This Is A Personalized Depression Treatment Success Story You'll Never Be Able To
Personalized Depression Treatment
Traditional treatment and medications do not work for many people suffering from depression. The individual approach to treatment could be the solution.
Cue is an intervention platform that converts sensor data collected from smartphones into personalised micro-interventions to improve mental health. We analyzed the best-fitting personalized ML models for each individual using Shapley values, in order to understand their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.
Predictors of Mood
Depression is a major cause of mental illness in the world.1 Yet only half of those affected receive treatment. To improve the outcomes, doctors must be able to recognize and treat patients most likely to respond to certain treatments.
The ability to tailor depression treatments is one method to achieve this. By 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 were awarded that total over $10 million, they will make use of these techniques to determine biological and behavioral predictors of the response to antidepressant medication and psychotherapy.
The majority of research to date has focused on clinical and sociodemographic characteristics. These include factors that affect the demographics such as age, gender and educational level, clinical characteristics like the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.
Few studies have used longitudinal data to determine mood among individuals. They have not taken into account the fact that mood can vary significantly between individuals. It is therefore important to develop methods which permit the analysis and measurement of individual differences between 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 behaviour and emotions that are unique to each person.
alternative depression treatment Iampsychiatry devised a machine learning algorithm to model dynamic predictors for each person's mood for depression. The algorithm combines these personal characteristics into a distinctive "digital phenotype" for each participant.
This digital phenotype was found to be associated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.
Predictors of symptoms
Depression is among the most prevalent causes of disability1, but it is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigmatization associated with depressive disorders prevent many from seeking treatment.
To facilitate personalized treatment, identifying factors that predict the severity of symptoms is crucial. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only reveal a few characteristics that are associated with depression.
Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes permit continuous, high-resolution measurements as well as capture a wide variety of distinctive behaviors and activity patterns that are difficult to record using interviews.
The study enrolled University of California Los Angeles (UCLA) students with moderate to severe depressive symptoms. enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment depending on their depression severity. Participants with a CAT-DI score of 35 or 65 were assigned to online support via an online peer coach, whereas those with a score of 75 patients were referred for psychotherapy in-person.
At the beginning of the interview, participants were asked a series of questions about their personal demographics and psychosocial characteristics. These included sex, age education, work, and financial status; whether they were divorced, married or single; the frequency of suicidal ideas, intent or attempts; and the frequency with that they consumed alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale ranging from 100 to. The CAT DI assessment was performed every two weeks for participants who received online support, and weekly for those who received in-person care.
Predictors of the Reaction to Treatment
The development of a personalized depression treatment is currently a research priority and many studies aim at identifying predictors that enable clinicians to determine the most effective medication for each person. Particularly, pharmacogenetics can identify genetic variations that affect how the body metabolizes antidepressants. This allows doctors to select the medications that are most likely to be most effective for each patient, minimizing the time and effort required in trials and errors, while avoiding side effects that might otherwise hinder advancement.
Another approach that is promising is to develop predictive models that incorporate information from clinical studies and neural imaging data. These models can then be used to determine the most appropriate combination of variables that are predictive of a particular outcome, such as whether or not a drug will improve symptoms and mood. These models can be used to predict the patient's response to treatment, allowing doctors to maximize the effectiveness of their treatment.
A new era of research uses machine learning methods such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to blend the effects of several variables and improve the accuracy of predictive. These models have been shown to be useful in predicting outcomes of treatment for example, the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the standard of future treatment.
In addition to ML-based prediction models, research into the mechanisms behind depression continues. Recent findings suggest that the disorder is associated with neurodegeneration in particular circuits. This theory suggests that a individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.
One way to do this is through internet-delivered interventions that can provide a more personalized and customized experience for patients. One study found that a web-based program improved symptoms and led to a better quality of life for MDD patients. Additionally, a randomized controlled study of a personalised treatment for depression demonstrated steady improvement and decreased adverse effects in a significant number of participants.
Predictors of adverse effects
A major challenge in personalized depression treatment is predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients experience a trial-and-error method, involving several medications prescribed before finding one that is safe and effective. Pharmacogenetics offers a fresh and exciting way to select antidepressant medications that is more effective and specific.
Many predictors can be used to determine the best antidepressant to prescribe, including genetic variants, patient phenotypes (e.g. gender, sex or ethnicity) and comorbidities. However it is difficult to determine the most reliable and accurate predictive factors for a specific treatment is likely to require controlled, randomized trials with significantly larger numbers of participants than those normally enrolled in clinical trials. This is because the detection of interaction effects or moderators can be a lot more difficult in trials that only focus on a single instance of treatment per patient instead of multiple episodes of treatment over time.
Furthermore, the estimation of a patient's response to a specific medication will likely also need to incorporate information regarding comorbidities and symptom profiles, in addition to the patient's prior subjective experiences with the effectiveness and tolerability of the medication. There are currently only a few easily measurable sociodemographic variables as well as clinical variables appear to be reliably related to response to MDD. These include gender, age, race/ethnicity as well as BMI, SES and the presence of alexithymia.
Many challenges remain in the application of pharmacogenetics for depression treatment. First, it is important to be able to comprehend and understand the definition of the genetic mechanisms that underlie depression, and an understanding of a reliable predictor of treatment response. Ethics, such as privacy, and the responsible use of genetic information are also important to consider. Pharmacogenetics can be able to, over the long term, reduce stigma surrounding treatments for mental illness and improve the quality of treatment. However, as with any other psychiatric treatment, careful consideration and implementation is necessary. For now, the best course of action is to offer patients a variety of effective depression medication options and encourage them to speak with their physicians about their concerns and experiences.