Why You Should Concentrate On Enhancing Personalized Depression Treatment Personalized Depression Treatment

Traditional therapy and medication do not work for many patients suffering from depression. The individual approach to treatment could be the answer.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into customized 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 characteristic predictors. The results revealed distinct characteristics that deterministically changed mood over time.

Predictors of Mood

Depression is one of the world's leading causes of mental illness.1 However, only half of those suffering from the disorder receive treatment1. To improve the outcomes, doctors must be able identify and treat patients who are the most likely to benefit from certain treatments.

Personalized depression treatment is one method to achieve this. Utilizing sensors on mobile phones, an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from the treatments they receive. Two grants worth more than $10 million will be used to discover biological and behavioral predictors of response.


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

Few studies have used longitudinal data to predict mood in individuals. Many studies do not consider the fact that mood can be very different between individuals. Therefore, it is crucial to develop methods that allow for the identification of different mood predictors for each person and the effects of treatment.

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 recognize patterns of behaviour and emotions that are unique to each person.

In addition to these modalities, the team developed a machine-learning algorithm to model the changing variables that influence each person's mood. The algorithm blends the individual differences to produce a unique "digital genotype" for each participant.

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

Predictors of symptoms

Depression is among the most prevalent causes of disability1, but it is often not properly diagnosed and treated. In addition an absence of effective treatments and stigma associated with depression disorders hinder many from seeking treatment.

To help with personalized treatment, it is crucial to determine the predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only detect a few characteristics that are associated with depression.

Using machine learning to combine continuous digital behavioral phenotypes of a person captured through smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of severity of symptoms can improve diagnostic accuracy and increase the effectiveness of treatment for depression. These digital phenotypes provide a wide range of distinct behaviors and activities that are difficult to record through interviews and permit continuous, high-resolution measurements.

The study involved University of California Los Angeles (UCLA) students experiencing mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical care depending on their depression severity. Those with a CAT-DI score of 35 or 65 were assigned online support via the help of a coach. Those with a score 75 patients were referred to in-person psychotherapy.

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial traits. The questions asked included age, sex and education as well as marital status, financial status as well as whether they divorced or not, current suicidal ideas, intent or attempts, and how often they drank. The CAT-DI was used to rate the severity of depression-related symptoms on a scale ranging from 0-100. The CAT-DI assessment was carried out every two weeks for those who received online support and weekly for those who received in-person support.

Predictors of Treatment Response

Research is focusing on personalized treatment for depression. Many studies are focused on identifying predictors, which will help doctors determine the most effective drugs to treat each individual. Particularly, pharmacogenetics is able to identify genetic variants that determine how the body metabolizes antidepressants. This allows doctors select medications that are most likely to work for every patient, minimizing time and effort spent on trial-and-error treatments and avoiding any side effects.

Iam Psychiatry is to create predictive models that incorporate the clinical data with neural imaging data. These models can then be used to identify the best combination of variables that are 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 and help doctors maximize the effectiveness of treatment currently being administered.

A new type of research utilizes 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 and increase predictive accuracy. These models have shown to be useful for forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming more popular in psychiatry and could be the norm in future treatment.

Research into the underlying causes of depression continues, as well as ML-based predictive models. Recent findings suggest that the disorder is connected with neurodegeneration in particular circuits. This suggests that the treatment for depression will be individualized based on targeted treatments that target these neural circuits to restore normal function.

Internet-delivered interventions can be an option to achieve this. They can provide an individualized and tailored experience for patients. A study showed that an internet-based program helped improve symptoms and improved quality life for MDD patients. In addition, a controlled randomized trial of a personalized treatment for depression demonstrated sustained improvement and reduced side effects in a significant proportion of participants.

Predictors of side effects

In the treatment of depression the biggest challenge is predicting and identifying the antidepressant that will cause very little or no negative side effects. Many patients are prescribed a variety drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics provides an exciting new way to take an efficient and specific method of selecting antidepressant therapies.

Many predictors can be used to determine the best antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g., sex or ethnicity) and comorbidities. To determine the most reliable and valid predictors for a particular treatment, random controlled trials with larger numbers of participants will be required. This is because the detection of interaction effects or moderators can be a lot more difficult in trials that only consider a single episode of treatment per participant instead of multiple sessions of treatment over a period of time.

Furthermore to that, predicting a patient's reaction will likely require information about comorbidities, symptom profiles and the patient's personal perception of effectiveness and tolerability. Currently, only a few easily identifiable sociodemographic variables and clinical variables seem to be reliable in predicting the response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

Many challenges remain in the application of pharmacogenetics for depression treatment. First is a thorough understanding of the underlying genetic mechanisms is essential, as is a clear definition of what constitutes a reliable predictor for treatment response. Ethics like privacy, and the responsible use of genetic information should also be considered. In the long-term pharmacogenetics can offer a chance to lessen the stigma associated with mental health treatment and improve treatment outcomes for those struggling with depression. As with any psychiatric approach it is essential to take your time and carefully implement the plan. For now, the best option is to provide patients with various effective medications for depression and encourage them to speak with their physicians about their experiences and concerns.