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Personalized Depression Treatment

Traditional therapies and medications don't work for a majority of people suffering from depression. A customized treatment may be the solution.

Cue is an intervention platform for digital devices that translates passively acquired normal sensor data from smartphones into customized micro-interventions designed to improve mental health. We looked at the best-fitting personal ML models to each person using Shapley values, in order to understand their characteristic predictors. The results revealed distinct characteristics that were deterministically changing mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only about half of those who have the disorder receive treatment1. In order to improve outcomes, healthcare professionals must be able to identify and treat patients with the highest likelihood of responding to specific treatments.

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will gain the most from certain treatments. They make use of sensors for 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 make use of these tools to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

The majority of research to so far has focused on clinical and sociodemographic characteristics. These include demographics such as gender, age and education as well as clinical characteristics like symptom severity, comorbidities and biological markers.

While many of these factors can be predicted from information in medical records, few studies have used longitudinal data to explore the factors that influence mood in people. They have not taken into account the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that allow for the determination and quantification of the personal differences between mood predictors, treatment effects, 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 allows the team to create algorithms that can detect various patterns of behavior and emotions that vary between individuals.

In addition to these methods, the team also developed a machine-learning algorithm to model the changing variables that influence each person's mood. The algorithm integrates the individual characteristics to create an individual "digital genotype" for each participant.

The digital phenotype was associated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was weak, however (Pearson r = 0,08; P-value adjusted for BH = 3.55 x 10 03) and varied widely between individuals.

Predictors of symptoms

Depression is one of the world's leading causes of disability1 yet 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 to improve treatment, identifying the patterns that can predict symptoms is essential. However, current prediction methods rely on clinical interview, which is not reliable and only detects a limited number of features related to depression.2

Machine learning can increase the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes collected from smartphone sensors with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). 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 mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety depression treatment and extreme depression treatment (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment based on the severity of their depression. Participants who scored a high on the CAT-DI scale of 35 65 students were assigned online support by an instructor and those with a score 75 patients were referred to psychotherapy in person.

At the beginning of the interview, participants were asked a series of questions about their personal demographics and psychosocial characteristics. These included sex, age education, work, and financial situation; whether they were divorced, married, or single; current suicidal thoughts, intentions, or attempts; and the frequency with that they consumed alcohol. Participants also rated their level of depression severity on a scale ranging from 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

The development of a personalized depression treatment is currently a top research topic, and many studies aim at identifying predictors that will help clinicians determine the most effective drugs for each patient. Pharmacogenetics in particular identifies genetic variations that determine how the human body metabolizes drugs. This enables doctors to choose the medications that are most likely to be most effective for each patient, while minimizing the time and effort involved in trial-and-error procedures and avoid any adverse effects that could otherwise slow progress.

Another promising approach is to create prediction models combining information from clinical studies and neural imaging data. These models can then be used to determine the most effective combination of variables predictors of a specific outcome, like whether or not a medication will improve the mood and symptoms. These models can also be used to predict the patient's response to an existing treatment, allowing doctors to maximize the effectiveness of current treatment.

A new generation of machines employs machine learning methods such as supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of multiple variables to improve the accuracy of predictive. These models have shown to be useful for the prediction of 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 the future of clinical practice.

In addition to prediction models based on ML research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This suggests that the treatment for depression will be individualized focused on therapies that target these circuits to restore normal functioning.

One method of doing this is by using internet-based programs that offer a more individualized and tailored experience for patients. For instance, one study found that a program on the internet was more effective than standard care in reducing symptoms and ensuring a better quality of life for those suffering from MDD. A controlled, randomized study of an individualized treatment for depression revealed that a significant number of patients saw improvement over time as well as fewer side consequences.

Predictors of adverse effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will cause minimal or no side effects. Many patients are prescribed a variety drugs before they find a drug that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant medications that is more effective and specific.

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 ethnicity or gender, and comorbidities. To identify the most reliable and reliable predictors of a specific treatment, randomized controlled trials with larger samples will be required. This is because it could be more difficult to identify the effects of moderators or interactions in trials that comprise only one episode per participant rather than multiple episodes over time.

Additionally, predicting a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's personal experience of tolerability and effectiveness. Currently, only a few easily measurable sociodemographic variables as well as clinical variables appear to be reliably related to response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

The application of pharmacogenetics to treatment for depression is in its infancy, and many challenges remain. First it is necessary to have a clear understanding of the underlying genetic mechanisms is essential as well as an understanding of what treatment for depression is a reliable predictor of treatment response. Ethics like privacy, and the ethical use of genetic information should also be considered. The use of pharmacogenetics may, in the long run, reduce stigma surrounding mental health treatments and improve treatment outcomes. Like any other psychiatric treatment it is essential to take your time and carefully implement the plan. At present, the most effective option is to provide patients with various effective medications for inpatient depression treatment centers and encourage them to speak freely with their doctors about their experiences and concerns.