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Personalized Depression Treatment

For a lot of people suffering from depression, traditional therapies and medication isn't effective. A customized treatment could be the solution.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to discover their feature predictors and reveal distinct characteristics that can be used to predict changes in mood as time passes.

Predictors of Mood

Depression is among the most prevalent causes of mental illness.1 However, only about half of those who have the condition receive treatment1. To improve outcomes, doctors must be able to recognize and treat patients who have the highest likelihood of responding to specific treatments.

The treatment of depression can be personalized to help. Utilizing mobile phone sensors as well as an artificial intelligence voice assistant and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new Natural Ways To Treat Depression to predict which patients will benefit from which treatments. Two grants were awarded that total over $10 million, they will make use of these technologies to identify biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.

The majority of research done to the present has been focused on sociodemographic and clinical characteristics. These include factors that affect the demographics like age, sex and educational level, clinical characteristics like symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these factors can be predicted from the data in medical records, few studies have employed longitudinal data to determine predictors of mood in individuals. Many studies do not consider the fact that moods can be very different between individuals. Therefore, it is essential to develop methods that allow for the determination 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 allows the team to create algorithms that can systematically identify different 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 changing predictors of each person's depressed mood. The algorithm blends these individual differences into a unique "digital phenotype" for each participant.

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

Predictors of symptoms

Depression is one of the world's leading causes of disability1 but is often untreated and not diagnosed. Depressive disorders are often not treated because of the stigma that surrounds them and the absence of effective interventions.

To facilitate personalized treatment, identifying predictors of symptoms is important. However, the methods used to predict symptoms depend on the clinical interview which is unreliable and only detects a small number of features that are associated with depression.2

Machine learning is used to combine continuous digital behavioral phenotypes of a person captured by smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) together with other predictors of symptom severity has the potential to increase the accuracy of diagnostics and the effectiveness of treatment for depression. These digital phenotypes provide a wide range of unique behaviors and activities, which are difficult to record through interviews and permit high-resolution, continuous measurements.

The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and situational depression treatment (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were referred to online support or in-person clinical care in accordance with their severity of depression. Those with a score on the CAT-DI of 35 65 were allocated online support via a peer coach, while those with a score of 75 patients were referred to psychotherapy in person.

At baseline, participants provided an array of questions regarding their personal characteristics and psychosocial traits. The questions covered age, sex, and education and financial status, marital status as well as whether they divorced or not, the frequency of suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale of zero to 100. CAT-DI assessments were conducted each week for those who received online support and once a week lithium for treatment resistant depression those receiving in-person care.

Predictors of treatment refractory depression Response

Research is focusing on personalization of depression treatment. Many studies are focused on finding predictors that can help clinicians identify the most effective medications to treat each individual. Pharmacogenetics in particular is a method of identifying genetic variations that affect how the human body metabolizes drugs. This allows doctors to select drugs that are 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 the progress of the patient.

Another option is to develop prediction models that combine the clinical data with neural imaging data. These models can then be used to identify the best combination of variables that is predictors of a specific outcome, such as whether or not a drug will improve mood and symptoms. These models can be used to predict the response of a patient to treatment, allowing doctors to maximize the effectiveness of their treatment.

A new generation of machines employs machine learning techniques such as the supervised and classification algorithms, regularized logistic regression and tree-based methods to combine the effects from multiple variables and improve predictive accuracy. These models have been demonstrated to be useful in predicting treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry, and it is likely that they will become the norm for the future of clinical practice.

Research into the underlying causes of depression continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is connected with neurodegeneration in particular circuits. This theory suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.

Internet-based interventions are an option to achieve this. They can provide a more tailored and individualized experience for patients. A study showed that an internet-based program improved symptoms and led to a better quality life for MDD patients. A randomized controlled study of a customized treatment for depression revealed that a substantial percentage of patients saw improvement over time and fewer side negative effects.

Predictors of Side Effects

In the treatment of depression one of the most difficult aspects is predicting and determining which antidepressant medications will have no or minimal negative side effects. Many patients are prescribed a variety of medications before finding a medication that is effective and tolerated. Pharmacogenetics provides a novel and exciting way to select antidepressant medicines that are more effective and precise.

There are many variables that can be used to determine the antidepressant to be prescribed, such as gene variations, phenotypes of patients such as gender or ethnicity, and the presence of comorbidities. However finding the most reliable and reliable factors that can predict the effectiveness of a particular treatment will probably require randomized controlled trials with much larger samples than those typically enrolled in clinical trials. This is because it could be more difficult to detect the effects of moderators or interactions in trials that only include one episode per participant instead of multiple episodes spread over a period of time.

Furthermore, the prediction of a patient's reaction to a particular medication is likely to require information about the symptom profile and comorbidities, and the patient's previous experiences with the effectiveness and tolerability of the medication. There are currently only a few easily assessable sociodemographic variables and clinical variables appear to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.

There are many challenges to overcome in the application of pharmacogenetics in the treatment of depression. First it is necessary to have a clear understanding of the underlying genetic mechanisms is needed and an understanding of what constitutes a reliable predictor for treatment response. Ethics such as privacy and the ethical use of genetic information should also be considered. In the long-term pharmacogenetics can provide an opportunity to reduce the stigma associated with mental health treatment and to improve the outcomes of those suffering with depression. However, as with any other psychiatric treatment, careful consideration and application is essential. At present, it's recommended to provide patients with an array of depression medications that are effective and urge them to speak openly with their doctors.