What You Should Be Focusing On Improving Personalized Depression Treatment
Personalized Depression Treatment
Traditional treatment and medications do not work for many people who are depressed. Personalized treatment may be the answer.
Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalised micro-interventions for improving mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to discover their predictors of feature and reveal 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 outcomes, clinicians need to be able to identify and treat patients with the highest chance of responding to specific treatments.
A customized depression treatment is one method to achieve this. Using 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 ways to determine which patients will benefit from the treatments they receive. With two grants totaling over $10 million, they will make use of these technologies to identify biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.
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 education, clinical characteristics including symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.
A few studies have utilized longitudinal data to predict mood of individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to develop methods that permit the determination of the individual differences in mood predictors and treatments 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. This allows the team to create algorithms that can systematically identify various patterns of behavior and emotion that are different between people.
In addition to these modalities the team developed a machine-learning algorithm to model the dynamic factors that determine a person's depressed mood. The algorithm integrates the individual characteristics to create a unique "digital genotype" for each participant.
This digital phenotype was linked to CAT DI scores which is a psychometrically validated symptom severity scale. 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 the most common reason for disability across the world1, but it is often untreated and misdiagnosed. Depressive disorders are often not treated because of the stigma associated with them and the absence of effective treatments.
To help with personalized treatment, it is important to identify predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only reveal a few symptoms associated with depression.
Machine learning is used to combine continuous digital behavioral phenotypes that are captured by smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory the CAT-DI) with other predictors of symptom severity could increase the accuracy of diagnostics 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 permit high-resolution, continuous measurements.
The study included University of California Los Angeles (UCLA) students experiencing mild to severe depression symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical treatment according to the severity of their depression. Those with a score on the CAT DI of 35 or 65 were given online support via the help of a coach. Those with scores of 75 were routed to in-person clinics for psychotherapy.
Participants were asked a series questions at the beginning of the study about their psychosocial and demographic characteristics as well as their socioeconomic status. The questions asked included age, sex, and education and marital status, financial status, whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, and the frequency with which they consumed alcohol. Participants also rated their degree of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI assessment was conducted every two weeks for those who received online support and weekly for those who received in-person support.
Predictors of Treatment Response
A customized treatment for depression is currently a top research topic and many studies aim at identifying predictors that will help clinicians determine the most effective drugs for each person. Particularly, pharmacogenetics is able to identify genetic variants that influence how the body metabolizes antidepressants. This allows doctors to select drugs that are likely to be most effective for each patient, reducing the time and effort involved in trials and errors, while avoid any adverse effects that could otherwise slow advancement.
Another promising method is to construct models for prediction using multiple data sources, such as data from clinical studies and neural imaging data. These models can be used to identify the variables that are most predictive of a specific outcome, such as whether a medication will improve symptoms or mood. These models can be used to predict the patient's response to a treatment, allowing doctors maximize the effectiveness.
A new generation employs machine learning techniques like the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of multiple variables to improve the accuracy of predictive. These models have been proven to be effective in the prediction of treatment outcomes like the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to become the norm in the future clinical practice.
In addition to ML-based prediction models, research into the underlying mechanisms of depression continues. Recent findings suggest that the disorder is connected with neurodegeneration in particular circuits. This suggests that an the treatment for depression will be individualized focused on therapies that target these neural circuits to restore normal functioning.
One way to do this is to use internet-based interventions that offer a more individualized and personalized experience for patients. A study showed that a web-based program improved symptoms and provided a better quality of life for MDD patients. depression therapy controlled study of 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 which antidepressant medications will cause very little or no side effects. Many patients are prescribed various medications before finding a medication that is both effective and well-tolerated. Pharmacogenetics is an exciting new avenue for a more efficient and specific method of selecting antidepressant therapies.
A variety of predictors are available to determine which antidepressant is best to prescribe, including gene variants, phenotypes of patients (e.g. gender, sex or ethnicity) and comorbidities. However finding the most reliable and valid factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials with significantly larger numbers of participants than those that are typically part of clinical trials. This is because the detection of interactions or moderators may be much 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 to that, predicting a patient's reaction will likely require information about comorbidities, symptom profiles and the patient's own experience of tolerability and effectiveness. Currently, only a few easily measurable sociodemographic variables as well as clinical variables are 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 for depression treatment. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, as well as a clear definition of an accurate indicator of the response to treatment. Ethics such as privacy and the responsible use of genetic information are also important to consider. Pharmacogenetics could be able to, over the long term help reduce stigma around mental health treatments and improve treatment outcomes. Like any other psychiatric treatment it is crucial to take your time and carefully implement the plan. For now, it is recommended to provide patients with a variety of medications for depression that are effective and encourage them to talk openly with their doctor.