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Personalized Depression Treatment

Traditional treatment and medications don't work for a majority of patients suffering from depression. Personalized treatment could be the answer.

Cue is an intervention platform that converts passively acquired sensor data from smartphones into customized micro-interventions for improving mental health. We analyzed the best-fitting personalized ML models for each individual using Shapley values, in order to understand their features and predictors. This revealed distinct features that changed mood in a predictable manner over time.

Predictors of Mood

depression treatment diet is the leading cause of mental illness around the world.1 Yet the majority of people affected receive cbt treatment for depression. To improve the outcomes, clinicians need to be able to identify and treat patients with the highest chance of responding to particular treatments.

Personalized depression treatment can help. Utilizing sensors on mobile phones 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 which treatments. Two grants worth more than $10 million will be used to identify the biological and behavioral predictors of response.

The majority of research done to date has focused on sociodemographic and clinical characteristics. These include demographic factors like age, sex and education, clinical characteristics such as symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

While many of these factors can be predicted by the information in medical records, very few studies have utilized longitudinal data to explore predictors of mood in individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is crucial to develop methods that allow for the recognition of the 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 distinct patterns of behavior and emotion that are different between people.

The team also created a machine learning algorithm to create dynamic predictors for each person's mood for depression treatment without meds. The algorithm integrates the individual differences to create an individual "digital genotype" for each participant.

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

Predictors of symptoms

Depression is one of the world's leading causes of disability1 but is often underdiagnosed and undertreated2. Depressive disorders are often not treated due to the stigma attached to them, as well as the lack of effective interventions.

To allow for individualized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. However, the current methods for predicting symptoms rely on clinical interview, which is unreliable and only detects a limited variety of characteristics that are associated with depression.2

Machine learning can increase the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior patterns gathered from sensors on smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements. They also capture a wide range of distinct behaviors and patterns that are difficult to record using interviews.

The study involved University of California Los Angeles (UCLA) students who were suffering from moderate to severe depressive symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were referred to online support or to clinical treatment based on the severity of their depression. Participants with a CAT-DI score of 35 or 65 were assigned online support via an instructor and those with scores of 75 patients were referred to psychotherapy in person.

At the beginning of the interview, participants were asked a series of questions about their personal characteristics and psychosocial traits. The questions asked included age, sex, and education and marital status, financial status, whether they were divorced or not, current suicidal thoughts, intent or attempts, as well as how treat anxiety and depression often they drank. The CAT-DI was used to assess the severity of depression symptoms on a scale from 100 to. CAT-DI assessments were conducted every week for those who received online support and every week for those who received in-person care.

Predictors of Treatment Reaction

The development of a personalized depression treatment is currently a research priority and a lot of studies are aimed to identify predictors that help clinicians determine the most effective drugs for each person. Pharmacogenetics, for instance, uncovers genetic variations that affect the way that our bodies process drugs. This allows doctors to select drugs that are likely to work best for each patient, minimizing the time and effort involved in trial-and-error treatments and avoiding side effects that might otherwise hinder advancement.

Another promising approach is to build prediction models combining clinical data and neural imaging data. These models can be used to determine the most appropriate combination of variables predictors of a specific outcome, such as whether or not a medication will improve symptoms and mood. These models can also be used to predict the response of a patient to treatment that is already in place, allowing doctors to maximize the effectiveness of their current treatment.

A new generation of machines employs machine learning techniques like supervised and classification algorithms, regularized logistic regression and tree-based methods to combine the effects of multiple variables and increase the accuracy of predictions. These models have been demonstrated to be useful in predicting treatment outcomes, such as response to antidepressants. These approaches are becoming more popular in psychiatry and will likely be the norm in future medical practice.

In addition to the ML-based prediction models research into the mechanisms that cause depression continues. Recent research suggests that the disorder is associated with neural dysfunctions that affect specific circuits. This theory suggests that individualized depression treatment will be based on targeted therapies that target these circuits in order to restore normal function.

Internet-based interventions are an option to accomplish this. They can offer an individualized and tailored 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. A controlled, randomized study of a personalized treatment for depression found that a substantial percentage of participants experienced sustained improvement and had fewer adverse negative effects.

Predictors of side effects

In the treatment of depression one of the most difficult aspects is predicting and identifying which antidepressant medication will have very little or no adverse negative effects. Many patients have a trial-and error method, involving a variety of medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a new and exciting method to choose antidepressant medications that is more effective and specific.

Many predictors can be used to determine which antidepressant is best to prescribe, including genetic variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. 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 normally enrolled in clinical trials. 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 person rather than multiple episodes over a period of time.

Additionally the estimation of a patient's response to a specific medication is likely to require information about comorbidities and symptom profiles, and the patient's prior subjective experience of its tolerability and effectiveness. There are currently only a few easily assessable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

Many issues remain to be resolved in the application of pharmacogenetics in the treatment of depression. First, it is important to have a clear understanding and definition of the genetic factors that cause depression, as well as a clear definition of a reliable indicator of the response to treatment. Additionally, ethical issues such as privacy and the responsible use of personal genetic information, should be considered with care. In the long term, pharmacogenetics may offer a chance to lessen the stigma associated with mental health care and improve the outcomes of those suffering with depression. As with any psychiatric approach it is essential to carefully consider and implement the plan. In the moment, it's best to offer patients a variety of medications for depression treatment brain stimulation that are effective and encourage them to speak openly with their doctors.