Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medication are ineffective. The individual approach to treatment could be the solution.

Cue is an intervention platform that converts passively acquired sensor data from smartphones into customized micro-interventions for improving mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to understand their predictors of feature and reveal distinct features that are able to change mood as time passes.

Predictors of Mood

Depression is a leading cause of mental illness across the world.1 Yet only half of those affected receive treatment. To improve the outcomes, doctors must be able to identify and treat patients who have the highest likelihood of responding to certain treatments.

A customized depression treatment plan can aid. Using sensors on mobile phones and an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to predict which patients will benefit from which treatments. With two grants totaling more than $10 million, they will use these tools to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.

The majority of research into predictors of depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographics like age, gender, and education, as well as clinical characteristics like symptom severity and comorbidities, as well as biological markers.

While many of these variables can be predicted by the information in medical records, very few studies have used 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 allow for the determination and quantification of the individual differences in 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 enables the team to develop algorithms that can detect various patterns of behavior and emotion that differ between individuals.

In addition to these methods, the team developed a machine-learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm blends the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.

Predictors of symptoms

Depression is a leading cause of disability in the world1, however, it is often not properly diagnosed and treated. In addition the absence of effective interventions and stigma associated with depressive disorders stop many individuals from seeking help.

To assist in individualized treatment, it is essential to identify the factors that predict symptoms. The current prediction methods rely heavily on clinical interviews, which are unreliable and only identify a handful of characteristics that are associated with deep depression treatment.

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements and capture a wide variety of distinct behaviors and patterns that are difficult to document through interviews.

The study involved University of California Los Angeles (UCLA) students with mild to severe depressive symptoms enrolled 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 assistance or medical care based on the severity of their depression. Patients who scored high on the CAT-DI of 35 or 65 were allocated online support with the help of a peer coach. those who scored 75 patients were referred for in-person psychotherapy.

At baseline, participants provided the answers to a series of questions concerning their personal demographics and psychosocial characteristics. The questions asked included education, age, sex and gender and marital status, financial status as well as whether they divorced or not, the frequency of suicidal thoughts, intent or attempts, as well as how often they drank. Participants also rated their degree of depression severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted every other week for the participants that received online support, and weekly for those receiving in-person treatment.

Predictors of Treatment Response

Research is focused on individualized treatment for depression. Many studies are focused on finding predictors, which can help clinicians identify the most effective drugs to treat each patient. Particularly, pharmacogenetics is able to identify genetic variants that determine the way that the body processes antidepressants. This enables doctors to choose drugs that are likely to work best for each patient, reducing the time and effort in trial-and-error procedures and avoiding side effects that might otherwise slow the progress of the patient.

Another approach that is promising is to build models for prediction using multiple data sources, such as the clinical information with neural imaging data. These models can be used to identify the best combination of variables predictive of a particular outcome, like whether or not a medication will improve symptoms and mood. These models can be used to determine the response of a patient to an existing treatment, allowing doctors to maximize the effectiveness of their current natural treatment for depression.

A new era of research uses machine learning methods like 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 been shown to be useful in predicting outcomes of homeopathic treatment for depression for example, the response to antidepressants. These models are getting more popular in psychiatry, and it is expected that they will become the norm for future clinical practice.

In addition to ML-based prediction models The study of the mechanisms behind depression continues. Recent research suggests that the disorder is connected with dysfunctions in specific neural circuits. This theory suggests that the treatment for depression will be individualized built around targeted treatments that target these circuits to restore normal function.

One way to do this is to use internet-based interventions which can offer an personalized and customized experience for patients. One study found that an internet-based program helped improve symptoms and provided a better quality life for MDD patients. A controlled study that was randomized to an individualized treatment for depression showed that a significant percentage of patients experienced sustained improvement and had fewer adverse effects.

Predictors of Side Effects

In the treatment of depression a major challenge is predicting and identifying the antidepressant that will cause minimal or zero side negative effects. Many patients are prescribed various medications before finding a medication that is safe and effective. Pharmacogenetics provides an exciting new avenue for a more effective and precise method of selecting antidepressant therapies.

There are many predictors that can be used to determine which antidepressant should be prescribed, including gene variations, patient phenotypes like gender or ethnicity and the presence of comorbidities. However finding the most reliable and accurate predictors for a particular treatment is likely to require controlled, randomized trials with much larger samples than those normally enrolled in clinical trials. This is due to the fact that it can be more difficult to detect moderators or interactions in trials that comprise only a single episode per person instead of multiple episodes over time.

Furthermore the prediction of a patient's response will likely require information about comorbidities, symptom profiles and the patient's own perception of effectiveness and tolerability. Currently, only a few easily measurable sociodemographic variables as well as clinical variables seem to be consistently associated with response to MDD. These include gender, age, race/ethnicity as well as BMI, SES and the presence of alexithymia.

There are many challenges to overcome in the use of pharmacogenetics to treat depression. First, a clear understanding of the underlying genetic mechanisms is required, as is a clear definition of what is a reliable indicator of treatment response. Ethics like privacy, and the responsible use of genetic information must also be considered. Pharmacogenetics could be able to, over the long term help reduce stigma around treatments for mental illness and improve the outcomes of treatment. However, as with all approaches to psychiatry, careful consideration and implementation is required. In the moment, it's best to offer patients a variety of medications for depression that are effective and encourage patients to openly talk with their doctors.