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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medication isn't effective. The individual approach to treatment could be the solution.

Cue is a digital intervention platform that translates passively acquired normal smartphone sensor data into personalized micro-interventions to improve mental health. We analyzed the best-fitting personalized ML models to each person, using Shapley values to determine their characteristic predictors. This revealed distinct features that were deterministically changing mood over time.

Predictors of Mood

Depression is one of the most prevalent causes of mental illness.1 However, only about half of people suffering from the disorder receive treatment1. To improve outcomes, clinicians must be able identify and treat patients who are most likely to respond to specific treatments.

The treatment of depression can be personalized to help. Using sensors on mobile phones and 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 which treatments. Two grants worth more than $10 million will be used to identify biological and behavior indicators of response.

The majority of research to so far has focused on clinical and sociodemographic characteristics. These include demographic variables like age, sex and education, clinical characteristics such as symptoms severity and comorbidities and biological markers like neuroimaging and genetic variation.

Very few studies have used longitudinal data to predict mood of individuals. A few studies also consider the fact that mood can vary significantly between individuals. Therefore, it is critical to create methods that allow the determination of individual differences in mood predictors 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 allows the team to develop algorithms that can detect distinct patterns of behavior and emotions that differ between individuals.

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 integrates the individual differences to create a unique "digital genotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, which is a psychometrically validated scale for assessing severity of symptom. The correlation was weak, however (Pearson r = 0,08; P-value adjusted for BH = 3.55 x 10 03) and varied greatly among individuals.

Predictors of Symptoms

Depression is among the leading causes of disability1 yet it is often untreated and not diagnosed. 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 in order to provide a more personalized treatment, identifying predictors of symptoms is important. However, the methods used to predict symptoms are based on the clinical interview, which is unreliable and only detects a small variety of characteristics related to depression.2

Machine learning is used to integrate continuous digital behavioral phenotypes that are captured by smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, CAT-DI) together with other predictors of severity of symptoms could improve diagnostic accuracy and increase the effectiveness of treatment for depression. Digital phenotypes permit continuous, high-resolution measurements and capture a wide range of unique behaviors and activity patterns that are difficult to document using interviews.

The study enrolled University of California Los Angeles (UCLA) students experiencing mild to severe depression symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical care in accordance with their severity of depression. Patients with a CAT DI score of 35 65 were assigned online support with the help of a peer coach. those with a score of 75 were sent to in-person clinics for psychotherapy.

Participants were asked a series questions at the beginning of the study about their demographics and psychosocial characteristics. These included age, sex and education, as well as work and financial situation; whether they were partnered, divorced or single; their current suicidal ideas, intent or attempts; as well as the frequency with that they consumed alcohol. Participants also rated their level of depression symptom severity on a scale of 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 care.

Predictors of Treatment Response

Research is focused on individualized depression treatment. Many studies are focused on identifying predictors, which will help doctors determine the most effective drugs to treat each individual. Pharmacogenetics, in particular, identifies genetic variations that determine how to treat depression and anxiety without medication the body's metabolism reacts to drugs. This lets doctors select the medication that will likely work best for each patient, reducing time and effort spent on trials and errors, while eliminating any adverse consequences.

Another promising approach is to create predictive models that incorporate information from clinical studies and neural imaging data. These models can be used to identify which variables are the most predictive of a particular outcome, such as whether a medication will improve symptoms or mood. These models can also be used to predict the patient's response to treatment that is already in place which allows doctors to maximize the effectiveness of the treatment currently being administered.

A new type of research utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be effective in predicting treatment for panic attacks and depression outcomes such as the response to antidepressants. These methods are becoming more popular in psychiatry, and are likely to become the standard of future treatment.

Research into depression's underlying mechanisms continues, as well as ML-based predictive models. Recent findings suggest that depression is connected to the dysfunctions of specific neural networks. This theory suggests that an individualized treatment for depression will depend on targeted therapies that restore normal function to these circuits.

One method to achieve this is by using internet-based programs which can offer an individualized and personalized experience for patients. One study found that an internet-based program improved symptoms and improved quality life for MDD patients. A controlled, randomized study of a customized treatment for depression found that a significant number of patients experienced sustained improvement and had fewer adverse effects.

Predictors of adverse effects

A major obstacle in individualized depression treatment involves identifying and predicting which antidepressant medications will cause the least amount of side effects or none at all. Many patients are prescribed various drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics provides a novel and exciting method of selecting antidepressant medications that is more effective and precise.

Many predictors can be used to determine the best 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 of a specific treatment, controlled trials that are randomized with larger sample sizes will be required. This is due to the fact that the identification of interaction effects or moderators may be much more difficult in trials that take into account a single episode of treatment per patient instead of multiple episodes of treatment over a period of time.

Furthermore the prediction of a patient's response to a specific medication is likely to require information about symptoms and comorbidities and the patient's personal experiences with the effectiveness and tolerability of the medication. Currently, only some easily measurable sociodemographic and clinical variables are believed to be reliable in predicting the severity of MDD factors, including gender, age race/ethnicity, SES, BMI and the presence of alexithymia and the severity of depression symptoms.

Many issues remain to be resolved in the application of pharmacogenetics to treat depression. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that underlie hormonal depression treatment, as well as an accurate definition of an accurate indicator of the response to treatment. Additionally, ethical issues, such as privacy and the appropriate use of personal genetic information must be considered carefully. Pharmacogenetics can, in the long run help reduce stigma around mental health treatments and improve the outcomes of treatment. However, as with all approaches to psychiatry, careful consideration and application is essential. At present, the most effective course of action is to offer patients an array of effective medications for depression treatment free and encourage them to talk freely with their doctors about their experiences and concerns.