Personalized Depression Treatment
Traditional therapy and medication do not work for many people who are depressed. The individual approach to treatment could be the answer.
Cue is an intervention platform that transforms sensor data collected from smartphones into personalised micro-interventions to improve mental health. We analysed the best-fit personalized 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 a major cause of mental illness across the world.1 Yet only half of those suffering from the condition receive treatment. To improve the outcomes, doctors must be able identify and treat patients most likely to respond to specific treatments.
The treatment of depression can be personalized to help. By using sensors for mobile phones, an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to predict which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to determine biological and behavioral indicators of 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 characteristics such as symptom severity, comorbidities and biological markers.
While many of these factors can be predicted from data in medical records, only a few studies have employed longitudinal data to explore predictors of mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is crucial to create methods that allow the determination of the individual differences in mood predictors and the effects of treatment.
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 identify different patterns of behavior and emotion that are different between people.
In addition to these modalities the team developed a machine-learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm integrates the individual characteristics to create a unique "digital genotype" for each participant.
This digital phenotype was correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was not strong (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.
Predictors of symptoms
Depression is the leading reason for disability across the world1, however, it is often untreated and misdiagnosed. Depression disorders are rarely treated because of the stigma associated with them, as well as the lack of effective treatments.
To aid in the development of a personalized treatment, it is important to determine the predictors of symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which has poor reliability and only detects a tiny variety of characteristics that are associated with depression.2
Machine learning can increase the accuracy of diagnosis and treatment for depression 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). Digital phenotypes can provide continuous, high-resolution measurements as well as capture a variety of distinctive behaviors and activity patterns that are difficult to record through interviews.
The study included University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical care according to the severity of their depression. Patients who scored high on the CAT-DI of 35 or 65 students were assigned online support by the help of a coach. preventive measures for depression with scores of 75 patients were referred for in-person psychotherapy.
At the beginning of the interview, participants were asked an array of questions regarding their personal demographics and psychosocial characteristics. The questions included education, age, sex and gender as well as financial status, marital status and whether they were divorced or not, their current suicidal ideas, intent or attempts, and how often they drank. The CAT-DI was used to assess the severity of depression symptoms on a scale ranging from 100 to. The CAT-DI tests were conducted every week for those who received online support and weekly for those receiving in-person support.
Predictors of the Reaction to Treatment
Research is focusing on personalized treatment for depression. Many studies are focused on finding predictors that can help doctors determine the most effective medications to treat each patient. Pharmacogenetics in particular is a method of identifying genetic variations that affect how the body's metabolism reacts to drugs. This enables doctors to choose medications that are likely to be most effective for each patient, while minimizing the time and effort required in trial-and-error procedures and avoid any adverse effects that could otherwise slow progress.
Another promising approach is building prediction models using multiple data sources, such as clinical information and neural imaging data. These models can then be used to identify the most appropriate combination of variables that are predictors of a specific outcome, like whether or not a drug is likely to improve symptoms and mood. These models can be used to predict the response of a patient to a treatment, allowing doctors to maximize the effectiveness.
A new generation employs machine learning techniques such as algorithms for classification and supervised learning, regularized logistic regression and tree-based methods to combine the effects of several variables and improve predictive accuracy. These models have been shown to be effective in predicting the outcome of treatment for example, the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could become the standard of future medical practice.
In addition to prediction models based on ML research into the mechanisms behind depression is continuing. Recent findings suggest that the disorder is connected with neural dysfunctions that affect specific circuits. This theory suggests that individual depression treatment will be based on targeted treatments that target these circuits to restore normal function.
Internet-delivered interventions can be a way to accomplish this. They can offer a more tailored and individualized experience for patients. One study found that a program on the internet was more effective than standard care in alleviating symptoms and ensuring the best quality of life for people with MDD. A controlled study that was randomized to a customized treatment for depression found that a significant percentage of patients saw improvement over time as well as fewer side effects.
Predictors of adverse effects
In the treatment of depression, the biggest challenge is predicting and identifying which antidepressant medications will have no or minimal side negative effects. Many patients take a trial-and-error method, involving several medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics is an exciting new method for an effective and precise method of selecting antidepressant therapies.
There are many variables that can be used to determine the antidepressant to be prescribed, including gene variations, phenotypes of patients such as ethnicity or gender and co-morbidities. However, identifying the most reliable and valid predictive factors for a specific treatment is likely to require controlled, randomized trials with considerably larger samples than those typically enrolled in clinical trials. This is because the identifying of interactions or moderators could be more difficult in trials that only focus on a single instance of treatment per person instead of multiple sessions of treatment over a period of time.
Furthermore, predicting a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's subjective experience of tolerability and effectiveness. Currently, only some easily assessable sociodemographic and clinical variables seem to be reliable in predicting the response to MDD factors, including age, gender, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depressive symptoms.
Many challenges remain in the use of pharmacogenetics for depression treatment. First, a clear understanding of the underlying genetic mechanisms is essential and an understanding of what constitutes a reliable predictor for treatment response. Additionally, ethical issues like privacy and the ethical use of personal genetic information must be carefully considered. Pharmacogenetics could be able to, over the long term reduce stigma associated with mental health treatments and improve the outcomes of treatment. However, as with all approaches to psychiatry, careful consideration and planning is essential. 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 physicians.