Personalized Depression Treatment
Traditional therapy and medication don't work for a majority of patients suffering from depression. Personalized treatment could be the solution.
Cue is a digital intervention platform that translates passively acquired normal sensor data from smartphones into personalised micro-interventions to improve mental health. We looked at the best-fitting personal ML models for each individual, using Shapley values to determine their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.
Predictors of Mood
Depression is one of the world's leading causes of mental illness.1 However, only about half of those suffering from the condition receive treatment1. To improve outcomes, clinicians must be able identify and treat patients most likely to benefit from certain treatments.
Personalized depression treatment is one way to do this. Using mobile phone sensors as well as an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to predict which patients will benefit from the treatments they receive. Two grants worth more than $10 million will be used to identify biological and behavioral predictors of response.
The majority of research on predictors for depression treatment effectiveness has focused on sociodemographic and clinical characteristics. These include demographics such as age, gender and education, as well as clinical characteristics like symptom severity and comorbidities as well as biological markers.
Very few studies have used longitudinal data to predict mood of individuals. Few studies also take into account the fact that moods can differ significantly between individuals. Therefore, it is essential to develop methods that permit the identification 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 will then create algorithms to recognize patterns of behaviour and emotions that are unique to each person.
In addition to these modalities, the team also developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm blends these individual characteristics into a distinctive "digital phenotype" for each participant.
This digital phenotype has been correlated with CAT DI scores which is a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely across individuals.
Predictors of symptoms
Depression is among the leading causes of disability1 but is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigma associated with depression disorders hinder many individuals from seeking help.
To aid in the development of a personalized treatment, it is crucial to determine the predictors of symptoms. 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 integrate continuous digital behavioral phenotypes captured through smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) along with other indicators of severity of symptoms has the potential to increase the accuracy of diagnostics and treatment efficacy for depression. Digital phenotypes are able to provide a wide range of unique behaviors and activities, which are difficult to record through interviews, and allow for continuous, high-resolution measurements.
The study included University of California Los Angeles (UCLA) students experiencing mild 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 directed to online support or clinical care according to the degree of their depression. Patients with a CAT DI score of 35 65 students were assigned online support by a coach and those with a score 75 patients were referred to in-person psychotherapy.
Participants were asked a series of questions at the beginning of the study about their demographics and psychosocial traits. The questions asked included age, sex, and education, marital status, financial status as well as whether they divorced or not, their current suicidal ideas, intent or attempts, as well as the frequency with which they consumed alcohol. depression treatment modalities -DI was used for assessing the severity of depression symptoms on a scale of zero to 100. The CAT DI assessment was conducted every two weeks for participants who received online support and weekly for those who received in-person care.
Predictors of Treatment Response
Personalized depression treatment is currently a major research area, and many studies aim at identifying predictors that will allow clinicians to identify the most effective drugs for each individual. Particularly, pharmacogenetics can identify genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors select medications that are most likely to work for each patient, while minimizing the amount of time and effort required for trial-and error treatments and avoid any negative side negative effects.
Another approach that is promising is to build predictive models that incorporate the clinical data with neural imaging data. These models can be used to determine which variables are most likely to predict a specific outcome, such as whether a drug will improve mood or symptoms. These models can be used to determine the response of a patient to a treatment, which will help doctors maximize the effectiveness.
A new era of research uses machine learning methods like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be useful for forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming more popular in psychiatry, and are likely to be the norm in future medical practice.
In addition to the ML-based prediction models research into the underlying mechanisms of depression continues. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This theory suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.
Internet-based-based therapies can be a way to accomplish this. They can offer a more tailored and individualized experience for patients. One study found that an internet-based program improved symptoms and led to a better quality life for MDD patients. In addition, a controlled randomized study of a personalised approach to treating depression showed sustained improvement and reduced adverse effects in a significant number of participants.
Predictors of Side Effects
A major obstacle in individualized depression treatment involves identifying and predicting the antidepressant medications that will have very little or no side effects. Many patients experience a trial-and-error approach, with several medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting method to choose antidepressant medicines that are more effective and specific.
Many predictors can be used to determine which antidepressant to prescribe, including genetic variants, patient phenotypes (e.g., sex or ethnicity) and the presence of comorbidities. However it is difficult to determine the most reliable and valid predictors for a particular treatment will probably require randomized controlled trials of considerably larger samples than those normally enrolled in clinical trials. This is because it could be more difficult to detect moderators or interactions in trials that contain only one episode per person instead of multiple episodes over time.
Furthermore, the prediction of a patient's response to a specific medication will also likely require information on symptoms and comorbidities as well as the patient's personal experience with tolerability and efficacy. Currently, only some easily measurable sociodemographic and clinical variables are believed to be reliable in predicting response to MDD like age, gender, race/ethnicity and SES, BMI, the presence of alexithymia and the severity of depressive symptoms.
There are many challenges to overcome in the use of pharmacogenetics in the treatment of depression. First, it is essential to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, as well as an accurate definition of a reliable indicator of the response to treatment. Ethics such as privacy and the ethical use of genetic information are also important to consider. Pharmacogenetics could be able to, over the long term reduce stigma associated with mental health treatments and improve the quality of treatment. As with any psychiatric approach, it is important to take your time and carefully implement the plan. For now, the best course of action is to offer patients an array of effective depression medications and encourage them to speak with their physicians about their experiences and concerns.