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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medications are not effective. A customized best natural treatment for anxiety and depression may be the answer.

Cue is an intervention platform for digital devices that converts passively collected smartphone sensor data into personalized micro-interventions designed to improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct features that deterministically change mood with time.

Predictors of Mood

Depression is a major cause of mental illness around the world.1 Yet the majority of people with the condition receive treatment. To improve the outcomes, doctors must be able to recognize and treat patients with the highest chance of responding to certain treatments.

Personalized 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 working on new ways to determine which patients will benefit from the treatments they receive. With two grants totaling over $10 million, they will employ these techniques to determine the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

The majority of research into predictors of depression treatment effectiveness has focused on clinical and sociodemographic characteristics. These include demographic factors like age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.

A few studies have utilized longitudinal data in order to predict mood of individuals. Many studies do not consider the fact that moods can be very different between individuals. Therefore, it is important to develop methods that allow for the analysis and measurement of individual differences between mood predictors and treatment effects, for instance.

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 develop algorithms that can detect various patterns of behavior and emotion that are different between people.

The team also developed an algorithm for machine learning to create dynamic predictors for each person's depression mood. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

The digital phenotype was associated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was not strong (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.

Predictors of Symptoms

Depression is the leading cause of disability in the world, but it is often untreated and misdiagnosed. Depression disorders are usually not treated because of the stigma that surrounds them, as well as the lack of effective treatments.

To facilitate personalized treatment to improve treatment, identifying the patterns that can predict symptoms is essential. The current prediction methods rely heavily on clinical interviews, which aren't reliable and only reveal a few symptoms associated with depression.

Machine learning is used to blend continuous digital behavioral phenotypes captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of severity of symptoms has the potential to improve the accuracy of diagnosis and the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements and capture a wide variety of distinctive behaviors and activity patterns that are difficult to document using interviews.

The study enrolled University of California Los Angeles (UCLA) students with moderate to severe depressive symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment according to the degree of their depression. Patients with a CAT DI score of 35 65 were allocated online support with a peer coach, while those who scored 75 patients were referred to psychotherapy in person.

At the beginning, participants answered a series of questions about their personal demographics and psychosocial features. These included age, sex and education, as well as work and financial status; whether they were divorced, partnered or single; their current suicidal ideation, intent or attempts; and the frequency at which they drank alcohol. Participants also rated their degree of depression severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI assessment was conducted every two weeks for participants who received online support, and weekly for those who received in-person support.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic, and many studies aim at identifying predictors that will help clinicians determine the most effective medications for each individual. Pharmacogenetics, in particular, uncovers genetic variations that affect how to treat depression and anxiety the body's metabolism reacts to drugs. This allows doctors to select medications that are likely to be most effective for each patient, reducing the time and effort required in trial-and-error procedures and eliminating any side effects that could otherwise hinder the progress of the patient.

Another promising method is to construct models of prediction using a variety of data sources, combining clinical information and neural imaging data. These models can be used to determine the most appropriate combination of variables predictive of a particular outcome, such as whether or not a particular medication is likely to improve the mood and symptoms. These models can also be used to predict a patient's response to an existing treatment and help doctors maximize the effectiveness of the treatment currently being administered.

A new generation employs machine learning methods such as the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects from multiple variables to improve the accuracy of predictive. These models have shown to be useful for predicting treatment outcomes such as the response to antidepressants. These models are getting more popular in psychiatry and it is expected that they will become the norm for future clinical practice.

Research into depression's underlying mechanisms continues, as well as ML-based predictive models. Recent research suggests that depression is linked to the dysfunctions of specific neural networks. This suggests that an individual depression treatment will be built around targeted treatments that target these circuits to restore normal function.

Internet-based interventions are an option to accomplish this. They can offer a more tailored and individualized experience for patients. A study showed that a web-based program improved symptoms and provided a better quality of life for MDD patients. Additionally, a randomized controlled study of a personalised approach to treating depression showed an improvement in symptoms and fewer adverse effects in a significant proportion of participants.

Predictors of adverse effects

In the treatment of depression a major challenge is predicting and identifying the antidepressant that will cause very little or no adverse negative effects. Many patients are prescribed various drugs before they find a drug that is effective and tolerated. Pharmacogenetics provides a novel and exciting method to choose antidepressant medicines that are more effective and specific.

There are a variety of predictors that can be used to determine which antidepressant should be prescribed, such as gene variations, patient phenotypes like gender or ethnicity and co-morbidities. However finding the most reliable and valid 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 due to the fact that it can be more difficult to identify the effects of moderators or interactions in trials that comprise only one episode per person rather than multiple episodes over a period of time.

Additionally the prediction of a patient's reaction to a particular medication is likely to need to incorporate information regarding comorbidities and symptom profiles, as well as the patient's prior subjective experience with tolerability and efficacy. 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, BMI, SES and the presence of alexithymia.

Many challenges remain in the use of pharmacogenetics in the treatment of depression. First, a clear understanding of the genetic mechanisms is needed and an understanding of what treatments are available for depression is a reliable predictor of treatment response. Additionally, ethical issues, such as privacy and the appropriate use of personal genetic information, must be considered carefully. In the long run the use of pharmacogenetics could be a way to lessen the stigma associated with mental health treatment and improve the outcomes of those suffering with depression. As with any psychiatric approach it is crucial to give careful consideration and implement the plan. In the moment, it's ideal to offer patients a variety of medications for depression that work and encourage patients to openly talk with their physicians.