderators in trials that comprise only one episode per participant rather than multiple episodes over a period of time.

Furthermore to that, predicting a patient's reaction will likely require information on the comorbidities, symptoms profiles and the patient's personal experience of tolerability and effectiveness. At  iampsychiatry.com , only a handful of easily assessable sociodemographic variables and clinical variables seem to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

Many issues remain to be resolved in the application of pharmacogenetics to treat depression. First is a thorough understanding of the genetic mechanisms is needed and a clear definition of what constitutes a reliable predictor for treatment response. Ethics like privacy, and the responsible use genetic information must also be considered. In the long term the use of pharmacogenetics could provide an opportunity to reduce the stigma associated with mental health care and improve the treatment outcomes for patients with depression. As with all psychiatric approaches it is essential to carefully consider and implement the plan. At present, the most effective option is to offer patients a variety of effective depression medications and encourage them to speak with their physicians about their concerns and experiences.