10 Meetups On Personalized Depression Treatment You Should Attend

Personalized Depression Treatment

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

Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalised micro-interventions for improving mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to understand their feature predictors and reveal distinct features that deterministically change mood with time.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 Yet, only half of those who have the condition receive treatment1. To improve outcomes, healthcare professionals must be able identify and treat patients who are the most likely to benefit from certain treatments.

A customized depression treatment options treatment plan can aid. Using sensors on mobile phones, 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 for depression. With two grants awarded totaling over $10 million, they will employ these tools to identify the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

The majority of research done to so far has focused on sociodemographic and clinical characteristics. These include demographic factors like age, sex and educational level, clinical characteristics like symptoms severity and comorbidities and biological indicators such as neuroimaging and genetic variation.

Very few studies have used longitudinal data to predict mood in individuals. Few studies also consider the fact that moods can be very different between individuals. Therefore, it is crucial to create methods that allow 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 is able to develop algorithms to detect patterns of behavior and emotions that are unique to each individual.

The team also developed a machine-learning algorithm that can model dynamic predictors for each person's mood for depression. The algorithm combines these personal differences into a unique "digital phenotype" for each participant.

The digital phenotype was associated with CAT-DI scores, which is a psychometrically validated symptom severity scale. However the correlation was tinny (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 the most common cause of disability in the world, but it is often untreated and misdiagnosed. Depressive disorders are often not treated due to the stigma attached to them, as well as the lack of effective interventions.

To aid in the development of a personalized treatment plan, identifying factors that predict the severity of symptoms is crucial. However, the methods used to predict symptoms depend on the clinical interview which is not reliable and only detects a small number of symptoms that are associated with depression.2

Using machine learning to blend continuous digital behavioral phenotypes of a person captured by smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) together with other predictors of severity of symptoms could improve diagnostic accuracy and increase the effectiveness of treatment for depression. Digital phenotypes are able to capture a large number of unique actions and behaviors that are difficult to document through interviews and permit high-resolution, continuous measurements.

The study involved University of California Los Angeles students with moderate to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and depression and anxiety treatment near me program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical treatment according to the severity of their depression. Patients who scored high on the CAT DI of 35 or 65 students were assigned online support by an instructor and those with scores of 75 patients were referred for in-person psychotherapy.

At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal demographics and psychosocial characteristics. The questions asked included age, sex and education as well as marital status, financial status and whether they were divorced or not, the frequency of 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 test 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

The development of a personalized depression treatment is currently a top research topic, and many studies aim at identifying predictors that will help clinicians determine the most effective medication for each person. Particularly, pharmacogenetics can identify genetic variants that influence how the body's metabolism reacts to antidepressants. This allows doctors to select medications that are likely to work best for each patient, reducing the time and effort involved in trial-and-error treatments and eliminating any side effects that could otherwise slow progress.

Another promising method is to construct models for prediction using multiple data sources, including clinical information and neural imaging data. These models can be used to determine the best combination of variables that is predictors of a specific outcome, such as whether or not a particular medication is likely to improve the mood and symptoms. These models can be used to predict the response of a patient to a treatment, allowing doctors maximize the effectiveness.

A new generation employs machine learning methods such as the supervised and classification algorithms such as regularized logistic regression, and tree-based methods to combine the effects of multiple variables and increase the accuracy of predictions. These models have shown to be useful in the prediction of treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry and it is likely that they will become the norm for future clinical practice.

Research into the underlying causes of depression continues, as well as ML-based predictive models. Recent research suggests that depression is linked to dysfunctions in specific neural networks. This theory suggests that individual depression treatment will be focused on therapies that target these circuits to restore normal functioning.

One method to achieve this is by using internet-based programs which can offer an individualized and tailored experience for patients. One study found that an internet-based program helped improve symptoms and provided a better quality life for MDD patients. In addition, a controlled randomized study of a personalised treatment for panic attacks and depression for depression demonstrated an improvement in symptoms and fewer adverse effects in a significant number of participants.

Predictors of side effects

In the treatment of depression, the biggest challenge is predicting and identifying which antidepressant medications will have minimal or zero negative side effects. Many patients are prescribed a variety medications before settling on a treatment that is effective and tolerated. Pharmacogenetics is an exciting new way to take an efficient and specific method of selecting antidepressant therapies.

Many predictors can be used to determine which antidepressant to prescribe, such as gene variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and comorbidities. To identify the most reliable and accurate predictors for a specific treatment, randomized controlled trials with larger samples will be required. This is because the detection of interactions or moderators can be a lot more difficult in trials that only take into account a single episode of treatment per participant, rather than multiple episodes of treatment over a period of time.

Additionally to that, predicting a patient's reaction will likely require information about the severity of symptoms, comorbidities and the patient's subjective experience of tolerability and effectiveness. Presently, only a handful of easily assessable sociodemographic and clinical variables appear to be reliable in predicting the severity of MDD like age, gender race/ethnicity, SES BMI and the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics in treatment for depression is in its infancy and there are many obstacles to overcome. First, a clear understanding of the genetic mechanisms is needed, as is a clear definition of what is a reliable predictor of treatment response. In addition, ethical concerns like privacy and the ethical use of personal genetic information, must be considered carefully. In the long term the use of pharmacogenetics could offer a chance to lessen the stigma associated with mental health care and improve the outcomes of those suffering with depression. As with all psychiatric approaches it is crucial to carefully consider and implement the plan. For now, the best course of action is to offer patients various effective depression medication options and encourage them to talk openly with their doctors about their experiences and concerns.