Personalized Depression Treatment

Traditional therapy and medication don't work for a majority of patients suffering from depression. A customized treatment may be the solution.

Cue is an intervention platform that transforms sensor data collected from smartphones into personalized micro-interventions for improving mental health. We looked at the best-fitting personal ML models for each individual using Shapley values to determine their features and predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

Depression is one of the world's leading causes of mental illness.1 However, only half of those suffering from the disorder receive treatment1. To improve the outcomes, clinicians need to be able to recognize and treat patients with the highest probability of responding to particular treatments.

A customized depression holistic treatment for anxiety and depression plan can aid. Using mobile phone sensors 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 determine which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to discover the biological and behavioral indicators of response.

The majority of research conducted to the present has been focused on clinical and sociodemographic characteristics. These include factors that affect the demographics like age, sex and educational level, clinical characteristics like symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these aspects can be predicted from data in medical records, few studies have used longitudinal data to study the causes of mood among individuals. Many studies do not consider the fact that moods can be very different 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. The team can then develop algorithms to identify patterns of behavior and emotions that are unique to each person.

The team also developed an algorithm for machine learning to identify dynamic predictors of each person's depression mood. The algorithm combines these individual characteristics into a distinctive "digital phenotype" for each participant.

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

Predictors of Symptoms

depression treatments is the most common cause of disability in the world1, but it is often misdiagnosed and untreated2. Depressive disorders are often not treated due to the stigma attached to them and the lack of effective interventions.

To aid in the development of a personalized treatment, it is important to determine the predictors of symptoms. However, the current methods for predicting symptoms depend on the clinical interview which is not reliable and only detects a tiny variety of characteristics associated with depression treatment elderly - Read More In this article -.2

Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors with a valid mental health tracker online (the Computerized Adaptive Testing alternative depression treatment options Inventory CAT-DI). Digital phenotypes can be used to provide a wide range of unique behaviors and activities that are difficult to record through interviews and permit continuous and high-resolution measurements.

The study comprised University of California Los Angeles students with moderate to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment in accordance with their severity of depression. Those with a CAT-DI score of 35 or 65 were assigned online support via a peer coach, while those with a score of 75 patients were referred for psychotherapy in person.

Participants were asked a series of questions at the beginning of the study about their demographics and psychosocial traits. The questions included age, sex and education, financial status, marital status, whether they were divorced or not, the frequency of suicidal thoughts, intent or attempts, and the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression treatment drugs-related symptoms on a scale ranging from zero to 100. CAT-DI assessments were conducted every other week for the participants who received online support and once a week for those receiving in-person support.

Predictors of Treatment Response

Personalized depression treatment is currently a major research area and a lot of studies are aimed at identifying predictors that will allow clinicians to identify the most effective medication for each patient. Pharmacogenetics in particular uncovers genetic variations that affect how the body's metabolism reacts to drugs. This allows doctors select medications that will likely work best for each patient, while minimizing the amount of time and effort required for trials and errors, while avoiding any side consequences.

Another promising approach is building models for prediction using multiple data sources, including the clinical information with neural imaging data. These models can be used to identify which variables are the most likely to predict a specific outcome, such as whether a medication can improve symptoms or mood. These models can also be used to predict the patient's response to a treatment they are currently receiving and help doctors maximize the effectiveness of current therapy.

A new type of research utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables and increase predictive accuracy. These models have been demonstrated to be effective in predicting outcomes of treatment like the response to antidepressants. These methods are becoming more popular in psychiatry and will likely become the norm in the future medical practice.

In addition to the ML-based prediction models, research into the mechanisms behind depression continues. Recent research suggests that the disorder is connected with neural dysfunctions that affect specific circuits. This theory suggests that an individualized treatment for depression will be based upon targeted treatments that restore normal function to these circuits.

One method to achieve this is to use internet-based interventions which can offer an individualized and tailored experience for patients. One study found that a web-based program was more effective than standard care in improving symptoms and providing a better quality of life for patients with MDD. A controlled study that was randomized to a customized treatment for depression showed that a significant percentage of patients saw improvement over time and had fewer adverse negative effects.

Predictors of Side Effects

A major obstacle in individualized depression treatment involves identifying and predicting the antidepressant medications that will have minimal or no side effects. Many patients are prescribed a variety medications before settling on a treatment that is safe and effective. Pharmacogenetics offers a new and exciting method to choose antidepressant drugs that are more efficient and targeted.

There are a variety of predictors that can be used to determine the antidepressant that should be prescribed, including gene variations, phenotypes of the patient such as gender or ethnicity, and the presence of comorbidities. To determine the most reliable and accurate predictors for a specific treatment options for depression, randomized controlled trials with larger numbers of participants will be required. This is because it could be more difficult to determine moderators or interactions in trials that comprise only one episode per person instead of multiple episodes spread over time.

Furthermore, predicting a patient's response will likely require information about the severity of symptoms, comorbidities and the patient's personal perception of effectiveness and tolerability. At present, only a handful of easily measurable sociodemographic variables as well as clinical variables seem to be reliably related to response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

There are many challenges to overcome when it comes to the use of pharmacogenetics to treat depression. First is a thorough understanding of the underlying genetic mechanisms is required and a clear definition of what constitutes a reliable predictor for treatment response. Ethics, such as privacy, and the ethical use of genetic information must also be considered. In the long run the use of pharmacogenetics could offer a chance to lessen the stigma that surrounds mental health treatment and to improve the outcomes of those suffering with depression. However, as with any other psychiatric treatment, careful consideration and application is essential. At present, it's best to offer patients a variety of medications for depression that are effective and encourage them to speak openly with their doctor.