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Personalized Depression Treatment

For many people gripped by depression, traditional therapies and medications are not effective. A customized treatment may be the answer.

Cue is a digital intervention platform that transforms passively acquired sensor data from smartphones into personalised micro-interventions to improve mental health. We analyzed the best-fitting personalized ML models for each individual, using Shapley values to discover their characteristic predictors. This revealed distinct features that deterministically changed mood over time.

Predictors of Mood

post natal depression treatment is one of the most prevalent causes of mental illness.1 However, only about half of people suffering from the condition receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients most likely to respond to specific 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 benefit the most from certain treatments. They use mobile phone sensors and a voice assistant incorporating artificial intelligence as well as other digital tools. Two grants worth more than $10 million will be used to identify biological and behavioral factors that predict response.

So far, the majority of research into predictors of depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographic factors such as age, sex and education, clinical characteristics including symptom severity and comorbidities, and biological markers such as neuroimaging and genetic variation.

While many of these variables can be predicted from information in medical records, only a few studies have utilized longitudinal data to study predictors of mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is essential to develop methods that permit the identification of 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 will then create algorithms to identify patterns of behavior and emotions that are unique to each individual.

The team also devised an algorithm for machine learning to identify dynamic predictors of the mood of each person's depression. The algorithm blends these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype was linked to CAT DI scores which is a psychometrically validated symptom severity scale. The correlation was not strong however (Pearson r = 0,08, BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals.

Predictors of Symptoms

Depression is the most common cause of disability in the world1, but it is often not properly diagnosed and treated. Depression disorders are rarely treated because of the stigma that surrounds them, as well as the lack of effective treatments.

To facilitate personalized treatment to improve treatment, identifying the predictors of symptoms is important. The current prediction methods rely heavily on clinical interviews, which are unreliable and only identify a handful of symptoms associated with depression.

Machine learning can be used to combine continuous digital behavioral phenotypes captured by smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing depression treatment techniques Inventory CAT-DI) along with other indicators of symptom severity could improve diagnostic accuracy and increase treatment efficacy for depression. Digital phenotypes can be used to capture a large number of unique behaviors and activities that are difficult to document through interviews, and allow for continuous and high-resolution measurements.

The study involved University of California Los Angeles students with moderate to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and untreatable depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment based on the severity of their depression. Participants with a CAT-DI score of 35 65 were given online support by an instructor and those with a score 75 were sent to in-person clinics for psychotherapy.

Participants were asked a set of questions at the beginning of the study about their psychosocial and demographic characteristics as well as their socioeconomic status. These included age, sex and education, as well as work and financial situation; whether they were partnered, divorced, or single; current suicidal ideation, intent, or attempts; and 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 tests were conducted every other week for participants who received online support and every week for those who received in-person treatment.

Predictors of the Reaction to Treatment

Research is focused on individualized treatment for depression. Many studies are focused on finding predictors, which can aid clinicians in identifying the most effective medications to treat each individual. In particular, pharmacogenetics identifies genetic variations that affect how the body's metabolism reacts to antidepressants. This enables doctors to choose medications that are likely to be most effective for each patient, reducing the time and effort involved in trials and errors, while avoiding side effects that might otherwise slow advancement.

Another promising method is to construct prediction models using multiple data sources, such as clinical information and neural imaging data. These models can be used to determine which variables are most likely to predict a specific outcome, like whether a drug will help with symptoms or mood. These models can be used to determine the response of a patient to a treatment, which will help doctors to maximize the effectiveness of their treatment.

A new generation uses machine learning techniques such as the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of multiple variables and improve predictive accuracy. These models have shown to be effective treatments for depression in predicting treatment outcomes such as the response to antidepressants. These methods are becoming more popular in psychiatry and could become the standard of future treatment.

Research into depression's underlying mechanisms continues, as well as ML-based predictive models. Recent research suggests that depression is connected to dysfunctions in specific neural networks. This suggests that an individualized treatment for depression will be based upon targeted treatments that restore normal function to these circuits.

Internet-delivered interventions can be a way to achieve this. They can provide a more tailored and individualized experience for patients. For example, one study discovered that a web-based treatment was more effective than standard treatment in improving symptoms and providing an improved quality of life for those with MDD. A randomized controlled study of a personalized treatment for depression revealed that a significant number of patients experienced sustained improvement and had fewer adverse negative effects.

Predictors of adverse effects

In the treatment of depression the biggest challenge is predicting and determining which antidepressant medications will have no or minimal side negative effects. Many patients are prescribed a variety drugs before they find a drug that is safe and effective. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant medicines that are more effective and specific.

Many predictors can be used to determine which antidepressant is best to prescribe, such as gene variants, patient phenotypes (e.g., sex or ethnicity) and the presence of comorbidities. However, identifying the most reliable and accurate predictive factors for a specific treatment will probably require randomized controlled trials of considerably larger samples than those typically enrolled in clinical trials. This is because the detection of interaction effects or moderators can be a lot more difficult in trials that only consider a single episode of treatment per patient, rather than multiple episodes of treatment over time.

Furthermore the prediction of a patient's reaction to a particular medication will also likely need to incorporate information regarding the symptom profile and comorbidities, as well as the patient's prior subjective experience with tolerability and efficacy. There are currently only a few easily identifiable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include gender, age, race/ethnicity, BMI, SES and the presence of alexithymia.

There are many challenges to overcome in the application of pharmacogenetics in the treatment of depression. first line treatment for depression it is necessary to have a clear understanding of the genetic mechanisms is needed and an understanding of what is a reliable indicator of treatment response. Additionally, ethical issues, such as privacy and the responsible use of personal genetic information must be considered carefully. Pharmacogenetics can eventually help reduce stigma around mental health treatment and improve treatment outcomes. However, as with any other psychiatric treatment, careful consideration and implementation is necessary. For now, it is ideal to offer patients a variety of medications for depression that are effective and urge them to talk openly with their doctor.