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Personalized Depression Treatment

Traditional therapies and medications don't work for a majority of people who are depressed. Personalized treatment may be the answer.

Cue is an intervention platform for digital devices that transforms passively acquired smartphone sensor data into personalized micro-interventions that improve mental health. We analyzed the best-fitting personalized ML models for each individual, using Shapley values to determine their feature predictors. The results revealed distinct characteristics that deterministically changed mood over time.

Predictors of Mood

Depression is a major cause of mental illness across the world.1 Yet the majority of people with the condition receive treatment. To improve the outcomes, healthcare professionals must be able to identify and treat patients with the highest likelihood of responding to specific treatments.

The ability to tailor depression treatments is one method to achieve this. By using mobile phone sensors, 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 totaling more than $10 million will be used to discover biological and behavior factors that predict response.

To date, the majority of research into predictors of depression treatment effectiveness has been focused on sociodemographic and clinical characteristics. These include demographics such as age, gender and education, as well as clinical aspects such as symptom severity and comorbidities as well as biological markers.

Few studies have used longitudinal data to predict mood in individuals. Few also take into account the fact that mood varies significantly between individuals. Therefore, it is essential to develop methods that permit 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 develop algorithms that can detect different patterns of behavior and emotion that vary between individuals.

In addition to these modalities the team created a machine learning algorithm to model the dynamic factors that determine a person's depressed mood. The algorithm blends the individual differences to produce an individual "digital genotype" for each participant.

This digital phenotype has been linked to CAT DI scores which is a psychometrically validated symptom severity scale. The correlation was weak, however (Pearson r = 0,08, P-value adjusted by BH 3.55 x 10 03) and varied significantly between individuals.

Predictors of symptoms

Depression is a leading reason for disability across the world1, however, it is often misdiagnosed and untreated2. In addition, a lack of effective treatments and stigma associated with antenatal depression treatment (read this blog post from cs.xuxingdianzikeji.com) disorders hinder many from seeking treatment.

To allow for individualized treatment, identifying patterns that can predict symptoms is essential. The current methods for predicting symptoms rely heavily on clinical interviews, which are unreliable and only detect a few features associated with depression.

Machine learning can be used to combine continuous digital behavioral phenotypes of a person captured by sensors on smartphones and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) together with other predictors of symptom severity has the potential to improve the accuracy of diagnosis and treatment efficacy for depression. Digital phenotypes permit continuous, high-resolution measurements and capture a wide range of distinct behaviors and patterns that are difficult to record with interviews.

The study involved University of California Los Angeles students with moderate to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and depression treatment plan program29 developed as part of the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical treatment depending on their depression severity. Participants who scored a high on the CAT-DI of 35 or 65 were allocated online support via the help of a peer coach. those who scored 75 patients were referred to psychotherapy in-person.

Participants were asked a series questions at the beginning of the study regarding their demographics and psychosocial traits. These included sex, age, education, work, and financial status; if they were partnered, divorced or single; their current suicidal ideation, intent or attempts; and the frequency at the frequency they consumed alcohol. Participants also rated their degree of depression severity on a scale of 0-100 using the CAT-DI. CAT-DI assessments were conducted each other week for participants that received online support, and once a week for those receiving in-person care.

Predictors of Treatment Reaction

Research is focusing on personalization of depression treatment. Many studies are aimed at finding predictors, which can help doctors determine the most effective medications to treat each patient. Particularly, pharmacogenetics is able medicine to treat anxiety and depression identify genetic variants that determine the way that the body processes antidepressants. This enables doctors to choose medications that are likely to be most effective for each patient, minimizing the time and effort required in trials and errors, while avoiding side effects that might otherwise slow progress.

Another promising approach is to create prediction models that combine clinical data and neural imaging data. These models can be used to determine the variables that are most likely to predict a specific outcome, such as whether a medication can improve symptoms or mood. These models can also be used to predict the response of a patient to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of their current treatment.

A new generation employs machine learning techniques like supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of several variables to improve the accuracy of predictive. These models have been proven to be useful in predicting the outcome of treatment for example, 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 prediction models based on ML, research into the mechanisms behind depression is continuing. Recent findings suggest that depression is related to the malfunctions of certain neural networks. This theory suggests that individualized depression treatment will be based on targeted therapies that target these circuits to restore normal function.

Internet-based interventions are an effective method to achieve this. They can provide an individualized and tailored experience for patients. A study showed 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 post natal depression treatment found that a significant number of patients experienced sustained improvement as well as fewer side effects.

Predictors of side effects

A major obstacle in individualized depression treatment is predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients have a trial-and error approach, with a variety of medications prescribed before finding one that is safe and effective. Pharmacogenetics provides an exciting new avenue for a more efficient and specific method of selecting antidepressant therapies.

Several predictors may be used to determine which antidepressant is best to prescribe, including genetic variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. However, identifying the most reliable and valid predictors for a particular treatment is likely to require randomized controlled trials with much larger samples than those that are typically part of clinical trials. This is because it may be more difficult to determine interactions or moderators in trials that only include one episode per person instead of multiple episodes spread over time.

Furthermore the prediction of a patient's response to a particular medication will also likely require information about comorbidities and symptom profiles, as well as the patient's prior subjective experience of its tolerability and effectiveness. At present, only a few easily assessable sociodemographic and clinical variables seem to be reliably associated with the severity of MDD factors, including age, gender, race/ethnicity and SES, BMI, the presence of alexithymia, and the severity of depressive symptoms.

The application of pharmacogenetics to treatment for depression is in its early stages and there are many hurdles to overcome. First is a thorough understanding of the underlying genetic mechanisms is essential and an understanding of what treatment for depression is a reliable predictor of treatment response. Additionally, ethical issues such as privacy and the appropriate use of personal genetic information, should be considered with care. In the long run the use of pharmacogenetics could provide an opportunity to reduce the stigma that surrounds mental health treatment and improve treatment outcomes for those struggling with depression. However, as with all approaches to psychiatry, careful consideration and planning is essential. In the moment, it's ideal to offer patients a variety of medications for depression that are effective and encourage them to speak openly with their doctors.