Personalized Depression Treatment

For a lot of people suffering from alternative depression treatment options, traditional therapies and medications are not effective. A customized treatment could be the answer.

Cue is a digital intervention platform that converts passively collected sensor data from smartphones into customized micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to understand their predictors of feature and reveal distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

Depression is one of the world's 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 certain treatments.

A customized depression treatment is one method to achieve this. By using sensors on mobile phones as well as an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from the treatments they receive. Two grants were awarded that total more than $10 million, they will make use of these tools to identify the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.

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

A few studies have utilized longitudinal data to determine mood among individuals. Many studies do not take into consideration the fact that moods vary significantly between individuals. Therefore, it is critical to develop methods that permit the recognition of individual differences in mood predictors and treatment 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 enables the team to create algorithms that can detect distinct patterns of behavior and emotion that vary between individuals.

The team also developed an algorithm for machine learning to model dynamic predictors for each person's mood for post stroke depression treatment. The algorithm integrates the individual differences to create a unique "digital genotype" for each participant.

This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is the most common reason for disability across the world1, but it is often not properly diagnosed and treated. In addition the absence of effective interventions and stigmatization associated with depressive disorders prevent many people from seeking help.

To aid in the development of a personalized treatment plan in order to provide a more personalized treatment, identifying patterns that can predict symptoms is essential. The current prediction methods rely heavily on clinical interviews, which are unreliable and only detect a few characteristics that are associated with depression.

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral patterns gathered from sensors on smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements as well as capture a variety of unique behaviors and activity patterns that are difficult to capture using interviews.

The study involved University of California Los Angeles students with mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical treatment in accordance with their severity of depression. Patients with a CAT DI score of 35 or 65 were given online support via a coach and those with a score 75 patients were referred to in-person psychotherapy.

At the beginning of the interview, participants were asked an array of questions regarding their personal demographics and psychosocial characteristics. The questions covered age, sex, and education as well as marital status, financial status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as how often they drank. Participants also rated their level of depression symptom severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted each other week for the participants who received online support and weekly for those receiving in-person care.

Predictors of the Reaction to Treatment

Research is focusing on personalized treatment for depression treatment centers near me. Many studies are aimed at finding predictors that can help clinicians identify the most effective drugs to treat each patient. Particularly, pharmacogenetics can identify genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors select medications that are most likely to work for each patient, reducing time and effort spent on trial-and-error treatments and eliminating any adverse effects.

Another promising approach is building models for prediction using multiple data sources, combining data from clinical studies and neural imaging data. These models can then be used to determine the best combination of variables predictive of a particular outcome, like whether or not a particular medication will improve mood and symptoms. These models can also be used to predict the patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of their current treatment.

A new generation of machines employs machine learning methods such as supervised and classification algorithms, regularized logistic regression and tree-based techniques to combine the effects of multiple variables and increase the accuracy of predictions. These models have been shown to be effective in predicting outcomes of treatment like the response to antidepressants. These approaches are gaining popularity in psychiatry and it is likely that they will become the standard for the future of clinical practice.

Research into the underlying causes of depression continues, as do ML-based predictive models. Recent research suggests that the disorder is associated with neural dysfunctions that affect specific circuits. This suggests that individual depression treatment will be based on targeted treatments that target these circuits in order to restore normal functioning.

Internet-based-based therapies can be an option to achieve this. They can offer more customized and personalized experience for patients. One study found that a web-based program was more effective than standard care in improving symptoms and providing a better quality of life for those with MDD. A controlled, randomized study of an individualized treatment for depression treatment for elderly revealed that a significant number of participants experienced sustained improvement and had fewer adverse negative effects.

Predictors of side effects

A major challenge in personalized depression treatment free treatment involves identifying and predicting the antidepressant medications that will have minimal or no side effects. Many patients take a trial-and-error approach, using a variety of medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant drugs that are more effective and specific.

Many predictors can be used to determine the best antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and comorbidities. However it is difficult to determine the most reliable and accurate factors that can predict the effectiveness of a particular treatment is likely to require controlled, randomized trials with significantly larger numbers of participants than those normally enrolled in clinical trials. This is because the detection of moderators or interaction effects can be a lot more difficult in trials that take into account a single episode of treatment per patient instead of multiple episodes of treatment over a period of time.

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

There are many challenges to overcome in the application of pharmacogenetics to treat depression. First, a clear understanding of the underlying genetic mechanisms is essential as well as an understanding of what is a reliable predictor of treatment response. In addition, ethical issues, such as privacy and the appropriate use of personal genetic information, should be considered with care. In the long-term the use of pharmacogenetics could be a way to lessen the stigma associated with mental health care and improve the treatment outcomes for patients with depression. But, like all approaches to psychiatry, careful consideration and application is necessary. In the moment, it's best to offer patients a variety of medications for depression that work and encourage them to speak openly with their physicians.