Personalized Depression Treatment

Traditional treatment and medications do not work for many patients suffering from depression. A customized treatment could be the solution.

Cue is an intervention platform that transforms sensors that are passively gathered from smartphones into personalised micro-interventions to improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their feature predictors and uncover distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

Depression is one of the world's leading causes of mental illness.1 However, only half of those who have the condition receive treatment1. To improve outcomes, healthcare professionals must be able to recognize and treat patients who are the most likely to respond to specific treatments.

Personalized depression treatment in pregnancy treatment is one method of doing this. Using sensors on mobile phones, 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 worth more than $10 million will be used to discover biological and behavior indicators of response.

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

While many of these aspects can be predicted by the information available in medical records, only a few studies have employed 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 develop methods that permit the determination and quantification of the individual differences between mood predictors and treatment effects, for instance.

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 will then create algorithms to detect patterns of behaviour and emotions that are unique to each individual.

The team also created a machine learning algorithm to identify dynamic predictors of each person's mood for depression. The algorithm integrates the individual differences to produce an individual "digital genotype" for each participant.

The digital phenotype was associated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. However, the correlation was weak (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is the most common cause of disability in the world1, but it is often untreated and misdiagnosed. Depressive disorders are often not treated due to the stigma that surrounds them and the lack of effective interventions.

To help with personalized treatment, it is important to identify the factors that predict symptoms. However, current prediction methods depend on the clinical interview which is unreliable and only detects a limited variety of characteristics related to depression.2

Machine learning can increase the accuracy of the diagnosis and treatment of depression treatment plan cbt by combining continuous digital behavior phenotypes collected from smartphone sensors with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements. They also capture a wide variety of distinctive behaviors and activity patterns that are difficult to document using interviews.

The study included University of California Los Angeles (UCLA) students with mild to severe depression symptoms. enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical treatment in accordance with their severity of depression. Patients who scored high on the CAT-DI scale of 35 65 students were assigned online support with the help of a coach. Those with scores of 75 patients were referred for psychotherapy in person.

At the beginning, participants answered an array of questions regarding their personal demographics and psychosocial characteristics. The questions included education, age, sex and gender as well as financial status, marital status, whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their degree of depression severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted each other week for participants who received online support and weekly for those receiving in-person treatment.

Predictors of Treatment Response

Personalized depression treatment is currently a research priority and a lot of studies are aimed to identify predictors that help clinicians determine the most effective medications for each individual. Pharmacogenetics in particular identifies genetic variations that determine how the human body metabolizes drugs. This allows doctors to select the medications that are most likely to be most effective for each patient, reducing the time and effort required in trial-and-error treatments and avoid any adverse effects that could otherwise slow the progress of the patient.

Another promising method is to construct models of prediction using a variety of data sources, including data from clinical studies and neural imaging data. These models can be used to determine which variables are most predictive of a specific outcome, such as whether a medication will improve mood or symptoms. These models can be used to determine a patient's response to treatment that is already in place which allows doctors to maximize the effectiveness of treatment currently being administered.

A new generation of studies utilizes 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 proven to be effective in predicting the outcome of treatment 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.

In addition to ML-based prediction models research into the mechanisms that cause depression is continuing. Recent research suggests that depression is linked to dysfunctions in specific neural networks. This suggests that individualized depression treatment will be focused on therapies that target these neural circuits to restore normal function.

One method to achieve this is to use internet-based interventions that offer a more individualized and tailored experience for patients. One study found that an internet-based program helped improve symptoms and led to a better quality life for MDD patients. Additionally, a randomized controlled study of a customized approach to depression private treatment treatment showed steady improvement and decreased adverse effects in a significant number of participants.

Predictors of Side Effects

A major obstacle in individualized depression treatment involves identifying and predicting the antidepressant medications that will have the least amount of side effects or none at all. Many patients experience a trial-and-error approach, with various medications prescribed before finding one that is safe and effective. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant drugs that are more efficient and targeted.

Several predictors may be used to determine which antidepressant to prescribe, including gene variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. To identify the most reliable and accurate predictors for a specific treatment, random controlled trials with larger samples will be required. This is because the detection of interactions or moderators could be more difficult in trials that only focus on a single instance of treatment per patient instead of multiple sessions of treatment over a period of time.

Furthermore, predicting a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's subjective perception of effectiveness and tolerability. At present, only a handful of easily identifiable sociodemographic variables and clinical variables seem to be consistently associated with response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many hurdles to overcome. First is a thorough understanding of the genetic mechanisms is essential and a clear definition of what is a reliable predictor of treatment response. In addition, ethical issues, such as privacy and the appropriate use of personal genetic information, must be carefully considered. Pharmacogenetics can, in the long run, reduce stigma surrounding treatments for mental illness and improve the quality of treatment. As with all psychiatric approaches, it is important to give careful consideration and implement the plan. The best method is to provide patients with an array of effective depression treatment residential medications and encourage them to talk openly with their doctors about their concerns and experiences.