Personalized Depression Treatment

Traditional treatment and medications do not work for many patients suffering from depression. A customized treatment may be the answer.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions to improve mental health. We looked at the best-fitting personal ML models for each individual using Shapley values, in order to understand their feature predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

Depression Treatment Without Meds is among the most prevalent causes of mental illness.1 However, only half of those suffering from the disorder receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat depression patients who are the most likely to respond to specific treatments.

A customized depression treatment is one way to do this. Utilizing mobile phone sensors and an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from which treatments. Two grants worth more than $10 million will be used to discover biological and behavior indicators of response.

The majority of research to date has focused on clinical and sociodemographic characteristics. These include demographics like age, gender and education, and clinical characteristics like symptom severity and comorbidities, as well as biological markers.

While many of these factors can be predicted by the data in medical records, only a few studies have used longitudinal data to explore the causes of mood among individuals. Few studies also consider the fact that moods can be very different between individuals. Therefore, it is important to devise methods that permit the determination and quantification of the individual 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 identify various patterns of behavior and emotions that differ between individuals.

The team also devised a machine-learning algorithm that can identify dynamic predictors of each person's mood for depression. The algorithm blends these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, which is a psychometrically validated symptom severity scale. However the correlation was not strong (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is one of the leading causes of disability1 yet it is often not properly diagnosed and treated. Depression disorders are rarely treated because of the stigma attached to them, as well as the lack of effective treatments.

To aid in the development of a personalized treatment, it is essential to determine the predictors of symptoms. However, the methods used to predict symptoms are based on the clinical interview, which has poor reliability and only detects a small variety of characteristics 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 gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements. They also capture a wide range of unique behaviors and activity patterns that are difficult to record through interviews.

The study involved University of California Los Angeles (UCLA) students who were suffering from moderate 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 referred to online support or to clinical treatment based on the severity of their depression treatment free. Those with a score on the CAT-DI of 35 or 65 were given online support via a coach and those with scores of 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 characteristics. The questions included age, sex and education, financial status, marital status and whether they were divorced or not, their current suicidal ideas, intent or attempts, and the frequency with which they consumed alcohol. Participants also scored their level of depression symptom severity on a scale of 0-100 using the CAT-DI. CAT-DI assessments were conducted each other week for the participants that received online support, and once a week for those receiving in-person care.

Predictors of the Reaction to Treatment

A customized treatment for depression is currently a top research topic and many studies aim to identify predictors that enable clinicians to determine the most effective drugs for each person. Particularly, pharmacogenetics is able to identify genetic variants that determine the way that the body processes antidepressants. This allows doctors select medications that are most likely to work for each patient, reducing the time and effort needed for trial-and error treatments and avoid any negative side effects.

Another approach that is promising is to build models of prediction using a variety of data sources, including clinical information and neural imaging data. These models can be used to identify the variables that are most predictive of a specific outcome, like whether a medication can improve symptoms or mood. These models can be used to predict the patient's response to a treatment, allowing doctors maximize the effectiveness.

A new era of research uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These approaches are gaining popularity in psychiatry and it is likely that they will become the standard for future clinical practice.

Research into depression's underlying mechanisms continues, as do predictive models based on ML. Recent findings suggest that depression treatment techniques is connected to dysfunctions in specific neural networks. This theory suggests that individual depression treatment will be based on targeted treatments that target these circuits in order to restore normal function.

Internet-delivered interventions can be an effective method to accomplish this. They can offer more customized and personalized experience for patients. For instance, one study discovered that a web-based treatment was more effective than standard care in alleviating symptoms and ensuring an improved quality of life for those with MDD. A randomized controlled study of an individualized treatment for depression found that a significant percentage of patients experienced sustained improvement and had fewer adverse consequences.

Predictors of side effects

In the treatment of depression one of the most difficult aspects is predicting and determining which antidepressant medication to treat anxiety and depression will have very little or no negative side negative effects. Many patients have a trial-and error approach, with a variety of medications prescribed before finding one that is effective and tolerable. Pharmacogenetics provides a novel and exciting method of selecting antidepressant drugs that are more effective and precise.

Several predictors may be used to determine the best antidepressant to prescribe, including gene variations, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. However it is difficult to determine the most reliable and reliable predictive factors for a specific treatment is likely to require controlled, randomized trials with much larger samples than those typically enrolled in clinical trials. This is due to the fact that it can be more difficult to detect interactions or moderators in trials that comprise only one episode per participant instead of multiple episodes spread over time.

Additionally, predicting a patient's response will likely require information about the severity of symptoms, comorbidities and the patient's own perception of effectiveness and tolerability. There are currently only a few easily measurable sociodemographic variables as well as clinical variables are consistently associated with response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

The application of pharmacogenetics to treatment for depression is in its early stages and there are many hurdles to overcome. first line treatment for anxiety and depression is a thorough understanding of the genetic mechanisms is needed as well as a clear definition of what constitutes a reliable predictor for treatment response. Ethics like privacy, and the responsible use of genetic information are also important to consider. Pharmacogenetics can, in the long run help reduce stigma around mental health treatment and improve the quality of treatment. However, as with any other psychiatric treatment, careful consideration and application is essential. The best option is to offer patients a variety of effective depression medications and encourage them to talk freely with their doctors about their concerns and experiences.