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Personalized Depression Treatment

Traditional therapies and medications do not work for many people who are depressed. A customized treatment could be the answer.

Cue is a digital intervention platform that transforms passively acquired smartphone sensor data into personalized micro-interventions to improve mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to understand their feature predictors and uncover distinct features that deterministically change mood over time.

Predictors of Mood

Depression is one of the most prevalent causes of mental illness.1 However, only about half of people suffering from the condition receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat depression patients who have the highest probability of responding to certain treatments.

Personalized depression treatment is one method of doing this. By using mobile phone sensors, an artificial intelligence voice assistant and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to treat depression to predict which patients will benefit from the treatments they receive. With two grants awarded totaling over $10 million, they will make use of these tools to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.

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

While many of these factors can be predicted from information available in medical records, few studies have utilized longitudinal data to study the causes of mood among individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is crucial to create methods that allow the recognition of different mood predictors for each person 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 enables the team to create algorithms that can identify distinct patterns of behavior and emotion that are different between people.

The team also developed an algorithm for machine learning to identify dynamic predictors of each person's extreme depression treatment, Read A lot more, mood. The algorithm blends the individual differences to produce a unique "digital genotype" for each participant.

This digital phenotype was correlated 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 among individuals.

Predictors of Symptoms

depression treatment without antidepressants is among the world's leading causes of disability1 yet it is often untreated and not diagnosed. Depressive disorders are often not treated because of the stigma associated with them and the lack of effective interventions.

To aid in the development of a personalized treatment resistant bipolar depression, it is important to determine the predictors of symptoms. However, the methods used to predict symptoms are based on the clinical interview, which is not reliable and only detects a small variety of characteristics related to depression.2

Machine learning can enhance 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). Digital phenotypes are able to capture a large number of unique actions and behaviors that are difficult to record through interviews and permit continuous and high-resolution measurements.

The study involved University of California Los Angeles (UCLA) students with mild to severe depressive symptoms participating 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 according to the degree of their depression. Patients who scored high on the CAT DI of 35 or 65 were assigned to online support via the help of a peer coach. those who scored 75 patients were referred for psychotherapy in person.

At baseline, participants provided an array of questions regarding their personal demographics and psychosocial features. The questions included education, age, sex and gender and financial status, marital status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, and the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale ranging from 0-100. The CAT-DI test was performed every two weeks for participants who received online support, and weekly for those who received in-person assistance.

Predictors of homeopathic treatment for depression Response

A customized treatment for depression is currently a research priority and many studies aim at identifying predictors that will allow clinicians to identify the most effective medication for each person. Pharmacogenetics, in particular, identifies genetic variations that determine the way that our bodies process drugs. This allows doctors select medications that are likely to be the most effective for every patient, minimizing the time and effort needed for trials and errors, while eliminating any adverse consequences.

Another option is to create predictive models that incorporate information from clinical studies and neural imaging data. These models can then be used to determine the most appropriate combination of variables that are predictive of a particular outcome, like whether or not a particular medication is likely to improve mood and symptoms. These models can be used to determine the response of a patient to a treatment they are currently receiving and help doctors maximize the effectiveness of their current treatment.

A new generation of machines employs machine learning techniques such as supervised and classification algorithms such as regularized logistic regression, and tree-based methods to integrate the effects from multiple variables to improve the accuracy of predictive. These models have shown to be useful for predicting treatment outcomes such as the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the standard for the future of clinical practice.

Research into the underlying causes of depression continues, as well as ML-based predictive models. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.

Internet-based interventions are a way to accomplish this. They can provide 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, randomized study of an individualized treatment for depression found that a significant number of patients experienced sustained improvement as well as fewer side negative effects.

Predictors of adverse effects

In the treatment of depression the biggest challenge is predicting and determining the antidepressant that will cause no or minimal adverse negative effects. Many patients have a trial-and error method, involving a variety of medications being prescribed before settling on one that is safe and effective. Pharmacogenetics is an exciting 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 gene variations, phenotypes of the patient like gender or ethnicity, and the presence of comorbidities. However it is difficult to determine the most reliable and accurate predictors for a particular treatment will probably require randomized controlled trials of much larger samples than those typically enrolled in clinical trials. This is because the detection of interaction effects or moderators may be much more difficult in trials that only consider a single episode of treatment per participant instead of multiple episodes of treatment over time.

In addition the prediction of a patient's response will likely require information on comorbidities, symptom profiles and the patient's personal perception of the effectiveness and tolerability. There are currently only a few easily measurable sociodemographic variables as well as clinical variables appear to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

There are many challenges to overcome in the application of pharmacogenetics for depression treatment. First it is necessary to have a clear understanding of the underlying genetic mechanisms is essential and a clear definition of what constitutes a reliable predictor for treatment response. Additionally, ethical issues such as privacy and the appropriate use of personal genetic information, must be carefully considered. Pharmacogenetics can be able to, over the long term, reduce stigma surrounding treatments for mental illness and improve the quality of treatment. However, as with any other psychiatric treatment, careful consideration and planning is necessary. At present, the most effective option is to provide patients with various effective depression medications and encourage them to talk openly with their doctors about their concerns and experiences.