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Personalized Depression Treatment

Traditional therapies and medications do not work for many people suffering from depression. A customized treatment may be the answer.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions for improving mental health. We analyzed the best-fitting personalized ML models to each subject, using Shapley values to discover their feature predictors. This revealed distinct features that were deterministically changing mood over time.

Predictors of Mood

Depression is a leading cause of mental illness in the world.1 Yet, only half of those suffering from the condition receive treatment. To improve the outcomes, doctors must be able identify and treat patients most likely to respond to specific treatments.

Personalized depression treatment can help. Utilizing sensors on mobile phones and an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are developing new methods to predict which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to identify biological and behavioral predictors of response.

So far, the majority of research on predictors for depression treatment effectiveness has been focused on the sociodemographic and clinical aspects. These include demographics such as age, gender and education, as well as clinical aspects like symptom severity and comorbidities, as well as biological markers.

A few studies have utilized longitudinal data in order to predict mood of individuals. Many studies do not take into account the fact that mood can differ significantly between individuals. Therefore, it is essential to develop methods that permit the identification of different mood predictors for each person and treatments 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. The team can then develop algorithms to detect patterns of behavior and emotions that are unique to each person.

The team also devised a machine-learning algorithm that can identify dynamic predictors of the mood of each person's depression. The algorithm integrates the individual characteristics to create an individual "digital genotype" for each participant.

This digital phenotype was associated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was not strong however (Pearson r = 0,08; P-value adjusted by BH 3.55 x 10 03) and varied significantly between individuals.

Predictors of symptoms

Depression is among the leading causes of disability1, but it is often not properly diagnosed and treated. In addition an absence of effective treatments and stigmatization associated with depressive disorders prevent many from seeking treatment.

To facilitate personalized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. Current prediction methods rely heavily on clinical interviews, which are not reliable and only detect a few features associated with depression.

Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous digital behavior patterns gathered from sensors on smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes capture a large number of distinct behaviors and activities, which are difficult to capture through interviews and permit continuous, high-resolution measurements.

The study comprised University of California Los Angeles students with moderate to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment depending on the severity of their depression. Participants who scored a high on the CAT DI of 35 65 students were assigned online support with a coach and those with scores of 75 were routed to clinics in-person for psychotherapy.

At baseline, participants provided an array of questions regarding their personal demographics and psychosocial features. These included age, sex education, work, and financial status; whether they were divorced, partnered, or single; current suicidal ideation, intent or attempts; as well as the frequency at that they consumed alcohol. Participants also scored their level of Depression treatment without drugs severity on a scale of 0-100 using the CAT-DI. The CAT-DI tests were conducted every week for those that received online support, and once a week for those receiving in-person support.

Predictors of the Reaction to Treatment

The development of a personalized depression treatment is currently a research priority and a lot of studies are aimed at identifying predictors that allow clinicians to identify the most effective medications for each person. Pharmacogenetics, for instance, identifies genetic variations that determine how to treat anxiety and depression without medication the human body metabolizes drugs. This enables doctors to choose medications that are likely medicine to treat anxiety and depression be most effective for each patient, while minimizing the time and effort required in trial-and-error treatments and avoiding side effects that might otherwise slow advancement.

Another promising approach is building models of prediction using a variety of data sources, such as the clinical information with neural imaging data. These models can then be used to identify the most appropriate combination of variables predictive of a particular outcome, like whether or not a particular medication will improve symptoms and mood. These models can also be used to predict the patient's response to treatment that is already in place, allowing doctors to maximize the effectiveness of current therapy.

A new type of research uses machine learning methods, such as 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 proven to be useful for the prediction of treatment outcomes like the response to antidepressants. These methods are becoming more popular in psychiatry and will likely become the norm in the future medical practice.

In addition to prediction models based on ML The study of the underlying mechanisms of depression continues. Recent research suggests that the disorder is connected 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.

Internet-delivered interventions can be an effective method to accomplish this. They can offer an individualized and tailored experience for patients. One study found that a web-based program was more effective than standard treatment in alleviating symptoms and ensuring an improved quality of life for people with MDD. A controlled, randomized study of a personalized treatment for situational depression treatment showed that a substantial percentage of participants experienced sustained improvement and had fewer adverse negative effects.

Predictors of Side Effects

A major obstacle in individualized depression treatment involves identifying and predicting the antidepressant medications that will have very little or no side effects. Many patients have a trial-and error approach, with various medications prescribed before finding one that is safe and effective. Pharmacogenetics offers a fascinating new way to take an efficient and specific approach to selecting antidepressant treatments.

There are several variables that can be used to determine which antidepressant should be prescribed, including genetic variations, patient phenotypes like gender or ethnicity and co-morbidities. To identify the most reliable and valid predictors of a specific treatment, randomized controlled trials with larger samples will be required. This is because it could be more difficult to identify moderators or interactions in trials that comprise only one episode per participant instead of multiple episodes spread over a long period of time.

In addition, predicting a patient's response will likely require information about the comorbidities, symptoms profiles and the patient's subjective experience of tolerability and effectiveness. At present, only a handful of easily identifiable sociodemographic variables and clinical variables seem to be consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI 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 essential, as is an understanding of what is a reliable predictor of treatment response. In addition, ethical concerns, such as privacy and the appropriate use of personal genetic information, must be considered carefully. In the long term, pharmacogenetics may offer a chance to lessen the stigma that surrounds mental health treatment and to improve the outcomes of those suffering with depression. But, like all approaches to psychiatry, careful consideration and application is required. At present, it's recommended to provide patients with an array of depression medications that work and encourage them to talk openly with their physicians.