Personalized Depression what treatment is there for depression (ai-db.science)

For many suffering from depression, traditional therapy and medications are not effective. A customized treatment could be the answer.

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

Predictors of Mood

Depression is among the leading causes of mental illness.1 However, only half of those suffering from the disorder receive treatment1. To improve outcomes, healthcare professionals must be able identify and treat patients who are the most likely to respond to certain treatments.

The ability to tailor depression treatments is one way to do this. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will gain the most from certain treatments. They use sensors for mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. With two grants totaling over $10 million, they will make use of these tools to identify the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

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

While many of these aspects can be predicted by the information in medical records, few studies have employed longitudinal data to determine the causes of mood among individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to create methods that allow 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. The team is able to develop algorithms to identify patterns of behaviour and emotions that are unique to each individual.

The team also developed a machine learning algorithm to model dynamic predictors for the mood of each person's depression. The algorithm blends the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype was found to be associated with CAT-DI scores, which is a psychometrically validated symptom severity scale. The correlation was not strong however (Pearson r = 0,08; BH adjusted P-value 3.55 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. Depression disorders are rarely treated due to the stigma associated with them, as well as the lack of effective treatments.

To help with personalized treatment, it is crucial to identify the factors that predict symptoms. However, current prediction methods are based on the clinical interview, which is not reliable and only detects a tiny number of symptoms that are associated with depression.2

Machine learning is used to combine continuous digital behavioral phenotypes of a person captured through smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory, CAT-DI) with other predictors of severity of symptoms could improve diagnostic accuracy and increase treatment for panic attacks and depression efficacy for dementia depression treatment. Digital phenotypes permit continuous, high-resolution measurements and capture a variety of distinct behaviors and patterns that are difficult to record with interviews.

The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and depression treatment options (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical care depending on their depression severity. Participants who scored a high on the CAT-DI scale of 35 or 65 were given online support by a coach and those with a score 75 patients were referred for in-person psychotherapy.

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial characteristics. These included sex, age and education, as well as work and financial status; if they were partnered, divorced or single; their current suicidal ideation, intent or attempts; as well as the frequency with which they drank alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale from 0-100. CAT-DI assessments were conducted every other week for the participants who received online support and weekly for those receiving in-person support.

Predictors of the Reaction to Treatment

The development of a personalized depression treatment is currently a major research area and many studies aim to identify predictors that enable clinicians to determine the most effective medication for each individual. Pharmacogenetics in particular uncovers genetic variations that affect how the body's metabolism reacts to drugs. This enables doctors to choose the medications that are most likely to work best for each patient, minimizing the time and effort in trial-and-error procedures and avoid any adverse effects that could otherwise slow the progress of the patient.

Another approach that is promising is to build prediction models combining information from clinical studies and neural imaging data. These models can then be used to identify the most effective 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, which will help doctors maximize the effectiveness.

A new generation of machines employs machine learning methods such as algorithms for classification and supervised learning such as regularized logistic regression, and tree-based methods to combine the effects of multiple variables and improve predictive accuracy. These models have proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to be the norm in future clinical practice.

Research into the underlying causes of depression continues, in addition to predictive models based on ML. Recent findings suggest that depression is linked to dysfunctions in specific neural networks. This suggests that individual depression treatment will be based on targeted therapies that target these neural circuits to restore normal functioning.

One method to achieve this is by using internet-based programs that offer a more individualized and tailored experience for patients. For instance, one study found that a program on the internet was more effective than standard treatment in alleviating symptoms and ensuring the best quality of life for those suffering from MDD. A controlled study that was randomized to an individualized treatment for depression found that a significant percentage of patients saw improvement over time and had fewer adverse consequences.

Predictors of Side Effects

In the treatment of depression, a major challenge is predicting and identifying which antidepressant medications will have minimal or zero adverse negative effects. Many patients are prescribed a variety of drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics offers a new and exciting way to select antidepressant drugs that are more effective and precise.

Several predictors may be used to determine the best antidepressant to prescribe, including gene variants, phenotypes of patients (e.g., sex or ethnicity) and the presence of comorbidities. However finding the most reliable and reliable factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials with considerably larger samples than those normally enrolled in clinical trials. This is because the identifying of interaction effects or moderators could be more difficult in trials that consider a single episode of treatment per patient, rather than multiple episodes of treatment over a period of time.

Additionally, the prediction of a patient's reaction to a particular medication will likely also require information about symptoms and comorbidities as well as the patient's previous experience with tolerability and efficacy. Currently, only some easily assessable sociodemographic and clinical variables seem to be reliably associated with the response to MDD factors, including gender, age, race/ethnicity and SES BMI, the presence of alexithymia, and the severity of depression symptoms.

Many challenges remain when it comes to the use of pharmacogenetics for depression treatment. First, it is important to have a clear understanding and definition of the genetic mechanisms that underlie depression, as well as an accurate definition of an accurate predictor of treatment response. Ethics like privacy, and the ethical use of genetic information should also be 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 implementation is required. For now, it is ideal to offer patients various prenatal depression treatment medications that work and encourage them to talk openly with their doctor.