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Personalized Depression Treatment

For many suffering from depression, traditional therapy and medications are not effective. A customized treatment could be the answer.

Cue is an intervention platform for digital devices that translates passively acquired normal smartphone sensor data into personalized micro-interventions designed meds to treat anxiety and depression improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to discover their feature predictors and uncover distinct features that are able to change mood as time passes.

Predictors of Mood

Depression is a major cause of mental illness around the world.1 Yet only half of those suffering from the condition receive treatment. To improve outcomes, healthcare professionals must be able identify and treat patients who are most likely to respond to certain treatments.

Personalized depression treatment is one way to do this. By using 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 predict which patients will benefit from the treatments they receive. Two grants worth more than $10 million will be used to discover biological and behavior predictors of response.

So far, the majority of research on predictors for depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographics such as gender, age and education, and clinical characteristics such as symptom severity and comorbidities as well as biological markers.

Very few studies have used longitudinal data to predict mood of individuals. Few studies also take into account the fact that moods can differ significantly between individuals. Therefore, it is essential to develop methods that allow for the recognition of different mood predictors for each person and treatment 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. This enables the team to create algorithms that can detect various patterns of behavior and emotions that vary between individuals.

The team also devised a machine-learning algorithm that can create dynamic predictors for the mood of each person's depression. The algorithm combines the individual differences to produce an individual "digital genotype" for each participant.

This digital phenotype was linked to CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.

Predictors of symptoms

post natal depression treatment 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 Pregnancy Depression Treatment disorders hinder many people from seeking help.

To help with personalized treatment, it is essential to identify the factors that predict symptoms. The current prediction methods rely heavily on clinical interviews, which aren't reliable and only identify a handful of characteristics that are associated with depression.

Machine learning is used to combine continuous digital behavioral phenotypes that are captured by sensors on smartphones and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory, CAT-DI) with other predictors of symptom severity can improve the accuracy of diagnosis and the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements. They also capture a wide range of unique behaviors and activity patterns that are difficult to capture using interviews.

The study included University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or clinical care depending on the severity of their depression. Patients who scored high on the CAT DI of 35 65 were assigned online support via the help of a coach. Those with scores of 75 patients were referred for psychotherapy in person.

Participants were asked a series questions at the beginning of the study regarding their psychosocial and demographic characteristics as well as their socioeconomic status. The questions included education, age, sex and gender as well as financial status, marital status and whether they were divorced or not, the frequency of suicidal ideas, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. The CAT-DI tests were conducted every week for those that received online support, and every week for those who received in-person care.

Predictors of non pharmacological treatment for depression Response

The development of a personalized depression treatment is currently a research priority, and many studies aim at identifying predictors that enable clinicians to determine the most effective medications for each patient. Particularly, pharmacogenetics can identify genetic variants that determine how the body's metabolism reacts to antidepressants. This lets doctors select the medication that will likely work best for each patient, reducing the time and effort needed for trials and errors, while avoiding any side consequences.

Another approach that is promising is to build models of prediction using a variety of data sources, such as clinical information and neural imaging data. These models can then be used to determine the most appropriate combination of variables that are predictive of a particular outcome, like whether or not a drug will improve mood and symptoms. These models can be used to determine a patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of current treatment.

A new generation uses machine learning methods such as algorithms for classification and supervised learning, regularized logistic regression and tree-based techniques to combine the effects of multiple variables to improve the accuracy of predictive. These models have proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the norm in the future medical practice.

Research into depression's underlying mechanisms continues, in addition to predictive models based on ML. Recent findings suggest that the disorder is connected with dysfunctions in specific neural circuits. This theory suggests that a individualized treatment for depression will depend on targeted therapies that restore normal functioning to these circuits.

One way to do this is by using internet-based programs which can offer an individualized and personalized experience for patients. For instance, 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 patients suffering from MDD. A controlled, randomized study of an individualized treatment for depression showed that a significant number of participants experienced sustained improvement and had fewer adverse effects.

Predictors of Side Effects

A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed various medications before settling on a treatment that is safe and effective. Pharmacogenetics offers a fresh and exciting method to choose antidepressant drugs that are more effective and precise.

There are several predictors that can be used to determine which antidepressant should be prescribed, including genetic variations, patient phenotypes such as ethnicity or gender, and the presence of comorbidities. To identify the most reliable and valid predictors for a specific treatment, randomized controlled trials with larger samples will be required. This is due to the fact that it can be more difficult to detect the effects of moderators or interactions in trials that only include one episode per person instead of multiple episodes over a period of time.

Additionally, the prediction of a patient's response to a particular medication is likely to require information on comorbidities and symptom profiles, and the patient's previous experience with tolerability and efficacy. At present, only a handful of easily assessable sociodemographic variables and clinical variables appear to be reliably related to response to MDD. These include gender, age, race/ethnicity as well as BMI, SES and the presence of alexithymia.

Many issues remain to be resolved when it comes to the use of pharmacogenetics in the treatment of depression. First, it is important to have a clear understanding and definition of the genetic factors that cause depression treatment techniques, and a clear definition of an accurate indicator of the response to treatment. Ethics such as privacy and the responsible use genetic information are also important to consider. In the long term, pharmacogenetics may offer a chance to lessen the stigma associated with mental health treatment and improve the outcomes of those suffering with depression. But, like any approach to psychiatry careful consideration and implementation is necessary. The best option is to offer patients various effective medications for depression and encourage them to talk with their physicians about their concerns and experiences.