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Personalized Depression Ketamine treatment for depression

For a lot of people suffering from depression, traditional therapies and medications are not effective. The individual approach to treatment could be the answer.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions that improve mental health. We analyzed the best-fitting personalized ML models for each individual, using Shapley values to determine their characteristic predictors. The results revealed distinct characteristics that deterministically changed mood over time.

Predictors of Mood

Depression is one of the leading causes of mental illness.1 However, only about half of those who have the condition receive treatment1. To improve the outcomes, doctors must be able to recognize and treat patients most likely to respond to specific treatments.

The ability to tailor depression treatments is one way to do this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from specific treatments. They use sensors on mobile phones, a voice assistant with artificial intelligence, and other digital tools. With two grants awarded totaling more than $10 million, they will make use of these tools to identify the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

The majority of research into predictors of depression treatment effectiveness has been focused on clinical and sociodemographic characteristics. These include demographic factors such as age, sex and education, clinical characteristics including symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

While many of these factors can be predicted by the data in medical records, only a few studies have used longitudinal data to study the causes of mood among individuals. Many studies do not consider the fact that moods can be very different between individuals. Therefore, it is crucial to develop methods which allow for the analysis and measurement of 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 will then create algorithms to identify patterns of behaviour and emotions that are unique to each person.

In addition to these modalities, the team developed a machine-learning algorithm to model the dynamic variables that influence each person's mood. The algorithm combines these personal differences into a unique "digital phenotype" for each participant.

This digital phenotype was associated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was low however (Pearson r = 0,08, BH adjusted P-value 3.55 10 03) and varied greatly among individuals.

Predictors of Symptoms

hormonal depression treatment is a leading reason for disability across the world, but it is often misdiagnosed and untreated2. Depression disorders are rarely treated because of the stigma associated with them and the lack of effective treatments.

To facilitate personalized treatment, identifying patterns that can predict symptoms is essential. However, the methods used to predict symptoms rely on clinical interview, which is not reliable and only detects a small number of features that are associated with depression.2

Machine learning can be used to combine continuous digital behavioral phenotypes of a person captured by smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) together with other predictors of symptom severity can improve the accuracy of diagnosis and the effectiveness of treatment for depression. Digital phenotypes are able to provide a wide range of unique behaviors and activities that are difficult to document through interviews, and allow for continuous and high-resolution measurements.

The study enrolled University of California Los Angeles (UCLA) students who were suffering from mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care according to the degree of their depression. Participants who scored a high on the CAT-DI scale of 35 or 65 were allocated online support with a peer coach, while those who scored 75 patients were referred to in-person clinics for psychotherapy.

Participants were asked a set of questions at the beginning of the study concerning their demographics and psychosocial characteristics. The questions asked included age, sex and education, financial status, marital status as well as whether they divorced or not, their current suicidal thoughts, intentions or attempts, as well as how often they drank. Participants also scored their level of depression symptom severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI test was conducted every two weeks for participants who received online support and weekly for those who received in-person assistance.

Predictors of Treatment Response

Research is focused on individualized treatment for depression. Many studies are aimed at identifying predictors, which will aid clinicians in identifying the most effective drugs to treat each individual. In particular, pharmacogenetics identifies genetic variants that influence the way that the body processes antidepressants. This lets doctors select the medication that will likely work best for every patient, minimizing time and effort spent on trial-and error treatments and avoid any negative side consequences.

Another approach that is promising is to build prediction models that combine the clinical data with neural imaging data. These models can then be used to identify the most effective combination of variables that is predictors of a specific outcome, like whether or not a medication will improve symptoms and mood. These models can also be used to predict a patient's response to an existing treatment, allowing doctors to maximize the effectiveness of current therapy.

A new type of research utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables and improve the accuracy of predictive. These models have shown to be useful in predicting treatment outcomes such as the response to antidepressants. These approaches are gaining popularity in psychiatry and it is likely that they will become the norm for future clinical practice.

In addition to the ML-based prediction models research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is linked to the dysfunctions of specific neural networks. This suggests that an individualized treatment for depression will be based on targeted therapies that restore normal functioning to these circuits.

One method of doing this is through internet-delivered interventions that offer a more individualized and personalized experience for patients. For example, one study found that a web-based program was more effective than standard treatment in reducing symptoms and ensuring the best quality of life for people with MDD. In addition, a controlled randomized trial of a personalized treatment for depression demonstrated sustained improvement and reduced adverse effects in a significant proportion of participants.

Predictors of adverse effects

In the treatment of depression one of the most difficult aspects is predicting and identifying which antidepressant medication will have minimal or zero negative side negative effects. Many patients are prescribed a variety of medications before finding a medication that is both effective and well-tolerated. Pharmacogenetics is an exciting new method for an effective and precise method of selecting antidepressant therapies.

Several predictors may be used to determine the best drug to treat anxiety and depression antidepressant to prescribe, including gene variants, phenotypes of patients (e.g. gender, sex or ethnicity) and the presence of comorbidities. However, identifying the most reliable and valid factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials of considerably larger samples than those that are typically part of clinical trials. This is because it could be more difficult to detect interactions or moderators in trials that comprise only a single episode per person rather than multiple episodes over a period of time.

Furthermore the prediction of a patient's response to a specific medication will also likely require information about the symptom profile and comorbidities, in addition to the patient's prior subjective experiences with the effectiveness and tolerability of the medication. At present, only a few easily measurable sociodemographic and clinical variables appear to be reliable in predicting the severity of MDD like age, gender race/ethnicity, SES BMI and the presence of alexithymia and the severity of menopause depression treatment symptoms.

There are many challenges to overcome in the application of pharmacogenetics to treat depression. First, a clear understanding of the genetic mechanisms is essential as well as an understanding of what is a reliable predictor of treatment response. Ethics like privacy, and the ethical use of genetic information should also be considered. In the long term, pharmacogenetics may be a way to lessen the stigma that surrounds mental health treatment and improve the treatment outcomes for patients with depression. As with all psychiatric approaches, it is important to take your time and carefully implement the plan. At present, the most effective option is to offer patients various effective depression medication options and encourage them to talk freely with their doctors about their concerns and experiences.