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Personalized Depression Treatment

For many people gripped by depression, traditional therapy treatment for Depression and medication isn't effective. A customized treatment may be the answer.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into personalized micro-interventions that improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to identify their feature predictors and uncover distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

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

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit most from certain treatments. They are using mobile phone sensors, a voice assistant with artificial intelligence, and other digital tools. Two grants totaling more than $10 million will be used to determine biological and behavioral factors that predict response.

The majority of research done to date has focused on clinical and sociodemographic characteristics. These include factors that affect the demographics such as age, gender and educational level, clinical characteristics like symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these variables can be predicted from the information available in medical records, only a few studies have employed longitudinal data to explore predictors of mood in individuals. They have not taken into account the fact that mood can vary significantly between individuals. Therefore, it is critical to create methods that allow the recognition 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 will then create algorithms to recognize patterns of behaviour and emotions that are unique to each person.

The team also created a machine learning algorithm to model dynamic predictors for the mood of each person's depression. The algorithm blends these individual characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype has been correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was low however (Pearson r = 0,08; P-value adjusted for BH = 3.55 x 10 03) and varied significantly among individuals.

Predictors of symptoms

Depression is a leading cause of disability in the world1, but it is often not properly diagnosed and treated. Depression disorders are usually not treated because of the stigma attached to them and the absence of effective interventions.

To help with personalized treatment, it is essential to determine the predictors of symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which is unreliable and only detects a limited number of features associated with depression.2

Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements as well as capture a wide range of distinct behaviors and patterns that are difficult to record using interviews.

The study involved University of California Los Angeles students with mild to severe depression symptoms who were enrolled 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 based on the severity of their depression. Patients with a CAT DI score of 35 65 were given online support by the help of a coach. Those with a score 75 patients were referred to in-person clinics for psychotherapy.

Participants were asked a series of questions at the beginning of the study concerning their demographics and psychosocial traits. The questions asked included age, sex and education as well as financial status, marital status, whether they were divorced or not, their current suicidal ideas, intent or attempts, and the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale ranging from zero to 100. The CAT DI assessment was conducted every two weeks for participants who received online support and weekly for those who received in-person assistance.

Predictors of Treatment Response

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 medications for each person. Particularly, pharmacogenetics is able to identify genetic variants that determine how to treatment depression the body's metabolism reacts to antidepressants. This lets doctors select the medication that will likely work best for each patient, reducing the amount of time and effort required for trial-and error treatments and avoid any negative side negative effects.

Another approach that is promising is to build models for prediction using multiple data sources, including data from clinical studies and neural imaging data. These models can be used to determine which variables are the most predictive of a specific outcome, like whether a drug will improve mood or symptoms. These models can be used to determine the patient's response to a treatment, which will help doctors to maximize the effectiveness.

A new generation of machines employs machine learning techniques like algorithms for classification and supervised learning such as regularized logistic regression, and tree-based techniques to combine the effects from multiple variables and improve predictive accuracy. These models have been demonstrated to be useful in predicting homeopathic treatment for depression outcomes for example, the response to antidepressants. These approaches are gaining popularity in psychiatry, and it is likely that they will become the standard for future clinical practice.

Research into depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent research suggests that depression treatment ect is related to the dysfunctions of specific neural networks. This suggests that individual depression treatment will be focused on therapies that target these circuits to restore normal function.

One method to achieve this is through internet-delivered interventions that can provide a more personalized and customized experience for patients. One study found that a web-based program improved symptoms and led to a better quality of life for MDD patients. A controlled study that was randomized to a customized treatment for depression found that a significant percentage of patients saw improvement over time as well as fewer side 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 drugs before they find a drug that is safe and effective. Pharmacogenetics offers a new and exciting method to choose antidepressant drugs that are more effective and precise.

There are several variables that can be used to determine which antidepressant should be prescribed, such as gene variations, patient phenotypes such as ethnicity or gender, and co-morbidities. However it is difficult to determine the most reliable and accurate predictive factors for a specific treatment will probably require randomized controlled trials with considerably larger samples than those typically enrolled in clinical trials. This is because the detection of moderators or interaction effects could be more difficult in trials that only consider a single episode of treatment per participant instead of multiple sessions of treatment over a period of time.

Additionally the prediction of a patient's response will likely require information on comorbidities, symptom profiles and the patient's own perception of the effectiveness and tolerability. At present, only a handful of easily identifiable sociodemographic variables and clinical variables appear to be reliably related to response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

Many issues remain to be resolved when it comes to the use of pharmacogenetics to treat depression. First it is necessary to have a clear understanding of the genetic mechanisms is essential, as is a clear definition of what is a reliable indicator of treatment response. In addition, ethical concerns such as privacy and the appropriate use of personal genetic information, should be considered with care. In the long term pharmacogenetics can offer a chance to lessen the stigma associated with mental health treatment and to improve the outcomes of those suffering with depression. However, as with any approach to psychiatry careful consideration and application is essential. For now, the best course of action is to provide patients with an array of effective depression medication options and encourage them to speak with their physicians about their concerns and experiences.