Personalized Depression Treatment
For many people gripped by depression, traditional therapies and medication isn't effective. Personalized treatment may be the solution.
Cue is a digital intervention platform that translates passively acquired normal smartphone sensor data into personalized micro-interventions designed to improve mental health. We examined the most effective-fitting personalized ML models for each individual using Shapley values to discover their features and predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.
Predictors of Mood
Depression is among the most prevalent causes of mental illness.1 However, only half of those suffering from the condition receive treatment1. In order to improve outcomes, clinicians need to be able to identify and treat patients with the highest chance of responding to specific 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 most from specific treatments. They make use of sensors for mobile phones and a voice assistant incorporating artificial intelligence as well as other digital tools. Two grants were awarded that total over $10 million, they will employ these tools to identify the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.
To date, the majority of research on factors that predict depression treatment effectiveness has been focused on the sociodemographic and clinical aspects. These include demographics such as gender, age and education, and clinical characteristics like symptom severity, comorbidities and biological markers.
While many of these variables can be predicted from information in medical records, only a few studies have used longitudinal data to determine predictors of mood in individuals. A few studies also take into consideration the fact that mood can differ significantly between individuals. Therefore, it is important to devise methods that allow for the determination and quantification of the individual differences in mood predictors, treatment effects, etc.
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 can then develop algorithms to detect patterns of behavior and emotions that are unique to each individual.
In addition to these modalities the team also developed a machine-learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these personal differences into a unique "digital phenotype" for each participant.
The digital phenotype was associated with CAT-DI scores, which is a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.
Predictors of symptoms
Depression is one of the most prevalent causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are usually not treated due to the stigma associated with them and the absence of effective treatments.
To assist in individualized treatment, it is crucial to identify predictors of symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which has poor reliability and only detects a small number of symptoms related to depression.2
Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavior phenotypes gathered from smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to are able to capture a variety of unique actions and behaviors that are difficult to capture through interviews, and allow for continuous, high-resolution measurements.
The study included University of California Los Angeles students with moderate to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or medical care depending on the severity of their depression. Participants who scored a high on the CAT-DI of 35 65 were assigned online support by the help of a coach. Those with scores of 75 were routed to clinics in-person for psychotherapy.
At the beginning, participants answered a series of questions about their personal characteristics and psychosocial traits. depression management strategies Iampsychiatry asked included age, sex and education, financial status, marital status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. Participants also scored their level of depression severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI tests were conducted every week for those that received online support, and weekly for those receiving in-person care.
Predictors of the Reaction to Treatment
Research is focused on individualized treatment for depression. Many studies are aimed at identifying predictors, which will help doctors determine the most effective medications to treat each individual. Pharmacogenetics, for instance, uncovers genetic variations that affect how the body's metabolism reacts to drugs. This enables doctors to choose drugs that are likely to be most effective for each patient, minimizing the time and effort involved in trials and errors, while eliminating any side effects that could otherwise hinder the progress of the patient.
Another promising method is to construct models for prediction using multiple data sources, such as the clinical information with neural imaging data. These models can then be used to identify the most effective combination of variables that is predictive of a particular outcome, such as whether or not a particular medication is likely to improve the mood and symptoms. These models can be used to determine the response of a patient to treatment that is already in place, allowing doctors to maximize the effectiveness of the current therapy.
A new generation employs machine learning techniques like the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of multiple variables and improve predictive accuracy. These models have been shown to be useful in predicting the outcome of treatment, such as response to antidepressants. These methods are becoming popular in psychiatry, and it is expected that they will become the standard for the future of clinical practice.
In addition to prediction models based on ML The study of the underlying mechanisms of depression is continuing. Recent findings suggest that depression is connected to the dysfunctions of specific neural networks. This theory suggests that a individualized treatment for depression will be based on targeted therapies that restore normal functioning to these circuits.
Internet-based-based therapies can be an effective method to achieve this. They can offer more customized and personalized experience for patients. One study found that an internet-based program improved symptoms and led to a better quality of life for MDD patients. Additionally, a randomized controlled trial of a personalized approach to treating depression showed sustained improvement and reduced adverse effects in a large proportion of participants.
Predictors of Side Effects
A major challenge in personalized depression treatment involves identifying and predicting the antidepressant medications that will have minimal or no side effects. Many patients have a trial-and error approach, with several medications prescribed until they find one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant medications that is more effective and specific.
There are several variables that can be used to determine which antidepressant should be prescribed, such as gene variations, phenotypes of patients like gender or ethnicity, and the presence of comorbidities. However, identifying the most reliable and reliable factors that can predict the effectiveness of a particular treatment will probably require controlled, randomized trials with considerably larger samples than those that are typically part of clinical trials. This is because the detection of interaction effects or moderators may be much more difficult in trials that only focus on a single instance of treatment per patient, rather than multiple episodes of treatment over a period of time.
Furthermore the prediction of a patient's response will likely require information about comorbidities, symptom profiles and the patient's subjective perception of effectiveness and tolerability. There are currently only a few easily identifiable sociodemographic variables and clinical variables appear to be consistently associated with response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.
The application of pharmacogenetics in depression treatment is still in its infancy, and many challenges remain. It is crucial to have a clear understanding and definition of the genetic mechanisms that underlie depression, and an understanding of an accurate indicator of the response to treatment. In addition, ethical issues, such as privacy and the responsible use of personal genetic information, must be carefully considered. The use of pharmacogenetics may, in the long run, reduce stigma surrounding mental health treatment and improve the quality of treatment. But, like any approach to psychiatry careful consideration and implementation is necessary. In the moment, it's recommended to provide patients with a variety of medications for depression that work and encourage patients to openly talk with their doctors.