Personalized Depression Treatment
Traditional therapy and medication are not effective
lithium for treatment resistant depression a lot of people who are depressed. Personalized treatment could be the solution.
Cue is a digital intervention platform that converts passively collected sensor data from smartphones into customized micro-interventions to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct features that are able to change mood as time passes.
Predictors of Mood
Depression is among the leading causes of mental illness.1 However, only half of people suffering from the condition receive treatment1. To improve outcomes, clinicians must be able to identify and treat patients who are the most likely to respond to specific treatments.
Personalized depression treatment can help. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from specific treatments. They use sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. Two grants were awarded that total more than $10 million, they will use these tools to identify the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.
The majority of research conducted to date has focused on sociodemographic and clinical characteristics. These include demographics like age, gender and education as well as clinical characteristics like severity of symptom and comorbidities, as well as biological markers.
Very few studies have used longitudinal data to predict mood in individuals. Many studies do not consider the fact that mood can differ significantly between individuals. It is therefore important to develop methods which permit the determination and quantification of the 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 will then create algorithms to recognize patterns of behaviour and emotions that are unique to each individual.
In addition to these modalities the team also developed a machine-learning algorithm that models the dynamic variables that influence each person's mood. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.
This digital phenotype was found to be associated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was low however (Pearson r = 0,08, P-value adjusted for BH = 3.55 10 03) and varied greatly between individuals.
Predictors of symptoms
Depression is among the most prevalent causes of disability1 yet it is often not properly diagnosed and treated. In addition an absence of effective treatments and stigmatization associated with depressive disorders stop many individuals from seeking help.
To aid in the development of a personalized treatment, it is essential to determine the predictors of symptoms. However, the current methods for predicting symptoms rely on clinical interview, which is unreliable and only detects a tiny variety of characteristics that are associated with depression.2
Machine learning can be used to blend continuous digital behavioral phenotypes that are captured by smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of severity of symptoms can improve the accuracy of diagnosis and the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements. They also capture a variety of distinctive behaviors and activity patterns that are difficult to capture with interviews.
The study enrolled University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical treatment depending on their depression severity. Participants with a CAT-DI score of 35 or 65 were assigned online support with an instructor and those with a score 75 patients were referred for in-person psychotherapy.
At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal demographics and psychosocial characteristics. These included sex, age and education, as well as work and financial status; whether they were divorced, partnered, or single; current suicidal thoughts, intentions or attempts; and the frequency with which they drank alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale ranging from 0-100. The CAT DI assessment was carried out every two weeks for those who received online support, and weekly for those who received in-person care.
Predictors of Treatment Response
Research is focusing on personalization of depression treatment. Many studies are focused on identifying predictors, which will aid clinicians in identifying the most effective drugs for each person. Pharmacogenetics, in particular, uncovers genetic variations that affect
how long does depression treatment last the body's metabolism reacts to drugs. This enables doctors to choose drugs that are likely to be most effective for each patient, while minimizing the time and effort involved in trial-and-error procedures and eliminating any side effects that could otherwise hinder progress.
Another option is to develop predictive models that incorporate information from clinical studies and neural imaging data. These models can then be used to determine the most appropriate combination of variables that are predictors of a specific outcome, like whether or not a medication is likely to improve symptoms and mood. These models can be used to determine the response of a patient to treatment that is already in place and help doctors maximize the effectiveness of their treatment currently being administered.
A new type of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have shown to be useful in forecasting treatment outcomes, such as the response to antidepressants. These approaches are gaining popularity in psychiatry and it is expected that they will become the standard for the future of clinical practice.
Research into depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent findings suggest that depression is connected to dysfunctions in specific neural networks. This suggests that individualized
depression treatment without drugs treatment will be focused on treatments that target these neural circuits to restore normal functioning.
One method to achieve this is by using internet-based programs which can offer an individualized and personalized experience for patients. For example, one study found that a program on the internet was more effective than standard care in improving symptoms and providing an improved quality of life for those suffering from MDD. A controlled, randomized study of a personalized treatment for
depression anxiety treatment near me treatment history (
go to these guys) found that a substantial percentage of participants experienced sustained improvement as well as fewer side effects.
Predictors of adverse effects
A major issue in personalizing depression
electric treatment for depression is predicting which antidepressant medications will have very little or no side effects. Many patients experience a trial-and-error approach, with various medications being prescribed before settling on one that is safe and effective. Pharmacogenetics offers a new and exciting method of selecting antidepressant drugs that are more effective and precise.
There are several variables that can be used to determine the antidepressant that should be prescribed, including genetic variations, patient phenotypes like gender or ethnicity, and co-morbidities. To identify the most reliable and accurate predictors for a specific treatment, random controlled trials with larger sample sizes will be required. This is because it could be more difficult to determine the effects of moderators or interactions in trials that contain only one episode per person rather than multiple episodes over time.
Additionally, predicting a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's subjective perception of the effectiveness and tolerability. At present, only a few easily measurable sociodemographic and clinical variables are believed to be correlated with response to MDD like gender, age race/ethnicity, BMI and the presence of alexithymia and the severity of depression symptoms.
The application of pharmacogenetics to treatment for depression is in its infancy and there are many hurdles to overcome. First, a clear understanding of the genetic mechanisms is required, as is a clear definition of what constitutes a reliable predictor for treatment response. Ethics such as privacy and the responsible use genetic information should also be considered. In the long run the use of pharmacogenetics could offer a chance to lessen the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. However, as with any other psychiatric treatment, careful consideration and planning is required. For now, the best method is to offer patients an array of effective depression medications and encourage them to talk freely with their doctors about their experiences and concerns.
