The Three Greatest Moments In Personalized Depression Treatment History

Personalized Depression Treatment

Traditional therapies and medications don't work for a majority of people suffering from depression. Personalized treatment could be the answer.

Cue is a digital intervention platform that converts passively collected smartphone sensor data into personalized micro-interventions that improve mental health. We looked at the best treatment for anxiety and depression-fitting personal ML models for each individual, using Shapley values to determine their characteristic predictors. This revealed distinct features that were deterministically changing mood over time.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 Yet, only half of people suffering from the disorder receive treatment1. To improve outcomes, healthcare professionals must be able to recognize and treat patients who are the most likely to respond to certain treatments.

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from certain treatments. They use sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence and other digital tools. With two grants awarded totaling more than $10 million, they will make use of these technologies to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.

The majority of research into predictors of depression treatment history treatment effectiveness has been focused on sociodemographic and clinical characteristics. These include demographics such as age, gender and education, as well as clinical characteristics like symptom severity, comorbidities and biological markers.

A few studies have utilized longitudinal data to predict mood of individuals. Few also take into account the fact that mood can vary significantly between individuals. Therefore, it is crucial to devise methods that permit the determination and quantification of the personal differences between mood predictors and treatment effects, for instance.

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 individual.

In addition to these modalities, the team also developed a machine-learning algorithm that models the dynamic predictors of each person's depressed mood. The algorithm combines the individual characteristics to create an individual "digital genotype" for each participant.

This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (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 the leading cause of disability in the world1, however, it is often untreated and misdiagnosed. In addition an absence of effective interventions and stigma associated with depressive disorders prevent many from seeking treatment.

To help with 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 is unreliable and only detects a tiny number of features that are associated with depression treatment online.2

Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes gathered from smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes capture a large number of distinct behaviors and activities, which are difficult to capture through interviews, and allow for continuous and high-resolution measurements.

The study included University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care according to the degree of their depression. Participants with a CAT-DI score of 35 or 65 were assigned online support via the help of a peer coach. those with a score of 75 patients were referred for psychotherapy in-person.

At the beginning, participants answered the answers to a series of questions concerning their personal demographics and psychosocial features. The questions asked included age, sex, and education and financial status, marital status as well as whether they divorced or not, the frequency of suicidal thoughts, intent 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 conducted every two weeks for those 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 major research area and a lot of studies are aimed to identify predictors that enable clinicians to determine the most effective medication for each person. Pharmacogenetics, in particular, uncovers genetic variations that affect how the body's metabolism reacts to drugs. This allows doctors to select drugs that are likely to be most effective for each patient, reducing the time and effort required in trial-and-error procedures and avoid any adverse effects that could otherwise slow advancement.

Another promising approach is to build prediction models combining the clinical data with neural imaging data. These models can be used to determine which variables are most predictive of a particular outcome, such as whether a medication can help with symptoms or mood. These models can also be used to predict the patient's response to a treatment they are currently receiving which allows doctors to maximize the effectiveness of treatment currently being administered.

A new generation employs machine learning techniques such as the supervised and classification algorithms such as regularized logistic regression, and tree-based methods to integrate the effects of several variables and increase the accuracy of predictions. These models have been demonstrated to be useful in predicting the outcome of treatment, such as response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could be the norm in future clinical practice.

In addition to the ML-based prediction models The study of the mechanisms behind depression is continuing. Recent findings suggest that depression is related to dysfunctions in specific neural networks. This theory suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.

Internet-based-based therapies can be a way to achieve this. They can provide an individualized and tailored experience for patients. One study found that a program on the internet was more effective than standard treatment in alleviating symptoms and ensuring a better quality of life for people suffering from MDD. In addition, a controlled randomized trial of a personalized approach to treating depression showed steady improvement and decreased side effects in a significant percentage of participants.

Predictors of Side Effects

In the treatment of depression the biggest challenge is predicting and determining which antidepressant medications will have no or minimal side effects. Many patients are prescribed a variety medications before finding a medication that is safe and effective. Pharmacogenetics provides an exciting new avenue for a more efficient and specific method of selecting antidepressant therapies.

Many predictors can be used to determine which antidepressant to prescribe, such as gene variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and comorbidities. However finding the most reliable and valid predictive factors for a specific treatment will probably require randomized controlled trials of considerably larger samples than those that are typically part of clinical trials. This is because the identifying of interaction effects or moderators could be more difficult in trials that consider a single episode of treatment per participant, rather than multiple episodes of treatment over a period of time.

Additionally, predicting a patient's response will likely require information about comorbidities, symptom profiles and the patient's subjective perception of the effectiveness and tolerability. Presently, only a handful of easily assessable sociodemographic and clinical variables appear to be reliably associated with response to MDD like age, gender race/ethnicity BMI and the presence of alexithymia and the severity of depression symptoms.

The application of pharmacogenetics to treatment for depression is in its early stages and there are many obstacles to overcome. First, a clear understanding of the underlying genetic mechanisms is required as well as a clear definition of what is the best treatment for anxiety and depression constitutes a reliable predictor for treatment response. Additionally, ethical issues like privacy and the ethical use of personal genetic information should be considered with care. The use of pharmacogenetics may eventually, reduce stigma surrounding treatments for mental depression treatment illness and improve treatment outcomes. As with all psychiatric approaches, it is important to take your time and carefully implement the plan. The best method is to offer patients various effective depression medications and encourage them to talk openly with their doctors about their concerns and experiences.