20 Trailblazers Lead The Way In Personalized Depression Treatment

Personalized Depression Treatment

For many suffering from depression, traditional therapy and medication are ineffective. A customized treatment could be the solution.

Cue is a digital intervention platform that translates passively acquired normal sensor data from smartphones into personalised micro-interventions designed to improve mental health. We looked at the best-fitting personal ML models to each subject, using Shapley values to determine their features and predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is one of the leading causes of mental illness.1 Yet, only half of people suffering from the disorder receive treatment1. In order to improve outcomes, clinicians need to be able to identify and treat patients with the highest likelihood of responding to particular treatments.

A customized depression treatment plan can aid. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit the most from certain treatments. They are using sensors for mobile phones, a voice assistant with artificial intelligence and other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavioral indicators of response.

The majority of research conducted to the present has been focused on clinical and sociodemographic characteristics. These include demographics such as gender, age, and education, as well as clinical aspects like symptom severity, comorbidities and biological markers.

A few studies have utilized longitudinal data to predict mood in individuals. A few studies also take into consideration the fact that moods can differ significantly between individuals. Therefore, it is essential to create methods that allow 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. The team is able to develop algorithms to identify patterns of behaviour and emotions that are unique to each individual.

The team also created an algorithm for machine learning to model dynamic predictors for the mood of each person's depression. The algorithm blends these individual differences into a unique "digital phenotype" for each participant.

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

Predictors of Symptoms

Depression is the 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 depressive disorders stop many from seeking treatment.

To facilitate personalized treatment, identifying patterns that can predict symptoms is essential. However, the methods used to predict symptoms depend on the clinical interview which has poor reliability and only detects a tiny number of symptoms associated with depression.2

Machine learning can improve the accuracy of diagnosis and treatment for depression treatment centers 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). These digital phenotypes provide a wide range of distinct actions and behaviors that are difficult to record through interviews, and also allow for continuous, high-resolution measurements.

The study included University of California Los Angeles (UCLA) students with moderate to severe depressive symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were routed to online assistance or in-person clinics in accordance with their severity of depression. Patients with a CAT DI score of 35 65 were assigned to online support with an online peer coach, whereas those with a score of 75 patients were referred for in-person psychotherapy.

At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal characteristics and psychosocial traits. These included sex, age and education, as well as work and financial status; whether they were divorced, partnered, or single; current suicidal ideation, intent, or attempts; and the frequency at that they consumed alcohol. Participants also scored their level of depression severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted every other week for participants that received online support, and weekly for those receiving in-person treatment.

Predictors of Treatment Response

Personalized menopause depression treatment (hyperlink) treatment is currently a major research area and a lot of studies are aimed at identifying predictors that will help clinicians determine the most effective drugs for each patient. Particularly, pharmacogenetics can identify genetic variations that affect how the body metabolizes antidepressants. This allows doctors select medications that will likely work best for each patient, reducing the amount of time and effort required for trial-and-error treatments and eliminating any adverse consequences.

Another approach that is promising is to create predictive models that incorporate information from clinical studies and neural imaging data. These models can be used to identify the variables that are most predictive of a specific outcome, like whether a medication can improve mood or symptoms. These models can be used to determine the response of a patient to a electric shock treatment for depression they are currently receiving which allows doctors to maximize the effectiveness of their current therapy.

A new generation employs machine learning methods such as the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects from multiple variables and improve predictive accuracy. These models have been shown to be useful in predicting treatment outcomes, such as response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could be the norm in future medical practice.

In addition to ML-based prediction models, research into the mechanisms behind depression is continuing. Recent research suggests that depression is related to dysfunctions in specific neural networks. This suggests that an individualized treatment for depression will be based upon targeted treatments that restore normal function to these circuits.

Internet-based-based therapies can be a way to achieve this. They can offer an individualized and tailored experience for patients. For example, one study found that a web-based program was more effective than standard care in reducing symptoms and ensuring the best quality of life for patients with MDD. In addition, a controlled randomized trial of a personalized approach to treating depression showed steady improvement and decreased adverse effects in a large percentage of participants.

Predictors of Side Effects

In the treatment of depression one of the most difficult aspects is predicting and determining the antidepressant that will cause very little or no negative side effects. Many patients are prescribed a variety of drugs before they find a drug that is both effective and well-tolerated. Pharmacogenetics is an exciting new way to take an efficient and targeted approach to selecting antidepressant treatments.

There are many predictors that can be used to determine which antidepressant should be prescribed, including gene variations, patient phenotypes such as gender or ethnicity, and co-morbidities. However it is difficult to determine the most reliable and reliable predictors for a particular treatment is likely to require controlled, randomized trials with significantly larger numbers of participants than those typically enrolled in clinical trials. This is because it may be more difficult to detect moderators or interactions in trials that contain only one episode per participant instead of multiple episodes spread over a period of time.

Furthermore to that, predicting a patient's reaction will likely require information on comorbidities, symptom profiles and the patient's personal perception of the effectiveness and tolerability. Presently, only a handful of easily assessable sociodemographic and clinical variables appear to be reliably associated with the severity of MDD like gender, age race/ethnicity, BMI and the presence of alexithymia, and the severity of depressive symptoms.

Many challenges remain in the application of pharmacogenetics to treat depression. First it is necessary to have a clear understanding of the underlying genetic mechanisms is needed and an understanding of what is a reliable indicator of treatment response. Additionally, ethical issues like privacy and the ethical use of personal genetic information, should be considered with care. In the long term pharmacogenetics can provide an opportunity to reduce the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. But, like any approach to psychiatry careful consideration and planning is essential. At present, it's best to offer patients various depression medications that are effective and encourage them to talk openly with their doctor.