Shantae Lankford

Personalized Depression Treatment

For many suffering from depression treatment Tms, traditional therapies and medication are ineffective. A customized treatment may be the solution.

Cue is an intervention platform that transforms sensor data collected from smartphones into personalized 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 reveal distinct features that are able to change mood as time passes.

Predictors of Mood

Depression is one of the most prevalent causes of mental illness.1 However, only half of people suffering from the condition receive treatment1. To improve outcomes, clinicians need to be able to identify and treat patients who have the highest likelihood of responding to certain treatments.

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most from specific treatments. They make use of sensors for mobile phones and a voice assistant incorporating artificial intelligence and other digital tools. Two grants worth more than $10 million will be used to determine biological and behavioral indicators of response.

The majority of research conducted to date has focused on sociodemographic and clinical characteristics. These include demographic variables such as age, gender and education, clinical characteristics including symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

Few studies have used longitudinal data in order to predict mood in individuals. A few studies also consider the fact that mood can differ significantly between individuals. Therefore, it is critical to develop methods that permit the determination of different mood predictors for each person and treatments 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. This enables the team alternative ways to treat depression develop algorithms that can identify different patterns of behavior and emotion that vary between individuals.

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

The digital phenotype was associated with CAT-DI scores, which is a psychometrically validated severity scale for symptom severity. However the correlation was tinny (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 among the most prevalent causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated because of the stigma attached to them and the lack of effective interventions.

To assist in individualized treatment, it is essential to identify predictors of symptoms. However, the methods used to predict symptoms are based on the clinical interview, which has poor reliability and only detects a tiny 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 patterns gathered from sensors on smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes are able to capture a variety of unique behaviors and activities that are difficult to document through interviews, and also allow for continuous, high-resolution measurements.

The study involved University of California Los Angeles students with mild depression treatments to severe depression symptoms who were participating in the Screening and non pharmacological treatment for depression for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for assistance or medical care based on the degree of their depression. Those with a CAT-DI score of 35 or 65 were allocated online support with the help of a peer coach. those who scored 75 patients were referred for psychotherapy in person.

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

Predictors of Treatment Response

A customized treatment for depression is currently a research priority and a lot of studies are aimed to identify predictors that allow clinicians to identify the most effective medication for each person. Pharmacogenetics, in particular, is a method of identifying genetic variations that affect how the human body metabolizes drugs. This lets doctors select the medication that are likely to be the most effective for each patient, while minimizing the time and effort needed for trial-and-error treatments and eliminating any adverse effects.

Another option is to build prediction models combining the clinical data with neural imaging data. These models can be used to determine the best combination of variables predictors of a specific outcome, like whether or not a particular medication will improve symptoms and mood. These models can be used to determine the patient's response to a treatment they are currently receiving and help doctors maximize the effectiveness of the treatment currently being administered.

A new era of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of many variables to improve predictive accuracy. These models have been shown to be effective in predicting outcomes of treatment for example, the response to antidepressants. These models are getting more popular in psychiatry, and it is expected that they will become the standard for future clinical practice.

The study of depression's underlying mechanisms continues, in addition to predictive models based on ML. Recent research suggests that the disorder is associated with neural dysfunctions that affect specific circuits. This theory suggests that individualized depression treatment will be built around targeted treatments that target these circuits to restore normal functioning.

One way to do this is by using internet-based programs that can provide a more individualized and tailored experience for patients. One study found that an internet-based program helped improve symptoms and provided a better quality of life for MDD patients. Furthermore, a randomized controlled trial of a personalized approach to depression treatment showed an improvement in symptoms and fewer adverse effects in a significant percentage of participants.

Predictors of Side Effects

In the treatment of depression a major challenge is predicting and determining the antidepressant that will cause no or minimal adverse negative effects. Many patients are prescribed various medications before finding a medication that is safe and effective. Pharmacogenetics provides a novel and exciting method of selecting antidepressant drugs that are more efficient and targeted.

Many predictors can be used to determine which antidepressant is best to prescribe, including genetic variations, phenotypes of patients (e.g., sex or ethnicity) and comorbidities. However it is difficult to determine the most reliable and valid factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with significantly larger numbers of participants than those normally enrolled in clinical trials. This is because the identifying of interactions or moderators can be a lot more difficult in trials that take into account a single episode of treatment per patient instead of multiple sessions of treatment over time.

Furthermore to that, predicting a patient's reaction will likely require information about comorbidities, symptom profiles and the patient's subjective perception of the effectiveness and tolerability. At present, only a few easily assessable sociodemographic and clinical variables appear to be reliably associated with response to MDD factors, including gender, age race/ethnicity, SES BMI and the presence of alexithymia, and the severity of depressive symptoms.

There are many challenges to overcome when it comes to the use of pharmacogenetics to treat depression. First, it is important to have a clear understanding and definition of the genetic mechanisms that cause depression, and a clear definition of 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. In the long term pharmacogenetics can provide an opportunity to reduce the stigma associated with mental health ect treatment for depression and anxiety and to improve the treatment outcomes for patients with depression. Like any other psychiatric treatment it is crucial to take your time and carefully implement the plan. The best course of action is to provide patients with an array of effective depression medication options and encourage them to speak with their physicians about their experiences and concerns.