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Personalized Depression Treatment

Traditional therapies and medications are not effective for a lot of people suffering from depression. The individual approach to lithium treatment for depression could be the solution.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into personalized micro-interventions that improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and uncover distinct features that are able to change mood with time.

Predictors of Mood

Depression is a leading cause of mental illness around the world.1 Yet, only half of those with the condition receive treatment. To improve the outcomes, doctors must be able to identify and treat patients most likely to benefit from certain treatments.

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will gain the most from certain treatments. They use sensors on mobile phones and a voice assistant incorporating artificial intelligence and other digital tools. Two grants totaling more than $10 million will be used to identify biological and behavior predictors of response.

To date, the majority of research on predictors for depression treatment effectiveness has been focused on sociodemographic and clinical characteristics. These include factors that affect the demographics such as age, sex and education, clinical characteristics such as symptoms severity and comorbidities and biological markers like neuroimaging and genetic variation.

While many of these variables can be predicted from the information in medical records, very few studies have utilized longitudinal data to explore the causes of mood among individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to develop methods that permit the identification 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 enables the team to create algorithms that can systematically identify various patterns of behavior and emotion that differ between individuals.

In addition to these methods, the team also developed a machine-learning algorithm to model the dynamic predictors of each person's depressed mood. The algorithm integrates the individual differences to produce an individual "digital genotype" for each participant.

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

Predictors of symptoms

moderate depression treatment is the leading cause of disability in the world1, but it is often misdiagnosed and untreated2. In addition, a lack of effective interventions and stigmatization associated with depressive disorders stop many people from seeking help.

To allow for individualized treatment to improve treatment, identifying the predictors of symptoms is important. The current methods for predicting symptoms rely heavily on clinical interviews, which aren't reliable and only identify a handful of symptoms associated with depression.

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior phenotypes gathered from smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes are able to capture a variety of unique behaviors and activities, which are difficult to document through interviews, and 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, which was developed under the UCLA depression treatments Grand Challenge. Participants were directed to online support or in-person clinical care according to the severity of their depression. Those with a score on the CAT-DI of 35 65 students were assigned online support with a coach and those with scores of 75 were routed to in-person clinics for psychotherapy.

At the beginning, participants answered a series of questions about their personal characteristics and psychosocial traits. These included sex, age, education, work, and financial situation; whether they were partnered, divorced or single; the frequency of suicidal thoughts, intentions or attempts; as well as the frequency with that they consumed alcohol. Participants also rated their degree of depression severity on a 0-100 scale using the CAT-DI. The CAT-DI tests were conducted every week for those who received online support and once a week for those receiving in-person treatment.

Predictors of Treatment Reaction

A customized treatment for depression is currently a research priority and a lot of studies are aimed at identifying predictors that will allow clinicians to identify the most effective drugs for each person. Pharmacogenetics in particular is a method of identifying genetic variations that affect how to treatment depression the body's metabolism reacts to drugs. This allows doctors to select the medications that are most likely to be most effective for each patient, while minimizing the time and effort required in trials and errors, while eliminating any side effects that could otherwise slow advancement.

Another option is to create predictive models that incorporate the clinical data with neural imaging data. These models can be used to identify which variables are the most predictive of a specific outcome, such as whether a medication will help with symptoms or mood. These models can be used to determine the patient's response to a treatment, which will help doctors to maximize the effectiveness of their treatment.

A new generation of studies utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and improve the accuracy of predictive. These models have been demonstrated to be effective in predicting treatment outcomes for example, the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the standard of future treatment.

In addition to ML-based prediction models, research into the underlying mechanisms of depression continues. Recent findings suggest that depression is related to the dysfunctions of specific neural networks. This theory suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.

One method of doing this is to use internet-based interventions that can provide a more individualized and personalized experience for patients. One study found that a web-based program was more effective than standard care in reducing symptoms and ensuring a better quality of life for people suffering from MDD. A controlled, randomized study of a customized treatment for depression found that a significant percentage of participants experienced sustained improvement and fewer side consequences.

Predictors of Side Effects

In the treatment of depression, a major challenge is predicting and determining the antidepressant that will cause no or minimal negative side negative effects. Many patients have a trial-and error approach, using various medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a new and exciting method to choose antidepressant drugs that are more effective and precise.

Several predictors may be used to determine which antidepressant is best to prescribe, including genetic variants, patient phenotypes (e.g. sexual orientation, gender or ethnicity) and co-morbidities. To determine the most reliable and accurate predictors for a particular 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 one episode per participant instead of multiple episodes spread over a long period of time.

Additionally to that, predicting a patient's reaction will likely require information on the comorbidities, symptoms profiles and the patient's personal perception of effectiveness and tolerability. At present, only a handful of easily measurable sociodemographic variables as well as clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

The application of pharmacogenetics to depression treatment is still in its beginning stages and there are many hurdles to overcome. First is a thorough understanding of the genetic mechanisms is essential as well as a clear definition of what is a reliable predictor of treatment response. Ethics, such as privacy, and the responsible use of genetic information must also be considered. In the long run, pharmacogenetics may provide an opportunity to reduce the stigma that surrounds mental health care and improve treatment outcomes for those struggling with depression. But, like any other psychiatric treatment, careful consideration and application is essential. The best course of action is to offer patients a variety of effective depression medication options and encourage them to speak openly with their doctors about their experiences and concerns.