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Personalized Depression Treatment

Traditional ect treatment for depression and medications don't work for a majority of people who are depressed. A customized treatment could be the solution.

Cue is an intervention platform for digital devices that transforms passively acquired sensor data from smartphones into customized micro-interventions designed to improve mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and reveal distinct features that are able to change 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 outcomes, healthcare professionals must be able to recognize and treat patients who are the most likely to benefit from certain treatments.

Personalized moderate depression treatment treatment is one method to achieve this. Utilizing sensors for mobile phones and an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new natural ways to treat depression and anxiety to determine which patients will benefit from which treatments. Two grants worth more than $10 million will be used to identify the biological and behavioral indicators of response.

The majority of research conducted to the present has been focused on clinical and sociodemographic characteristics. These include factors that affect the demographics like age, sex and education, clinical characteristics such as symptoms severity and comorbidities and biological indicators such as neuroimaging and genetic variation.

While many of these aspects can be predicted from the information in medical records, only a few studies have used longitudinal data to determine predictors of mood in individuals. Few studies also take into consideration the fact that mood can vary significantly between individuals. It is therefore important to develop methods that allow for the determination and quantification of the individual 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 can then develop algorithms to detect patterns of behaviour and emotions that are unique to each individual.

In addition to these modalities, the team developed a machine-learning algorithm to model the dynamic variables that influence each person's mood. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. However the correlation was not strong (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely across individuals.

Predictors of symptoms

Depression is one of the world's leading causes of disability1, but it is often underdiagnosed and undertreated2. In addition the absence of effective interventions and stigma associated with depressive disorders prevent many from seeking treatment.

To facilitate personalized treatment in order to provide a more personalized treatment, identifying factors that predict the severity of symptoms is crucial. However, the current methods for predicting symptoms depend on the clinical interview which is unreliable and only detects a limited number of features associated with depression.2

Using machine learning to integrate continuous digital behavioral phenotypes of a person captured by sensors on smartphones and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of symptom severity can improve the accuracy of diagnosis and treatment efficacy for depression. These digital phenotypes capture a large number of unique behaviors and activities, which are difficult to document through interviews, and also allow for high-resolution, continuous measurements.

The study enrolled University of California Los Angeles (UCLA) students experiencing mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment depending on the severity of their depression. Participants with a CAT-DI score of 35 or 65 were assigned to online support via the help of a peer coach. those who scored 75 patients were referred for in-person psychotherapy.

At baseline, participants provided the answers to a series of questions concerning their personal characteristics and psychosocial traits. The questions included education, age, sex and gender and marital status, financial status as well as whether they divorced or not, the frequency of suicidal thoughts, intent or attempts, and how often they drank. Participants also scored their level of depression severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI tests were conducted every other week for participants who received online support and weekly for those receiving in-person treatment.

Predictors of Treatment Response

A customized natural Treatment Depression anxiety for depression is currently a research priority and a lot of studies are aimed at identifying predictors that will allow clinicians to identify the most effective drugs for each person. Pharmacogenetics, in particular, uncovers genetic variations that affect how the human body metabolizes drugs. This allows doctors select medications that are likely to be the most effective for every patient, minimizing time and effort spent on trial-and-error treatments and eliminating any adverse effects.

Another approach that is promising is to build models for prediction using multiple data sources, including clinical information and neural imaging data. These models can be used to determine which variables are most likely to predict a specific outcome, such as whether a medication will help with symptoms or mood. These models can also be used to predict the patient's response to an existing treatment, allowing doctors to maximize the effectiveness of the treatment currently being administered.

A new era of research utilizes machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to blend the effects of several variables and improve the accuracy of predictive. These models have been shown to be useful in predicting treatment outcomes like the response to antidepressants. These methods are becoming more popular in psychiatry and will likely become the norm in the future treatment.

Research into the underlying causes of depression continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is linked with neurodegeneration in particular circuits. This suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.

One way to do this is to use internet-based interventions that can provide a more individualized and tailored experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality life for MDD patients. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed sustained improvement and reduced side effects in a significant proportion of participants.

Predictors of Side Effects

In the treatment of depression, a major challenge is predicting and identifying which antidepressant medication will have very little or no adverse effects. Many patients take a trial-and-error approach, using various medications being prescribed before settling on one that is safe and effective. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant drugs that are more effective and specific.

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

In addition the prediction of a patient's response will likely require information about the severity of symptoms, comorbidities and the patient's own perception of the effectiveness and tolerability. Currently, only a few easily measurable sociodemographic variables as well as clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics to treatment for depression is in its infancy and there are many obstacles to overcome. First, a clear understanding of the underlying genetic mechanisms is required as well as a clear definition of what constitutes a reliable predictor for treatment response. Ethics, such as privacy, and the responsible use of genetic information must also be considered. In the long run, pharmacogenetics may offer a chance to lessen the stigma that surrounds mental health care and improve the treatment outcomes for patients with depression. As with any psychiatric approach, it is important to give careful consideration and implement the plan. At present, the most effective option is to provide patients with an array of effective medications for depression and encourage them to talk freely with their doctors about their experiences and concerns.