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Personalized Depression Treatment

For many people gripped by depression, traditional therapy and medications are not effective. A customized treatment could be the solution.

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

Predictors of Mood

Depression is a major cause of mental illness in the world.1 Yet the majority of people with the condition receive natural treatment depression anxiety. To improve the outcomes, doctors must be able identify and treat patients who are most likely to respond to certain treatments.

Personalized depression treatment is one method to achieve this. Utilizing 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 determine which patients will benefit from which treatments. Two grants worth more than $10 million will be used to discover biological and behavior predictors of response.

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

While many of these factors can be predicted by the information available in medical records, only a few studies have employed longitudinal data to study the causes of mood among individuals. Few studies also consider the fact that mood can be very different between individuals. Therefore, it is critical to develop methods that allow for the identification of 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 can then develop algorithms to detect patterns of behaviour and emotions that are unique to each person.

In addition to these modalities the team developed 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.

This digital phenotype has been associated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely across individuals.

Predictors of symptoms

Depression is a leading cause of disability around the world1, however, it is often untreated and misdiagnosed. In addition, a lack of effective interventions and stigma associated with depression disorders hinder many from seeking treatment.

To aid in the development of a personalized treatment, it is important to identify predictors of symptoms. However, the methods used to predict symptoms are based on the clinical interview, which is unreliable and only detects a tiny number of symptoms that are associated with depression.2

Machine learning can increase the accuracy of diagnosis and treatment for moderate depression treatment by combining continuous digital behavior phenotypes collected from smartphone sensors with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes provide a wide range of distinct behaviors and activities that are difficult to record through interviews, and allow for continuous, high-resolution measurements.

The study comprised University of California Los Angeles students with mild to severe depression 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 directed to online support or in-person clinical treatment depending on their depression severity. Those with a score on the CAT-DI scale of 35 or 65 were assigned online support via an online peer coach, whereas those with a score of 75 were routed to in-person clinical care for psychotherapy.

Participants were asked a series questions at the beginning of the study concerning their psychosocial and demographic characteristics as well as their socioeconomic status. These included sex, age and education, as well as work and financial status; whether they were divorced, partnered, or single; current suicidal thoughts, intentions, or attempts; and the frequency at which they drank alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale from 0-100. CAT-DI assessments were conducted every other week for participants who received online support and once a week for those receiving in-person support.

Predictors of Treatment Response

Research is focusing on personalization of treatment for depression. Many studies are focused on finding predictors, which can help doctors determine the most effective medications to treat each individual. Pharmacogenetics in particular identifies genetic variations that determine how the body's metabolism reacts to drugs. This allows doctors to select drugs that are likely to work best for each patient, reducing the time and effort required in trials and errors, while avoiding side effects that might otherwise hinder advancement.

Another promising approach is building models of prediction using a variety of data sources, such as data from clinical studies and neural imaging data. These models can be used to identify which variables are most likely to predict a specific outcome, such as whether a medication will improve mood or symptoms. These models can be used to determine the patient's response to a treatment, which will help doctors maximize the effectiveness.

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

The study of depression treatment resistant's underlying mechanisms continues, in addition to ML-based predictive models. Recent findings suggest that depression is linked to the dysfunctions of specific neural networks. This suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.

Internet-based interventions are an effective method to accomplish this. They can offer an individualized and tailored experience for patients. For instance, one study found that a web-based program was more effective than standard treatment in improving symptoms and providing a better quality of life for those with MDD. Additionally, a randomized controlled trial of a personalized treatment for depression demonstrated steady improvement and decreased adverse effects in a large proportion of participants.

Predictors of side effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will cause minimal or no side effects. Many patients are prescribed a variety of drugs before they find a drug that is safe and effective. Pharmacogenetics offers a fascinating new method for an efficient and targeted method of selecting antidepressant therapies.

Many predictors can be used to determine which antidepressant is best to prescribe, such as gene variants, phenotypes of patients (e.g. gender, sex or ethnicity) and comorbidities. However finding the most reliable and valid predictive factors for a specific treatment is likely to require randomized controlled trials with considerably larger samples than those that are typically part of clinical trials. This is because it could be more difficult to identify moderators or interactions in trials that contain only one episode per participant rather than multiple episodes over a period of time.

In addition the prediction of a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's own perception of effectiveness and tolerability. At present, only a handful of easily identifiable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

Many issues remain to be resolved in the application of pharmacogenetics in the treatment of depression. It is crucial to have a clear understanding and definition of the genetic mechanisms that underlie depression, as well as an understanding of a reliable indicator of the response to treatment. In addition, ethical concerns such as privacy and the appropriate use of personal genetic information must be carefully considered. Pharmacogenetics can be able to, over the long term reduce stigma associated with mental health treatment and improve treatment outcomes. However, as with any approach to psychiatry careful consideration and implementation is required. In the moment, it's ideal to offer patients an array of herbal depression treatments medications that are effective and encourage them to speak openly with their physicians.