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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medications are not effective. Personalized treatment may be the solution.

Cue is a digital intervention platform that translates passively acquired normal sensor data from smartphones into personalised micro-interventions that improve mental health. We looked at the best-fitting personal ML models to each subject, using Shapley values, in order to understand their characteristic predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only half of people suffering from the disorder receive treatment1. To improve outcomes, doctors must be able to recognize and treat patients who have the highest likelihood of responding to particular treatments.

A customized depression treatment is one way to do this. Using sensors for 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 predict which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to determine biological and behavior indicators of response.

The majority of research done ways to treat depression date has focused on clinical and sociodemographic characteristics. These include demographic variables such as age, gender and educational level, clinical characteristics like symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these factors can be predicted from the data in medical records, very few studies have employed longitudinal data to determine the factors that influence mood in people. Many studies do not take into consideration the fact that moods can be very different between individuals. Therefore, it is critical to create methods that allow the identification of different mood predictors for each person and the effects of treatment.

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 is able to develop algorithms to identify patterns of behavior and emotions that are unique to each individual.

In addition to these modalities, the team developed a machine-learning algorithm that models the dynamic factors that determine a person's depressed mood. The algorithm combines these individual differences into a unique "digital phenotype" for each participant.

This digital phenotype was found to be associated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was weak however (Pearson r = 0,08; P-value adjusted by BH 3.55 10 03) and varied significantly between individuals.

Predictors of symptoms

Depression is among the world's leading causes of disability1 yet it is often not properly diagnosed and treated. Depressive disorders are often not treated because of the stigma attached to them and the absence of effective treatments.

To help with personalized treatment, it is important to determine the predictors of symptoms. However, current prediction methods depend on the clinical interview which is unreliable and only detects a small variety of characteristics that are associated with depression.2

Machine learning can improve the accuracy of diagnosis and psychological treatment for depression for depression by combining continuous digital behavioral phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing depression treatment online Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements as well as capture a wide range of distinct behaviors and patterns that are difficult to record through interviews.

The study involved University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care based on the severity of their depression treatment History. Participants who scored a high on the CAT-DI of 35 65 were assigned to online support with an online peer coach, whereas those who scored 75 patients were referred to in-person psychotherapy.

Participants were asked a series of questions at the beginning of the study concerning their psychosocial and demographic characteristics as well as their socioeconomic status. The questions included education, age, sex and gender, financial status, marital status, whether they were divorced or not, depression Treatment history current suicidal thoughts, intentions or attempts, and how often they drank. The CAT-DI was used to rate the severity of depression-related symptoms on a scale from zero to 100. The CAT-DI assessment was carried out every two weeks for participants who received online support, and weekly for those who received in-person assistance.

Predictors of Treatment Response

Research is focusing on personalization of treatment for depression. Many studies are focused on finding predictors that can help doctors determine the most effective medications to treat each individual. Particularly, pharmacogenetics can identify genetic variants that determine the way that the body processes antidepressants. This enables doctors to choose medications that are likely to be most effective for each patient, minimizing the time and effort required in trial-and-error procedures and avoid any adverse effects that could otherwise slow progress.

Another promising approach is to build 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 particular outcome, such as whether a drug will help with symptoms or mood. These models can be used to determine the patient's response to a treatment, allowing doctors maximize the effectiveness.

A new era of research utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables to improve predictive accuracy. These models have been proven to be effective in the prediction of treatment outcomes like the response to antidepressants. These approaches are gaining popularity 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 findings suggest that the disorder is connected with neural dysfunctions that affect specific circuits. This theory suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.

Internet-based-based therapies can be a way to accomplish this. They can offer an individualized and tailored experience for patients. A study showed that an internet-based program improved symptoms and led to a better quality of life for MDD patients. A controlled study that was randomized to a customized treatment for depression treatment centers revealed that a substantial percentage of participants experienced sustained improvement as well as fewer side effects.

Predictors of Side Effects

In the treatment of depression a major challenge is predicting and determining which antidepressant medication will have very little or no adverse negative effects. Many patients have a trial-and error method, involving a variety of medications prescribed until they find one that is effective and tolerable. Pharmacogenetics is an exciting new way to take an effective and precise method of selecting antidepressant therapies.

There are many predictors that can be used to determine the antidepressant to be prescribed, including genetic variations, phenotypes of patients such as gender or ethnicity and co-morbidities. To identify the most reliable and valid predictors of a specific treatment, controlled trials that are randomized with larger samples will be required. This is because the detection of interactions or moderators could be more difficult in trials that only take into account a single episode of treatment per person, rather than multiple episodes of treatment over a period of time.

Additionally, predicting a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's subjective perception of the effectiveness and tolerability. At present, only a few easily measurable sociodemographic and clinical variables are believed to be correlated with the severity of MDD factors, including gender, Depression Treatment history age race/ethnicity BMI and the presence of alexithymia, and the severity of depression symptoms.

There are many challenges to overcome in the application of pharmacogenetics for depression treatment. It is crucial to be able to comprehend and understand the definition of the genetic factors that cause depression, and a clear definition of an accurate predictor of treatment response. Additionally, ethical issues, such as privacy and the appropriate use of personal genetic information must be carefully considered. In the long term the use of pharmacogenetics could provide an opportunity to reduce the stigma associated with mental health treatment and to improve treatment outcomes for those struggling with depression. Like any other psychiatric treatment, it is important to give careful consideration and implement the plan. In the moment, it's best to offer patients a variety of medications for depression that are effective and encourage patients to openly talk with their physicians.