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Personalized prenatal depression treatment Treatment

Traditional therapies and medications are not effective for a lot of people suffering from depression. A customized treatment may be the solution.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into customized micro-interventions that improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to understand their predictors of feature and reveal distinct features that deterministically change mood as time passes.

Predictors of Mood

Depression is one of the leading causes of mental illness.1 Yet, only half of people suffering from the disorder receive treatment1. To improve the outcomes, doctors must be able to recognize and treat patients who have the highest chance of responding to certain treatments.

A customized depression treatment is one method to achieve this. By using mobile phone sensors as well as an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to predict which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to determine biological and behavior indicators of response.

The majority of research done to the present has been focused on sociodemographic and clinical characteristics. These include demographics like age, gender and education, as well as clinical characteristics such as symptom severity, comorbidities and biological markers.

While many of these factors can be predicted by the information in medical records, few studies have used longitudinal data to explore predictors of mood in individuals. Few studies also take into account the fact that mood can vary significantly between individuals. It is therefore important to develop methods which 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. The team is able to develop algorithms to recognize patterns of behavior and emotions that are unique to each person.

In addition to these modalities, the team developed a machine-learning algorithm that models the dynamic predictors of each person's depressed mood. The algorithm combines the individual differences to create an individual "digital genotype" for each participant.

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

Predictors of Symptoms

Depression is a leading reason for disability across the world1, however, it is often misdiagnosed and untreated2. In addition the absence of effective interventions and stigmatization associated with depression disorders hinder many individuals from seeking help.

To facilitate personalized treatment, identifying predictors of symptoms is important. However, current prediction methods rely on clinical interview, which has poor reliability and only detects a tiny number of features associated with depression.2

Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes gathered from smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can be used to capture a large number of unique behaviors and activities, which are difficult to document through interviews, and also allow for continuous and high-resolution measurements.

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 that 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. Those with a CAT-DI score of 35 65 were allocated online support with an online peer coach, whereas those with a score of 75 were routed to clinics in-person for psychotherapy.

At the beginning, participants answered the answers to a series of questions concerning their personal characteristics and psychosocial traits. The questions asked included age, sex, and education as well as marital status, financial status and whether they were divorced or not, current suicidal thoughts, intentions or attempts, and the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale from zero to 100. CAT-DI assessments were conducted each week for those that received online support, and once a week for those receiving in-person support.

Predictors of Treatment Response

Research is focused on individualized depression treatment without drugs treatment. Many studies are aimed at finding predictors, which can help clinicians identify the most effective medications to treat each individual. Pharmacogenetics, in particular, is a method of identifying genetic variations that affect how the human body metabolizes drugs. This lets doctors select the medication that will likely work best for every patient, minimizing the time and effort needed for trial-and-error treatments and avoiding any side effects.

Another promising approach is building prediction models using multiple data sources, such as data from clinical studies and neural imaging data. These models can be used to determine the best combination of variables that are predictors of a specific outcome, like whether or not a non drug treatment for depression is likely to improve mood and symptoms. These models can be used to predict the response of a patient to treatment, allowing doctors to maximize the effectiveness of their treatment.

A New treatments for depression era of research utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables and increase predictive accuracy. These models have been proven to be effective in predicting treatment outcomes like the response to antidepressants. These approaches are gaining popularity 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 research suggests that depression is linked to the malfunctions of certain neural networks. This suggests that individualized depression treatment will be focused on therapies that target these circuits to restore normal functioning.

One method to achieve this is through internet-delivered interventions which can offer an individualized and tailored experience for patients. One study discovered that a web-based treatment was more effective than standard treatment in reducing symptoms and ensuring an improved quality of life for patients suffering from MDD. A controlled study that was randomized to an individualized treatment for depression revealed that a significant percentage of patients experienced sustained improvement and fewer side negative effects.

Predictors of Side Effects

A major challenge in personalized depression treatment is predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients take a trial-and-error approach, with several medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics provides a novel and exciting method of selecting antidepressant medications that is more efficient and targeted.

Several predictors may be used to determine which antidepressant to prescribe, including genetic variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. To determine the most reliable and valid predictors of a specific treatment, random controlled trials with larger sample sizes will be required. This is because it may be more difficult to identify moderators or interactions in trials that contain only one episode per person instead of multiple episodes spread over a period of time.

Additionally, predicting a patient's response will likely require information about the severity of symptoms, comorbidities and the patient's own experience of tolerability and effectiveness. Currently, only some easily assessable sociodemographic and clinical variables appear to be correlated with the severity of MDD, such as gender, age, race/ethnicity and SES, BMI, the presence of alexithymia, and the severity of depression symptoms.

Many issues remain to be resolved when it comes to the use of pharmacogenetics to treat depression. It is crucial to have a clear understanding and definition of the genetic factors that cause 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. In the long run the use of pharmacogenetics could be a way to lessen the stigma associated with mental health care and improve the outcomes of those suffering with depression. But, like all approaches to psychiatry, careful consideration and planning is essential. For now, it is best to offer patients various depression medications that work and encourage them to talk openly with their doctor.