Personalized Depression Treatment

For many suffering from depression, traditional therapies and medications are not effective. Personalized treatment could be the answer.

Cue is a digital intervention platform that translates passively acquired normal 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 predictors of feature and reveal distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only half of those who have the disorder receive treatment1. To improve outcomes, clinicians must be able identify and treat patients 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 benefit the most from certain treatments. They are using sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence and other digital tools. Two grants were awarded that total more than $10 million, they will use these tools to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

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

While many of these variables can be predicted by the information in medical records, few studies have used longitudinal data to determine the causes of mood among individuals. They have not taken into account the fact that mood varies significantly between individuals. It is therefore important to develop methods which permit the analysis and measurement of personal differences between mood predictors treatments, mood predictors, 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. This allows the team to develop algorithms that can detect different patterns of behavior and emotions that vary between individuals.

In addition to these modalities the team also developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm combines the individual differences to create a unique "digital genotype" for each participant.

This digital phenotype was associated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was low, however (Pearson r = 0,08, P-value adjusted for BH = 3.55 10 03) and varied widely between individuals.

Predictors of symptoms

Depression is among the most prevalent causes of disability1 yet it is often not properly diagnosed and treated. In addition the absence of effective treatments and stigmatization associated with depressive disorders prevent many people from seeking help.

To assist in individualized treatment, it is important to identify predictors of symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which is unreliable and only detects a tiny number of symptoms that are associated with depression.2

Machine learning can increase the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to provide a wide range of unique actions and behaviors that are difficult to capture through interviews, and allow for continuous, high-resolution measurements.

The study involved University of California Los Angeles (UCLA) students who were suffering from Mild Depression Treatments to severe depressive symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were routed to online assistance or in-person clinics in accordance with their severity of depression. Patients with a CAT DI score of 35 or 65 were given online support with a coach and those with scores of 75 were sent to in-person clinical care for psychotherapy.

Participants were asked a series questions at the beginning of the study regarding their demographics and psychosocial characteristics. The questions included education, age, sex and gender and financial status, marital status and whether they were divorced or not, their current suicidal ideas, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their degree of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI assessment was carried out every two weeks for those who received online support, and weekly for those who received in-person support.

Predictors of the Reaction to Treatment

The development of a personalized depression treatment is currently a top research topic and a lot of studies are aimed to identify predictors that enable clinicians to determine the most effective medication for each individual. Particularly, pharmacogenetics is able to identify genetic variants that determine how the body metabolizes antidepressants. This enables doctors to choose the medications that are most likely to work best for each patient, reducing the time and effort required in trial-and-error treatments and eliminating any side effects that could otherwise slow the progress of the patient.

Another promising approach is building models of prediction using a variety of data sources, including the clinical information with neural imaging data. These models can be used to identify the best combination of variables that is predictors of a specific outcome, such as whether or not a drug will improve mood and symptoms. These models can be used to determine the patient's response to an existing treatment, allowing doctors to maximize the effectiveness of their treatment currently being administered.

A new generation of machines employs machine learning techniques like the supervised and classification algorithms, regularized logistic regression and tree-based techniques to combine the effects from multiple variables to improve the accuracy of predictive. These models have been proven to be effective in predicting the outcome of treatment, such as response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could become the norm in the future treatment.

In addition to prediction models based on ML research into the mechanisms that cause depression treatment guidelines continues. Recent research suggests that the disorder is connected with neurodegeneration in particular circuits. This suggests that an individualized treatment for depression will depend on targeted therapies that restore normal function to these circuits.

One way to do this is to use internet-based interventions that can provide a more individualized and tailored experience for patients. A study showed that an internet-based program improved symptoms and improved quality of life for MDD patients. Furthermore, a randomized controlled trial of a personalized approach to depression treatment for anxiety and depression near me showed steady improvement and decreased adverse effects in a significant proportion of participants.

Predictors of adverse effects

In the treatment of postpartum depression treatment, the biggest challenge is predicting and determining which antidepressant medications will have no or minimal side effects. Many patients are prescribed a variety of drugs before they find a non drug treatment for depression that is effective and tolerated. Pharmacogenetics offers a fresh and exciting way to select antidepressant medications that is more efficient and targeted.

There are a variety of predictors that can be used to determine the antidepressant to be prescribed, including genetic variations, phenotypes of patients such as ethnicity or gender and co-morbidities. However it is difficult to determine the most reliable and reliable factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials with much larger samples than those normally enrolled in clinical trials. This is because the identifying of interaction effects or moderators may be much more difficult in trials that only take into account a single episode of treatment per participant instead of multiple episodes of treatment over time.

Additionally, predicting a patient's response will likely require information about the comorbidities, symptoms profiles and the patient's personal perception of the effectiveness and tolerability. Presently, only a handful of easily identifiable sociodemographic and clinical variables are believed to be correlated with the response to MDD like age, gender, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many obstacles to overcome. First, it is essential to have a clear understanding and definition of the genetic mechanisms that cause depression, as well as an understanding of a reliable predictor of treatment response. Additionally, ethical issues such as privacy and the ethical use of personal genetic information, should be considered with care. In the long run pharmacogenetics can offer a chance to lessen the stigma associated with mental health treatment and to improve treatment outcomes for those struggling with depression. As with all psychiatric approaches it is crucial to take your time and carefully implement the plan. The best option is to offer patients an array of effective medications for depression and encourage them to talk openly with their doctors about their concerns and experiences.