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Personalized Depression Treatment

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

Cue is an intervention platform that converts sensors that are passively gathered from smartphones into personalized micro-interventions to improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their feature predictors and uncover distinct features that are able to change mood as time passes.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 Yet, only half of people suffering from the condition receive treatment1. To improve the outcomes, clinicians need to be able to identify and treat patients with the highest chance of responding to certain treatments.

A customized depression treatment is one method to achieve this. Utilizing mobile phone sensors as well as an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from the treatments they receive. Two grants totaling more than $10 million will be used to discover the biological and behavioral predictors of response.

The majority of research done to so far has focused on sociodemographic and clinical characteristics. These include demographics such as gender, age 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 the information available in medical records, very few studies have employed longitudinal data to study the factors that influence mood in people. Many studies do not take into consideration the fact that mood can be very different between individuals. Therefore, it is important to develop methods which permit the determination and quantification of the individual differences between mood predictors, treatment effects, etc.

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 create algorithms that can systematically identify different 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 predictors of each person's depressed mood. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.

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

Predictors of symptoms

Depression is among the leading causes of disability1, but it is often untreated and not diagnosed. In addition an absence of effective interventions and stigma associated with depressive disorders prevent many individuals from seeking help.

To aid in the development of a personalized treatment, it is important to determine the predictors of symptoms. However, the current methods for predicting symptoms depend on the clinical interview which has poor reliability and only detects a small number of symptoms that are associated with depression.2

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can be used to provide a wide range of distinct actions and behaviors that are difficult to capture through interviews, and also allow for continuous and high-resolution measurements.

The study involved University of California Los Angeles (UCLA) students with mild to severe depression symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were routed to online assistance or in-person clinics according to the severity of their depression. Participants with a CAT-DI score of 35 or 65 were assigned online support via the help of a coach. Those with scores of 75 patients were referred to psychotherapy in-person.

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial characteristics. The questions asked included age, sex and education as well as marital status, financial status, whether they were divorced or not, the frequency of suicidal ideas, intent or attempts, and how often they drank. The CAT-DI was used to assess the severity of depression symptoms on a scale from zero to 100. The CAT-DI tests were conducted every week for those who received online support and once a week for those receiving in-person care.

Predictors of the Reaction to Treatment

Research is focusing on personalized treatment for depression. Many studies are focused on finding predictors that can aid clinicians in identifying the most effective drugs to treat each patient. Pharmacogenetics in particular identifies genetic variations that determine how treat anxiety and depression the body's metabolism reacts to drugs. This allows doctors to select the medications that are most likely to work best medication to treat anxiety and depression for each patient, reducing the time and effort in trial-and-error treatments and avoiding side effects that might otherwise hinder the progress of the patient.

Another approach that is promising is to create prediction models that combine the clinical data with neural imaging data. These models can then be used to identify the most appropriate combination of variables that is predictive of a particular outcome, like whether or not a particular medication is likely to improve the mood and symptoms. These models can be used Meds To Treat Depression determine the patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of their current therapy.

A new generation of studies uses machine learning methods like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be effective in predicting outcomes of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry and will likely become the norm in the future clinical practice.

In addition to prediction models based on ML, research into the mechanisms behind depression continues. Recent findings suggest that the disorder is associated with neurodegeneration in particular circuits. This suggests that individual depression treatment refractory depression will be focused on therapies that target these neural circuits to restore normal function.

One method of doing this is to use internet-based interventions that can provide a more individualized and personalized experience for patients. A study showed that an internet-based program helped improve symptoms and led to a better quality of life for MDD patients. Additionally, a randomized controlled study of a customized approach to treating depression showed steady improvement and decreased side effects in a significant percentage of participants.

Predictors of side effects

In the treatment of depression, one of the most difficult aspects is predicting and identifying the antidepressant that will cause very little or no adverse effects. Many patients are prescribed a variety medications before settling on a treatment that is safe and effective. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant drugs that are more effective and specific.

There are a variety of variables that can be used to determine the antidepressant that should be prescribed, including gene variations, phenotypes of patients such as gender or ethnicity, and comorbidities. To determine the most reliable and reliable predictors for a specific treatment, random controlled trials with larger sample sizes will be required. This is due to the fact that it can be more difficult to identify interactions or moderators in trials that only include a single episode per person instead of multiple episodes spread over a long period of time.

In addition the prediction of a patient's response will likely require information on comorbidities, symptom profiles and the patient's subjective perception of effectiveness and tolerability. 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 as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics to depression treatment is still in its infancy and there are many hurdles to overcome. First, it is important to be able to comprehend and understand the definition of the genetic factors that cause depression, as well as a clear definition of an accurate predictor of residential treatment for depression response. Ethics like privacy, and the ethical use of genetic information must also be considered. The use of pharmacogenetics may eventually help reduce stigma around mental health treatments and improve treatment outcomes. However, as with all approaches to psychiatry, careful consideration and implementation is necessary. For now, the best option is to offer patients a variety of effective medications for depression and encourage them to talk openly with their doctors about their experiences and concerns.