Personalized Depression Treatment

Traditional therapies and medications do not work for many people suffering from depression. Personalized treatment could be the answer.

Cue is an intervention platform that transforms sensor data collected from smartphones into customized micro-interventions for improving mental health. We looked at the best-fitting personal ML models to each subject, using Shapley values to determine their features and predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

Depression is the leading cause of mental illness across the world.1 Yet the majority of people with the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat patients who are the most likely to respond to specific treatments.

A customized depression treatment is one method to achieve this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit the most from certain treatments. They use sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. With two grants totaling over $10 million, they will use these tools to identify the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

To date, the majority of research into predictors of depression treatment effectiveness has focused on the sociodemographic and clinical aspects. These include factors that affect the demographics like age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these variables can be predicted from information available in medical records, few studies have used longitudinal data to study the causes of mood among individuals. Many studies do not take into consideration the fact that mood varies significantly between individuals. Therefore, it is important to develop methods that allow for the identification and quantification of personal differences between mood predictors treatments, mood predictors, etc.

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 enables the team to create algorithms that can identify different patterns of behavior and emotions that vary between individuals.

The team also devised a machine learning algorithm to model dynamic predictors for each person's depression mood. The algorithm integrates the individual differences to create a unique "digital genotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, which is a psychometrically validated scale for assessing severity of symptom. The correlation was weak however (Pearson r = 0,08; BH adjusted P-value 3.55 10 03) and varied greatly between individuals.

Predictors of symptoms

Depression is one of the leading causes of disability1, but it is often underdiagnosed and undertreated2. In addition an absence of effective interventions and stigma associated with depression disorders hinder many individuals from seeking help.

To aid in the development of a personalized treatment plan to improve treatment, identifying the patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which aren't reliable and only reveal a few characteristics that are associated with depression.

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements as well as capture a wide range of unique behaviors and activity patterns that are difficult to record through interviews.

The study comprised University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care depending on the degree of their depression. Participants with a CAT-DI score of 35 or 65 were given online support via a coach and those with scores of 75 patients were referred to in-person psychotherapy.

Participants were asked a series of questions at the beginning of the study concerning their psychosocial and demographic characteristics as well as their socioeconomic status. The questions asked included age, sex, and education and marital status, financial status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, and how often they drank. The CAT-DI was used to assess the severity of depression symptoms on a scale of zero to 100. The CAT-DI test was conducted every two weeks for those who received online support, and weekly for those who received in-person support.

Predictors of treatment depression Response

A customized treatment for depression is currently a top research topic and a lot of studies are aimed to identify predictors that enable clinicians to determine the most effective drugs for each person. In particular, pharmacogenetics identifies genetic variants that influence how the body metabolizes antidepressants. This lets doctors select the medication that will likely work best for each patient, reducing time and effort spent on trials and errors, while avoiding any side consequences.

Another approach that is promising is to build prediction models combining the clinical data with neural imaging data. These models can be used to identify which variables are most predictive of a particular outcome, such as whether a drug will improve symptoms or mood. These models can be used to predict the patient's response to a treatment, which will help doctors maximize the effectiveness.

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

In addition to ML-based prediction models research into the underlying mechanisms of depression continues. Recent findings suggest that the disorder is associated with dysfunctions in specific neural circuits. This suggests that the treatment for dementia depression treatment will be individualized built around targeted treatments that target these circuits to restore normal functioning.

Internet-based-based therapies can be an option to achieve this. They can offer an individualized and tailored experience for patients. One study found that a web-based program improved symptoms and improved quality life for MDD patients. Additionally, a randomized controlled study of a customized treatment for depression (click for source) demonstrated sustained improvement and reduced adverse effects in a significant percentage of participants.

Predictors of side effects

In the treatment of depression one of the most difficult aspects is predicting and determining which antidepressant medications will have minimal or zero adverse effects. Many patients experience a trial-and-error method, involving various medications prescribed until they find one that is effective and tolerable. Pharmacogenetics is an exciting new way to take an effective and precise approach to choosing antidepressant medications.

There are several predictors that can be used to determine which antidepressant should be prescribed, including genetic variations, patient phenotypes such as gender or ethnicity, and co-morbidities. However finding the most reliable and accurate predictive factors for a specific treatment will probably require controlled, randomized trials with much larger samples than those normally enrolled in clinical trials. This is because it could be more difficult to determine moderators or interactions in trials that comprise only one episode per person instead of multiple episodes spread over time.

Furthermore the estimation of a patient's response to a specific medication is likely to require information on comorbidities and symptom profiles, as well as the patient's prior subjective experience with tolerability and efficacy. Currently, only some easily assessable sociodemographic and clinical variables are believed to be reliable in predicting the severity of MDD, such as age, gender, race/ethnicity and SES BMI and the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics to treatment for atypical depression treatment is in its early stages, and many challenges remain. It is crucial to have a clear understanding and definition of the genetic mechanisms that underlie depression, as well as an accurate definition of an accurate predictor of treatment response. Ethics such as privacy and the responsible use genetic information are also important to consider. Pharmacogenetics could be able to, over the long term help reduce stigma around treatments for mental illness and improve the quality of treatment. As with all psychiatric approaches, it is important to give careful consideration and implement the plan. At present, it's ideal to offer patients various depression medications that work and encourage them to talk openly with their doctor.