Personalized Depression
treatment for depression and anxietyTraditional therapies and medications are not effective for a lot of patients suffering from depression. A customized treatment could be the answer.
Cue is an intervention platform for digital devices that translates passively acquired normal sensor data from smartphones into personalised micro-interventions designed to improve mental health. We examined the most effective-fitting personalized ML models for each individual using Shapley values to determine their characteristic predictors. This revealed distinct features that changed mood in a predictable manner over time.
Predictors of Mood
depression can be treated is one of the world's leading causes of mental illness.1 Yet, only half of those suffering from the condition receive treatment1. To improve outcomes, healthcare professionals must be able to recognize and treat patients with the highest chance of responding to specific treatments.
A customized depression treatment plan can aid. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most from certain treatments. They are using sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. With two grants awarded totaling over $10 million, they will make use of these tools to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.
The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include factors that affect the demographics such as age, gender and educational level, clinical characteristics like symptom severity and comorbidities, and biological markers such as neuroimaging and genetic variation.
While many of these factors can be predicted from data in medical records, very few studies have used longitudinal data to determine the causes of mood among individuals. Many studies do not take into account the fact that moods can differ significantly between individuals. Therefore, it is crucial to develop methods that 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. This enables the team to create algorithms that can identify distinct patterns of behavior and emotion that are different between people.
In addition to these modalities the team also developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm blends the individual differences to produce a unique "digital genotype" for each participant.
This digital phenotype has been linked to CAT DI scores which is a psychometrically validated symptom severity scale. The correlation was not strong, however (Pearson r = 0,08, P-value adjusted by BH 3.55 10 03) and varied significantly among individuals.
Predictors of Symptoms
Depression is the most common reason for disability across the world, but it is often untreated and misdiagnosed. Depressive disorders are often not treated because of the stigma that surrounds them and the lack of effective interventions.
To allow for individualized treatment, identifying factors that predict the severity of symptoms is crucial. Current prediction methods rely heavily on clinical interviews, which aren't reliable and only identify a handful of symptoms associated with depression.
Using machine learning to blend continuous digital behavioral phenotypes that are captured by smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory, CAT-DI) together with other predictors of symptom severity can increase the accuracy of diagnostics and treatment efficacy for depression. Digital phenotypes permit continuous, high-resolution measurements. They also capture a wide variety of unique behaviors and activity patterns that are difficult to capture through interviews.
The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care depending on the severity of their depression. Patients with a CAT DI score of 35 or 65 students were assigned online support with a coach and those with scores of 75 patients were referred for psychotherapy in-person.
Participants were asked a series questions at the beginning of the study about their demographics and psychosocial characteristics. The questions covered age, sex and education and financial status, marital status and whether they were divorced or not, their current suicidal thoughts, intentions or attempts, and how often they drank. Participants also scored their level of depression severity on a scale of 0-100 using the CAT-DI. 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 the Reaction to Treatment
Research is focused on individualized
post stroke depression treatment treatment. Many studies are aimed at identifying predictors, which will help doctors determine the most effective drugs to treat each individual. Particularly, pharmacogenetics is able to identify genetic variants that determine how the body's metabolism reacts to antidepressants. This enables doctors to choose the medications that are most likely to be most effective for each patient, minimizing the time and effort involved in trials and errors, while avoid any adverse effects that could otherwise hinder progress.
Another approach that is promising is to build models of prediction using a variety of data sources, such as the clinical information with neural imaging data. These models can be used to determine which variables are the most predictive of a specific outcome, like whether a
drug treatment for depression will improve symptoms or mood. These models can be used to determine a patient's response to an existing treatment, allowing doctors to maximize the effectiveness of the treatment currently being administered.
A new generation of machines employs machine learning techniques such as supervised and classification algorithms, regularized logistic regression and tree-based methods to integrate the effects from multiple variables and increase the accuracy of predictions. These models have been demonstrated to be useful in predicting the outcome of treatment like the response to antidepressants. These models are getting more popular in psychiatry and it is likely that they will become the standard for the future of clinical practice.
In addition to ML-based prediction models research into the mechanisms that cause depression continues. Recent findings suggest that depression is connected to dysfunctions in specific neural networks. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.
One method to achieve this is to use internet-based interventions which can offer an individualized and tailored experience for patients. For instance, one study found that a web-based program was more effective than standard treatment in alleviating symptoms and ensuring a better quality of life for patients suffering from MDD.