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Personalized Depression Treatment

Traditional therapy and medication do not work for many patients suffering from depression. The individual approach to treatment could be the solution.

Cue is an intervention platform that converts sensor data collected from smartphones into personalized micro-interventions for improving mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to understand their feature predictors and uncover distinct characteristics that can be used to predict changes in mood as time passes.

Predictors of Mood

Depression is among the leading causes of mental illness.1 However, only half of people suffering from the condition receive treatment1. To improve the outcomes, clinicians need to be able to recognize and treat patients with the highest likelihood of responding to particular treatments.

Personalized depression treatment can help. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most effective treatment for depression from specific treatments. They are using sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence, and other digital tools. Two grants totaling more than $10 million will be used to determine the biological and behavioral predictors of response.

The majority of research on predictors for depression treatment effectiveness has focused on clinical and sociodemographic characteristics. These include demographics like age, gender and education and clinical characteristics like severity of symptom and comorbidities, as well as biological markers.

Few studies have used longitudinal data to predict mood of individuals. A few studies also take into consideration the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that permit the recognition of the individual differences in mood predictors and treatment 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. This allows the team to create algorithms that can systematically identify various patterns of behavior and emotion that differ between individuals.

In addition to these modalities the team developed a machine-learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.

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

Predictors of symptoms

Depression is one of the most prevalent causes of disability1, but it is often not properly diagnosed and treated. Depression disorders are usually not treated due to the stigma attached to them and the lack of effective treatments.

To assist in individualized treatment, it is crucial to identify the factors that predict symptoms. Current prediction methods rely heavily on clinical interviews, which are not reliable and only detect a few features associated with depression.

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to capture a large number of unique actions and behaviors that are difficult to capture through interviews, and also allow for continuous, high-resolution measurements.

The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. who were 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 assistance or medical care depending on the degree of their depression. Patients who scored high on the CAT-DI scale of 35 65 were assigned online support via an instructor and those with scores of 75 were routed to in-person clinics for psychotherapy.

At baseline, participants provided the answers to a series of questions concerning their personal demographics and psychosocial characteristics. The questions asked included education, age, sex and gender and financial status, marital status and whether they were divorced or not, current suicidal ideas, intent or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale from 0-100. 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 Response

A customized holistic treatment for anxiety and depression for depression is currently a research priority, and many studies aim at identifying predictors that will enable clinicians to determine the most effective medications for each patient. In particular, pharmacogenetics identifies genetic variants that influence how the body metabolizes antidepressants. This enables doctors to choose drugs that are likely to work best for each patient, minimizing the time and effort in trials and errors, while avoid any adverse effects that could otherwise slow advancement.

Another approach that is promising is to create prediction models that combine clinical data and neural imaging data. These models can be used to determine the variables that are most likely to predict a specific outcome, like whether a medication can help with symptoms or mood. These models can be used to determine the response of a patient to a treatment, which will help doctors maximize the effectiveness.

A new type of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and improve the accuracy of predictive. These models have been demonstrated to be useful in predicting treatment resistant bipolar depression outcomes for example, the response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to be the norm in future treatment.

Research into the underlying causes of depression continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is associated with neural dysfunctions that affect specific circuits. This theory suggests that individual depression treatment will be built around targeted therapies that target these circuits to restore normal functioning.

Internet-based interventions are an effective method to accomplish this. They can offer an individualized and tailored experience for patients. One study found that an internet-based program helped improve symptoms and provided a better quality life for MDD patients. A controlled study that was randomized to a personalized treatment for depression showed that a significant percentage of patients saw improvement over time as well as fewer side effects.

Predictors of Side Effects

A major issue in personalizing depression treatment is predicting which antidepressant medications will have very little or no side effects. Many patients are prescribed a variety drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics offers a fascinating new avenue for a more efficient and specific method of selecting antidepressant therapies.

There are many variables that can be used to determine the antidepressant that should be prescribed, including genetic variations, phenotypes of patients such as ethnicity or gender and the presence of comorbidities. However, identifying the most reliable and accurate predictive factors for a specific treatment will probably require randomized controlled trials of significantly larger numbers of participants than those normally enrolled in clinical trials. This is because it may be more difficult to identify the effects of moderators or interactions in trials that contain only one episode per participant instead of multiple episodes over time.

In addition, predicting a patient's response will likely require information about comorbidities, symptom profiles and the patient's personal experience of tolerability and effectiveness. Currently, only some easily identifiable sociodemographic and clinical variables are believed to be reliable in predicting the response to MDD, such as age, gender race/ethnicity, SES, BMI and the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics to post stroke depression treatment treatment is still in its early stages and there are many obstacles to overcome. It is crucial to have a clear understanding and definition of the genetic mechanisms that cause depression, and a clear definition of an accurate predictor of treatment response. Additionally, ethical issues like privacy and the appropriate use of personal genetic information, must be carefully considered. In the long run, pharmacogenetics may be a way to lessen the stigma associated with mental health care and improve the treatment outcomes for patients with depression. But, like any approach to psychiatry careful consideration and application is necessary. In the moment, it's best to offer patients a variety of medications for depression that work and encourage them to talk openly with their physicians.