Why We Why We Personalized Depression Treatment And You Should Too

Personalized Depression Treatment

For many suffering from menopause depression treatment, traditional therapy and medication isn't effective. The individual approach to treatment could be the answer.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into personalized micro-interventions to improve mental health. We analyzed the best-fitting personalized ML models to each person, using Shapley values to discover their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression treatment without Antidepressants is among the leading 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 most likely to respond to certain treatments.

A customized depression treatment is one way to do this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from specific treatments. They use sensors for mobile phones, a voice assistant with artificial intelligence and other digital tools. With two grants awarded totaling over $10 million, they will use these techniques to determine the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

To date, the majority of research on predictors for depression treatment effectiveness has focused on the sociodemographic and clinical aspects. These include demographics like age, gender, and education, as well as clinical characteristics like severity of symptom and comorbidities as well as biological markers.

While many of these aspects can be predicted from information available in medical records, very few studies have utilized longitudinal data to determine the factors that influence mood in people. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is essential to create methods that allow the determination of the individual differences in mood predictors and treatments 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 is able to develop algorithms to detect patterns of behavior and emotions that are unique to each individual.

In addition alternative ways to treat depression these modalities the team also developed a machine-learning algorithm to model the dynamic factors that determine a person's depressed mood. The algorithm blends these individual characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype was correlated with CAT DI scores, a psychometrically validated severity scale for symptom severity. The correlation was not strong, however (Pearson r = 0,08, BH adjusted P-value 3.55 10 03) and varied greatly among individuals.

Predictors of symptoms

Depression is among the most prevalent causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated due to the stigma attached to them and the absence of effective interventions.

To assist in individualized treatment, it is crucial to identify predictors of symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only detect a few symptoms associated with depression.

Using machine learning to integrate continuous digital behavioral phenotypes of a person captured by sensors on smartphones and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) together with other predictors of severity of symptoms can improve diagnostic accuracy and increase treatment efficacy for depression. Digital phenotypes can provide continuous, high-resolution measurements as well as capture a wide variety of distinct behaviors and patterns that are difficult to document with interviews.

The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depressive symptoms enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were sent online for assistance or medical care based on the degree of their depression. Patients with a CAT DI score of 35 65 students were assigned online support with an instructor and those with scores of 75 patients were referred to psychotherapy in-person.

Participants were asked a set of questions at the beginning of the study about their demographics and psychosocial characteristics. The questions asked included education, age, sex and gender as well as marital status, financial status and whether they were divorced or not, the frequency of suicidal ideas, intent or attempts, and how to treat depression and anxiety without medication often they drank. Participants also rated their level of depression symptom severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted each week for those that received online support, and once a week for those receiving in-person support.

Predictors of Treatment Response

Research is focusing on personalization of depression treatment. Many studies are focused on identifying predictors, which will aid clinicians in identifying the most effective medications to treat each individual. Pharmacogenetics, in particular, identifies genetic variations that determine the way that our bodies process drugs. This lets doctors choose the medications that are most likely to work for every patient, minimizing the amount of time and effort required for trials and errors, while avoiding any side negative effects.

Another approach that is promising is to develop prediction models combining clinical data and neural imaging data. These models can be used to identify the best combination of variables that is predictors of a specific outcome, such as whether or not a particular medication will improve symptoms and 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 the current therapy.

A new generation of studies employs machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) 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 popular in psychiatry, and it is likely that they will become the norm for the future of clinical practice.

In addition to the ML-based prediction models The study of the underlying mechanisms of depression continues. Recent findings suggest that the disorder is linked with neurodegeneration in particular circuits. This theory suggests that a individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.

One way to do this is by using internet-based programs that can provide a more individualized and tailored experience for patients. One study found that a program on the internet was more effective than standard treatment in improving symptoms and providing a better quality of life for those with MDD. A controlled study that was randomized to a personalized treatment for depression showed that a significant percentage of patients experienced sustained improvement and had fewer adverse effects.

Predictors of Side Effects

In the treatment of depression the biggest challenge is predicting and identifying which antidepressant medications will have no or minimal negative side negative effects. Many patients have a trial-and error method, involving several medications being prescribed before settling on one that is safe and effective. Pharmacogenetics provides a novel and exciting method of selecting antidepressant medications that is more effective and precise.

Several predictors may be used to determine which antidepressant is best to prescribe, including gene variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. However finding the most reliable and accurate predictors for a particular treatment is likely to require randomized controlled trials of considerably larger samples than those typically enrolled in clinical trials. This is due to the fact that the identification of moderators or interaction effects can be a lot more difficult in trials that consider a single episode of treatment per person instead of multiple episodes of treatment over time.

Furthermore, the prediction of a patient's reaction to a particular medication will likely also require information about symptoms and comorbidities and the patient's personal experience with tolerability and efficacy. There are currently only a few easily measurable sociodemographic variables as well as clinical variables appear to be reliably related to response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

The application of pharmacogenetics in depression treatment no medication treatment is still in its infancy, and many challenges remain. First it is necessary to have a clear understanding of the underlying genetic mechanisms is required as well as a clear definition of what is a reliable indicator of treatment response. Ethics, such as privacy, and the responsible use of genetic information should also be considered. The use of pharmacogenetics may, in the long run reduce stigma associated with mental health treatments and improve treatment outcomes. As with any psychiatric approach, it is important to carefully consider and implement the plan. At present, it's best to offer patients a variety of medications for depression that are effective and encourage them to speak openly with their physicians.