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Personalized Depression Treatment

For many suffering from depression, traditional therapies and medication are ineffective. The individual approach to treatment could be the answer.

Cue is an intervention platform that converts sensor data collected from smartphones into personalized micro-interventions to improve mental health. We examined the most effective-fitting personalized ML models to each subject, using Shapley values, in order to understand their features and predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

Depression is one of the most prevalent causes of mental illness.1 However, only half of people suffering from 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 certain treatments.

A customized depression treatment is one method of doing this. 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 which treatments. With two grants totaling more than $10 million, they will use these tools to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

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

While many of these aspects can be predicted from information available in medical records, few studies have employed longitudinal data to study the causes of mood among individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is essential to develop methods that allow for the determination of the 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 detect various patterns of behavior and emotions that vary between individuals.

In addition to these methods, the team created a machine learning algorithm to model the changing predictors of each person's depressed mood. The algorithm blends the individual characteristics to create a unique "digital genotype" for each participant.

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

Predictors of symptoms

Depression is a leading cause of disability around the world, but it is often misdiagnosed and untreated2. In addition, a lack of effective treatments and stigma associated with depressive disorders stop many people from seeking help.

To facilitate personalized treatment, identifying patterns that can predict symptoms is essential. The current prediction methods rely heavily on clinical interviews, which are not reliable and only identify a handful of symptoms associated with depression.

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

The study enrolled University of California Los Angeles (UCLA) students who were suffering from mild to severe depressive symptoms enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA herbal depression treatments Grand Challenge. Participants were referred to online assistance or medical care depending on the degree of their morning depression treatment. Patients who scored high on the CAT-DI scale of 35 65 were assigned online support via an online peer coach, whereas those who scored 75 patients were referred to psychotherapy in-person.

Participants were asked a series questions at the beginning of the study regarding their demographics and psychosocial characteristics. The questions included age, sex, and education and marital status, financial status, whether they were divorced or not, the frequency of suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their degree of depression symptom severity on a 0-100 scale using the CAT-DI. The CAT-DI test was performed every two weeks for participants who received online support, and weekly for those who received in-person care.

Predictors of Treatment Response

A customized treatment for depression is currently a major research area and a lot of studies are aimed to identify predictors that help clinicians determine the most effective medication for each individual. In particular, pharmacogenetics identifies genetic variations that affect the way that the body processes antidepressants. This allows doctors to select the medications that are most likely to be most effective for each patient, reducing the time and effort required in trials and errors, while eliminating any side effects that could otherwise hinder the progress of the patient.

Another promising approach is to build predictive models that incorporate information from clinical studies and neural imaging data. These models can then be used to determine the best combination of variables that is predictors of a specific outcome, such as whether or not a medication will improve symptoms and mood. These models can be used to predict the patient's response to a treatment, allowing doctors to maximize the effectiveness.

A new generation uses machine learning methods such as the supervised and classification algorithms, regularized logistic regression and tree-based methods to combine the effects of multiple variables and improve predictive accuracy. These models have been proven to be effective in predicting treatment outcomes like the response holistic ways to treat depression antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the norm for future clinical practice.

In addition to prediction models based on ML research into the underlying mechanisms of depression is continuing. Recent research suggests that the disorder is connected with neurodegeneration in particular circuits. This suggests that an the treatment for depression will be individualized focused on therapies that target these circuits to restore normal function.

Internet-based-based therapies can be an effective method to accomplish this. They can offer an individualized and tailored experience for patients. One study found that a program on the internet was more effective than standard care in reducing symptoms and ensuring an improved quality of life for those with MDD. A controlled, randomized study of a personalized treatment for depression found that a significant percentage of patients experienced sustained improvement and had fewer adverse consequences.

Predictors of Side Effects

A major issue in personalizing depression treatment is predicting the antidepressant medications that will have the least amount of side effects or none at all. Many patients are prescribed a variety of medications before settling on a treatment that is effective and tolerated. Pharmacogenetics provides a novel and exciting method to choose antidepressant medications that is more efficient and targeted.

Many predictors can be used to determine the best antidepressant to prescribe, including gene variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) 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 considerably larger samples than those that are typically part of clinical trials. This is because it may be more difficult to detect the effects of moderators or interactions in trials that comprise only a single episode per person rather than multiple episodes over time.

In addition, predicting a patient's response will likely require information on comorbidities, symptom profiles and the patient's own perception of the effectiveness and tolerability. Presently, only a handful of easily identifiable sociodemographic and clinical variables seem to be reliably associated with response to MDD like age, gender race/ethnicity, SES BMI, the presence of alexithymia, and the severity of depressive symptoms.

There are many challenges to overcome in the application of pharmacogenetics in the treatment of depression. First it is necessary to have a clear understanding of the underlying genetic mechanisms is required and a clear definition of what is a reliable predictor of treatment response. Ethics like privacy, and the ethical use of genetic information must also be considered. In the long-term pharmacogenetics can provide an opportunity to reduce the stigma associated with mental health treatment and improve treatment outcomes for those struggling with depression. As with all psychiatric approaches, it is important to give careful consideration and implement the plan. The best course of action is to offer patients an array of effective depression medication options and encourage them to speak freely with their doctors about their concerns and experiences.