20 Fun Details About Personalized Depression Treatment
Personalized Depression Treatment
Traditional therapy and medication don't work for a majority of patients suffering from depression. The individual approach to treatment could be the solution.
Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions to improve mental health. We examined the most effective-fitting personalized ML models to each subject, using Shapley values to determine their features and predictors. The results revealed distinct characteristics that were deterministically changing mood over time.
Predictors of Mood
Depression is the leading cause of mental illness around 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 ability to tailor depression treatments is one method to achieve this. Utilizing mobile phone sensors and an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from which treatments. With two grants awarded totaling over $10 million, they will make use of these tools to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.
The majority of research on factors that predict depression treatment effectiveness has focused on clinical and sociodemographic characteristics. These include demographic variables 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.
While many of these factors can be predicted by the information available in medical records, few studies have utilized longitudinal data to explore the causes of mood among individuals. They have not taken into account the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that allow for 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. This enables the team to develop algorithms that can identify different patterns of behavior and emotion that vary between individuals.
In addition to these modalities the team developed a machine-learning algorithm to model the dynamic predictors of each person's depressed mood. The algorithm combines the individual characteristics to create an individual "digital genotype" 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 not strong (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely among individuals.
Predictors of symptoms
post pregnancy depression treatment is a leading reason for disability across the world1, however, it is often misdiagnosed and untreated2. In addition an absence of effective treatments and stigmatization associated with depressive disorders stop many people from seeking help.
To aid in the development of a personalized treatment plan to improve treatment, identifying the patterns that can predict symptoms is essential. The current methods for predicting symptoms rely heavily on clinical interviews, which aren't reliable and only identify a handful of characteristics that are associated with depression.
Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral phenotypes gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes provide a wide range of unique behaviors and activities, which are difficult to record through interviews, and also allow for continuous and high-resolution measurements.
The study included University of California Los Angeles (UCLA) students with mild to severe depressive symptoms enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were referred to online assistance or in-person clinics according to the severity of their depression. Participants who scored a high on the CAT-DI scale of 35 or 65 were assigned online support by the help of a coach. Those with a score 75 were sent to clinics in-person for psychotherapy.
Participants were asked a series of questions at the beginning of the study concerning their demographics and psychosocial traits. The questions included age, sex, and education, financial status, marital status, whether they were divorced or not, their current suicidal ideas, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also scored their level of depression symptom severity on a scale of 0-100 using the CAT-DI. CAT-DI assessments were conducted every other week for the participants that received online support, and once a week for those receiving in-person treatment.
Predictors of Treatment Response
The development of a personalized depression treatment is currently a top research topic and a lot of studies are aimed to identify predictors that enable clinicians to determine the most effective medications for each person. Particularly, pharmacogenetics can identify genetic variants that influence the way that the body processes antidepressants. This enables doctors to choose medications that are likely to be most effective for each patient, reducing the time and effort in trial-and-error procedures and avoid any adverse effects that could otherwise hinder advancement.
Another option is to develop prediction models combining information from clinical studies epilepsy and depression treatment neural imaging data. These models can be used to determine the variables that are most likely to predict a specific outcome, such as whether a drug will help with symptoms or mood. These models can be used to determine a patient's response to an existing treatment and help doctors maximize the effectiveness of current therapy.
A new generation uses machine learning techniques like algorithms for classification and supervised learning such as regularized logistic regression, and tree-based methods to combine the effects of multiple variables and increase the accuracy of predictions. These models have been proven to be useful for predicting treatment outcomes such as the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the norm for future clinical practice.
The study of depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent findings suggest that the disorder is linked with dysfunctions in specific neural circuits. This suggests that individualized depression treatment will be based on targeted therapies that target these circuits to restore normal function.
One method of doing this is through internet-delivered interventions which can offer an personalized and customized experience for patients. For example, one study discovered that a web-based treatment was more effective than standard care in alleviating symptoms and ensuring a better quality of life for patients with MDD. A controlled, randomized study of a customized treatment for depression revealed that a significant percentage of patients experienced sustained improvement and had fewer adverse negative effects.
Predictors of adverse effects
In the treatment of depression a major challenge is predicting and determining which antidepressant medications will have minimal or zero adverse effects. Many patients experience a trial-and-error method, involving a variety of medications prescribed until they find one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting way to select antidepressant medicines that are more effective and specific.
There are several predictors that can be used to determine which antidepressant should be prescribed, including gene variations, phenotypes of patients such as ethnicity or gender and comorbidities. To identify the most reliable and valid predictors for a particular treatment, randomized controlled trials with larger samples will be required. This is due to the fact that the identification of interactions or moderators may be much more difficult in trials that focus on a single instance of treatment per patient instead of multiple sessions of treatment over a period of time.
Furthermore, predicting a patient's response will likely require information on comorbidities, symptom profiles and the patient's personal experience of tolerability and effectiveness. Presently, only a handful of easily measurable sociodemographic and clinical variables are believed to be correlated with the severity of MDD, such as gender, age, race/ethnicity and SES BMI and the presence of alexithymia, and the severity of depressive symptoms.
Many challenges remain when it comes to the use of pharmacogenetics for depression treatment. First, a clear understanding of the genetic mechanisms is required and an understanding of what treatment is there for depression is a reliable predictor of treatment response. Ethics such as privacy and the responsible use genetic information are also important to consider. Pharmacogenetics could, in the long run help reduce stigma around mental health treatment and improve the quality of treatment. But, like any other psychiatric treatment, careful consideration and implementation is essential. The best option is to provide patients with various effective depression treatment without meds medication options and encourage them to speak with their physicians about their experiences and concerns.