The Three Greatest Moments In Personalized Depression Treatment History

Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medication isn't effective. A customized treatment could be the solution.

Cue is an intervention platform for digital devices that converts passively collected smartphone sensor data into personalized micro-interventions designed to improve mental health. We looked at the best-fitting personal ML models for each individual, using Shapley values to discover their features and predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is among the leading causes of mental illness.1 However, only half of those suffering from the condition receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients most likely to benefit from certain treatments.

A customized depression treatment plan can aid. By using sensors on mobile phones and 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 the treatments they receive. Two grants worth more than $10 million will be used to determine the biological and behavioral factors that predict response.

To date, the majority of research on predictors for depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographic factors such as age, sex and educational level, clinical characteristics like the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.

While many of these aspects can be predicted by the information in medical records, only a few studies have utilized longitudinal data to explore the factors that influence mood in people. Few studies also consider the fact that mood can be very different between individuals. Therefore, it is crucial to devise methods that allow for the determination and quantification of the individual differences between mood predictors, treatment effects, etc.

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 behavior and emotions that are unique to each individual.

In addition to these modalities the team created a machine learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype has been linked to CAT DI scores which is a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.

Predictors of symptoms

Depression is among the most prevalent causes of disability1, but it is often underdiagnosed and undertreated2. In addition an absence of effective interventions and stigmatization associated with depression disorders hinder many from seeking treatment.

To aid in the development of a personalized treatment plan to improve natural treatment for anxiety and depression, identifying the patterns that can predict symptoms is essential. However, the current methods for predicting symptoms rely on clinical interview, which is not reliable and only detects a small number of symptoms that are associated with depression.2

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements. They also capture a wide variety of distinct behaviors and patterns that are difficult to document using interviews.

The study included University of California Los Angeles students with moderate to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or clinical care according to the severity of their depression. Those with a score on the CAT-DI of 35 65 were assigned online support via an online peer coach, whereas those with a score of 75 patients were referred 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, whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as how to treat depression and anxiety without medication often they drank. Participants also rated their level of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI assessment was conducted every two weeks for participants who received online support, and weekly for those who received in-person assistance.

Predictors of the Reaction to Treatment

Research is focused on individualized depression treatment. Many studies are focused on finding predictors, which can help doctors determine the most effective drugs to treat each patient. Particularly, pharmacogenetics can identify genetic variants that determine the way that the body processes antidepressants. This lets doctors select the medication that will likely work best for each patient, while minimizing the time and effort needed for trials and errors, while avoiding any side effects.

Another promising approach is building prediction models using multiple data sources, such as the clinical information with neural imaging data. These models can be used to identify which variables are most likely to predict a specific outcome, like whether a medication can improve symptoms or mood. These models can also be used to predict a patient's response to an existing treatment, allowing doctors to maximize the effectiveness of their treatment currently being administered.

A new generation of studies uses machine learning methods such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and increase predictive accuracy. These models have been demonstrated to be useful in predicting outcomes of treatment, such as response to antidepressants. These approaches are becoming more popular in psychiatry and will likely be the norm in future treatment.

The study of depression's underlying mechanisms continues, as well as predictive models based on ML. Recent findings suggest that the disorder is connected with neurodegeneration in particular circuits. This theory suggests that a individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.

One method to achieve this is by using internet-based programs which can offer an personalized and customized experience for patients. One study found that a program on the internet was more effective than standard treatment in alleviating symptoms and ensuring a better quality of life for people with MDD. In addition, a controlled randomized trial of a personalized approach to treating depression showed steady improvement and decreased adverse effects in a significant number of participants.

Predictors of side effects

In the treatment of depression, the biggest challenge is predicting and identifying the antidepressant that will cause no or minimal side effects. Many patients are prescribed a variety drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant drugs that are more effective and specific.

A variety of predictors are available to determine which antidepressant to prescribe, such as gene variations, 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 holistic treatment for depression is likely to require randomized controlled trials of much larger samples than those normally enrolled in clinical trials. This is because it could be more difficult to identify interactions or moderators in trials that comprise only one episode per participant instead of multiple episodes spread over a period of time.

Furthermore, the prediction of a patient's reaction to a specific medication will also likely need to incorporate information regarding comorbidities and symptom profiles, in addition to the patient's prior subjective experience with tolerability and efficacy. Currently, only a few easily assessable sociodemographic variables and clinical variables appear to be consistently associated with response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

Many challenges remain when it comes to the use of pharmacogenetics to treat depression. First, it is essential to have a clear understanding and definition of the genetic factors that cause depression, and a clear definition of an accurate indicator of the response to treatment. In addition, ethical issues such as privacy and the appropriate use of personal genetic information, must be considered carefully. In the long-term pharmacogenetics can provide an opportunity to reduce the stigma associated with mental health treatment and improve treatment outcomes for those struggling with depression. As with any psychiatric approach, it is important to carefully consider and implement the plan. At present, it's ideal to offer patients a variety of medications for depression that are effective and urge them to speak openly with their physicians.