Personalized Depression Treatment

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

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised 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 were deterministically changing mood over time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only about half of people suffering from the condition receive treatment1. To improve the outcomes, doctors must be able to identify and treat patients who have the highest probability of responding to certain treatments.

A customized depression treatment is one method to achieve this. By using sensors for mobile phones and an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new alternative ways to treat depression to determine which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to discover biological and behavioral predictors of response.

So far, the majority of research on predictors for depression treatment effectiveness has been focused on clinical and sociodemographic characteristics. These include factors that affect the demographics such as age, sex and education, clinical characteristics such as symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these variables can be predicted by the data in medical records, few studies have employed longitudinal data to study the causes of mood among individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is critical to develop methods that permit the identification of 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 behaviour and emotions that are unique to each person.

In addition to these modalities the team also developed a machine-learning algorithm that models the dynamic factors that determine a person's depressed mood. The algorithm integrates the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype has been correlated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was low, however (Pearson r = 0,08, BH adjusted P-value 3.55 10 03) and varied greatly between individuals.

Predictors of symptoms

depression and treatment is the leading cause of disability in the world, but it is often untreated and misdiagnosed. Depression disorders are rarely treated due to the stigma attached to them, as well as the lack of effective treatments.

To assist in individualized treatment, it is essential to determine the predictors of symptoms. However, the methods used to predict symptoms depend on the clinical interview which has poor reliability and only detects a limited variety of characteristics that are associated with depression.2

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

The study comprised University of California Los Angeles students with mild 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 directed to online support or to clinical treatment depending on the severity of their depression. Those with a score on the CAT-DI scale of 35 65 were assigned to online support with a peer coach, while those with a score of 75 patients were referred to in-person psychotherapy.

Participants were asked a series questions at the beginning of the study regarding their demographics and psychosocial characteristics. These included age, sex and education, as well as work and financial status; if they were divorced, married or single; the frequency of suicidal ideas, intent, or attempts; and the frequency with that they consumed alcohol. The CAT-DI was used for assessing the severity of depression symptoms on a scale of zero to 100. The CAT DI assessment was carried out every two weeks for those who received online support and weekly for those who received in-person care.

Predictors of Treatment Reaction

Personalized depression treatment is currently a research priority and many studies aim at identifying predictors that allow clinicians to identify the most effective medications for each patient. In particular, pharmacogenetics identifies genetic variants that determine how to treat depression and anxiety without medication the body metabolizes 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 negative effects.

Another promising approach is to develop prediction models that combine the clinical data with neural imaging data. These models can be used to identify which variables are most likely to predict a specific outcome, such as whether a drug will improve symptoms or mood. These models can also be used to predict a patient's response to a treatment they are currently receiving and help doctors maximize the effectiveness of treatment currently being administered.

A new type of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and improve the accuracy of predictive. These models have been proven to be useful in predicting outcomes of treatment, such as response to antidepressants. These methods are becoming popular in psychiatry, and it is likely that they will become the norm for future clinical practice.

The study of depression's underlying mechanisms continues, as well as ML-based predictive models. Recent research suggests that depression is connected to the malfunctions of certain neural networks. This theory suggests that individualized depression treatment will be built around targeted therapies that target these neural circuits to restore normal function.

One method of doing this is to use internet-based interventions that offer a more personalized and customized experience for patients. For instance, one study discovered that a web-based treatment was more effective than standard care in improving symptoms and providing a better quality of life for patients suffering from MDD. Additionally, a randomized controlled trial of a personalized treatment for depression demonstrated steady improvement and decreased adverse effects in a significant proportion of participants.

Predictors of Side Effects

A major challenge in personalized depression treatment involves identifying and predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed a variety of medications before finding a medication that is effective and tolerated. Pharmacogenetics is an exciting new way to take an efficient and targeted approach to choosing antidepressant medications.

Many predictors can be used to determine the best antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g. gender, sex or ethnicity) and the presence of comorbidities. However, identifying the most reliable and valid factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials of much larger samples than those typically enrolled in clinical trials. This is because the detection of interactions or moderators can be a lot more difficult in trials that only consider a single episode of treatment per person instead of multiple episodes of treatment over a period of time.

Additionally 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. There are currently only a few easily assessable sociodemographic variables and clinical variables seem to be reliable in predicting the response to MDD. These include gender, age, race/ethnicity, BMI, SES and the presence of alexithymia.

Many challenges remain in the use of pharmacogenetics for Hormonal depression treatment treatment. First, it is essential to have a clear understanding and definition of the genetic mechanisms that cause depression, and an accurate definition of an accurate indicator of the response to treatment. Ethics like privacy, and the responsible use genetic information are also important to consider. Pharmacogenetics can be able to, over the long term help reduce stigma around mental health treatments and improve treatment outcomes. As with any psychiatric approach it is crucial to give careful consideration and implement the plan. For now, the best option is to offer patients an array of effective depression medications and encourage them to speak freely with their doctors about their concerns and experiences.