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Personalized Depression Treatment

Traditional treatment and medications do not work for many patients suffering from depression. Personalized treatment could be the solution.

Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalised micro-interventions that improve mental health. We examined the most effective-fitting personalized ML models to each person, using Shapley values to determine their characteristic predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is a major cause of mental illness across the world.1 Yet only half of those with the condition receive treatment. To improve the outcomes, healthcare professionals must be able to identify and treat patients who have the highest probability of responding to certain treatments.

A customized depression treatment plan can aid. 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. Two grants were awarded that total over $10 million, they will employ these tools to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

So far, the majority of research on factors that predict depression treatment effectiveness has focused on the sociodemographic and clinical aspects. These include demographic variables such as age, sex and education, clinical characteristics such as symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

Few studies have used longitudinal data to predict mood of individuals. Few studies also take into consideration the fact that mood can be very different between individuals. Therefore, it is crucial to develop methods which permit 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 enables the team to develop algorithms that can systematically identify different patterns of behavior and emotions that vary between individuals.

The team also developed a machine-learning algorithm that can identify dynamic predictors of each person's depression mood. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. However the correlation was not strong (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of Symptoms

Depression is the leading cause of disability in the world1, but it is often untreated and misdiagnosed. In addition the absence of effective treatments for depression interventions and stigmatization associated with depression disorders hinder many people from seeking help.

To allow for individualized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. However, the methods used to predict symptoms depend on the clinical interview which is unreliable and only detects a tiny number of features associated with depression.2

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior patterns gathered from sensors on smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can provide continuous, high-resolution measurements. They also capture a wide range of distinctive behaviors and activity patterns that are difficult to document through interviews.

The study enrolled University of California Los Angeles (UCLA) students experiencing moderate to severe depressive symptoms. enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment depending on the severity of their depression. Participants with a CAT-DI score of 35 65 were given online support via a coach and those with a score 75 were sent to in-person clinics for psychotherapy.

At the beginning of the interview, participants were asked a series of questions about their personal demographics and psychosocial characteristics. These included age, sex education, work, and financial status; if they were divorced, married, or single; current suicidal ideation, intent, or attempts; and the frequency with which they drank alcohol. Participants also scored their level of residential depression treatment uk symptom severity on a scale of 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 care.

Predictors of Treatment Reaction

Personalized inpatient depression treatment centers treatment is currently a research priority and many studies aim at identifying predictors that allow clinicians to identify the most effective medication for each patient. Pharmacogenetics, for instance, is a method of identifying genetic variations that affect the way that our bodies process drugs. This lets doctors select the medication that are most likely to work for every patient, minimizing the amount of time and effort required for trial-and-error treatments and avoiding any side effects.

Another option is to develop prediction models that combine clinical data and neural imaging data. These models can be used to determine the variables that are most predictive of a specific outcome, like whether a drug will improve symptoms or mood. These models can be used to predict the response of a patient to a treatment, which will help doctors to maximize the effectiveness.

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

Research into the underlying causes of depression continues, in addition to predictive models based on ML. Recent research suggests that the disorder is linked with dysfunctions in specific neural circuits. This theory suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.

One method of doing this is to use internet-based interventions which can offer an individualized and tailored experience for patients. For example, one study discovered that a web-based treatment was more effective than standard care in reducing symptoms and ensuring a better quality of life for people with MDD. A controlled, randomized study of an individualized treatment for depression found that a substantial percentage of participants experienced sustained improvement as well as fewer side negative effects.

Predictors of adverse effects

A major issue in personalizing depression treatment involves identifying and predicting the antidepressant medications that will have minimal or no side effects. Many patients are prescribed various medications before settling on a treatment that is safe and effective. Pharmacogenetics is an exciting new way to take an efficient and specific approach to selecting antidepressant treatments.

There are many variables that can be used to determine the antidepressant to be prescribed, such as gene variations, phenotypes of the patient such as gender or ethnicity, and the presence of comorbidities. However finding the most reliable and valid factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with significantly larger numbers of participants than those that are typically part of clinical trials. This is because it could be more difficult to determine moderators or interactions in trials that comprise only one episode per person instead of multiple episodes over time.

Additionally, the estimation of a patient's response to a particular medication is likely to require information about the symptom profile and comorbidities, as well as the patient's prior subjective experience of its tolerability and effectiveness. There are currently only a few easily assessable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

There are many challenges to overcome in the use of pharmacogenetics in the treatment of depression. First, a clear understanding of the genetic mechanisms is essential as well as a clear definition of what constitutes a reliable predictor for treatment response. In addition, ethical issues like privacy and the ethical use of personal genetic information must be considered carefully. The use of pharmacogenetics may eventually, reduce stigma surrounding treatments for mental illness and improve treatment outcomes. However, as with any other psychiatric treatment, careful consideration and planning is essential. In the moment, it's recommended to provide patients with a variety of medications for depression that are effective and urge patients to openly talk with their doctors.