Personalized Depression Treatment
Traditional therapy and medication are not effective for a lot of people suffering from
psychotic depression treatment. A customized treatment could be the solution.
Cue is a digital intervention platform that translates passively acquired normal sensor data from smartphones into personalised 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 reveal distinct characteristics that can be used to predict changes in mood over time.
Predictors of Mood
Depression is the leading cause of mental illness in the world.1 Yet, only half of those affected receive treatment. To improve outcomes, clinicians must be able identify and treat patients who are most likely to benefit from certain treatments.
A customized depression treatment is one way to do this. Utilizing mobile phone sensors and an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from which treatments. With two grants totaling more than $10 million, they will use these techniques to determine biological and behavioral predictors of response to antidepressant medications and psychotherapy.
The majority of research into predictors of
depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographics such as age, gender, and education, as well as clinical characteristics like symptom severity and comorbidities as well as biological markers.
A few studies have utilized longitudinal data to predict mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. It is therefore important to develop methods that allow for the determination and quantification of the 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. This allows the team to create algorithms that can detect various patterns of behavior and emotions that are different between people.
In addition to these modalities the team created a machine learning algorithm to model the changing variables that influence each person's mood. The algorithm combines the individual differences to produce a unique "digital genotype" for each participant.
The digital phenotype was associated with CAT-DI scores, a psychometrically validated severity scale for symptom severity. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely among individuals.
Predictors of symptoms
Depression is the most common cause of disability in the world1, however, it is often misdiagnosed and untreated2. In addition the absence of effective interventions and stigma associated with depression disorders hinder many individuals from seeking help.
To assist in individualized treatment, it is essential to identify predictors of symptoms. However, current prediction methods are based on the clinical interview, which is unreliable and only detects a limited variety of characteristics associated with depression.2
Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes gathered from smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to provide a wide range of unique behaviors and activities that are difficult to record through interviews, and also allow for continuous and high-resolution measurements.
The study comprised University of California Los Angeles students with mild to severe depression symptoms who were participating 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 in-person clinical care in accordance with their severity of depression. Patients who scored high on the CAT-DI of 35 or 65 were assigned to online support with an online peer coach, whereas those who scored 75 patients were referred to in-person clinics for psychotherapy.
Participants were asked a series questions at the beginning of the study regarding their demographics and psychosocial characteristics. These included age, sex education, work, and financial status; whether they were partnered, divorced or single; their current suicidal thoughts, intentions, or attempts; and the frequency at which they drank alcohol. The CAT-DI was used to rate the severity of
depression treatment history symptoms on a scale from zero to 100. CAT-DI assessments were conducted every other week for the participants who received online support and every week for those who received in-person treatment.
Predictors of Treatment Reaction
Research is focusing on personalized treatment for depression. Many studies are focused on finding predictors that can help doctors determine the most effective drugs to treat each individual. Pharmacogenetics, in particular, identifies genetic variations that determine how the body's metabolism reacts to drugs. This allows doctors select medications that are likely to be the most effective for every patient, minimizing the amount of time and effort required for trial-and-error treatments and avoid any negative side negative effects.
Another promising method is to construct models of prediction using a variety of data sources, combining data from clinical studies and neural imaging data. These models can be used to identify the most effective combination of variables predictive of a particular outcome, such as whether or not a drug will improve mood and symptoms. These models can be used to predict the patient's response to a treatment, allowing doctors maximize the effectiveness.
A new generation of studies uses machine learning methods such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables and improve the accuracy of predictive. These models have proven to be useful in predicting treatment outcomes such as the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the standard for future clinical practice.
In addition to ML-based prediction models research into the underlying mechanisms of depression is continuing. Recent findings suggest that the disorder is associated with neurodegeneration in particular circuits. This suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.
Internet-based-based therapies can be an option to accomplish this. They can offer a more tailored and individualized experience for patients. One study found that a web-based program improved symptoms and provided a better quality of life for MDD patients. A controlled study that was randomized to a customized treatment for depression showed that a significant percentage of patients experienced sustained improvement and fewer side negative effects.
Predictors of adverse effects
In the treatment of
depression treatment centers near me, one of the most difficult aspects is predicting and identifying which antidepressant medication will have no or minimal negative side effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics is an exciting new avenue for a more efficient and targeted approach to choosing antidepressant medications.
There are several variables that can be used to determine the antidepressant that should be prescribed, including genetic variations, patient phenotypes like gender or ethnicity and the presence of comorbidities. However finding the most reliable and valid factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with considerably larger samples than those that are typically part of clinical trials. This is due to the fact that it can be more difficult to determine the effects of moderators or interactions in trials that contain only one episode per participant instead of multiple episodes over time.
Furthermore, the prediction of a patient's response to a specific medication will also likely require information on symptoms and comorbidities in addition to the patient's previous experiences with the effectiveness and tolerability of the medication. Currently, only some easily identifiable sociodemographic and clinical variables appear to be reliable in predicting the response to MDD like age, gender race/ethnicity BMI and the presence of alexithymia and the severity of depression symptoms.
Many challenges remain in the use of pharmacogenetics in the treatment of depression. First, it is important to be able to comprehend and understand the definition of the genetic factors that cause depression, as well as an accurate definition of a reliable indicator of the response to treatment. In addition, ethical concerns, such as privacy and the responsible use of personal genetic information must be considered carefully. Pharmacogenetics could, in the long run reduce stigma associated with mental health treatments and improve treatment outcomes. However, as with any other psychiatric treatment, careful consideration and application is required. The
best treatment for anxiety depression method is to provide patients with a variety of effective depression medications and encourage them to speak openly with their doctors about their concerns and experiences.
