Personalized Depression Treatment
For a lot of people suffering from hormonal depression treatment, traditional therapies and medications are not effective. Personalized treatment could be the solution.
Cue is an intervention platform that transforms passively acquired sensor data from smartphones into personalized micro-interventions that improve mental health. We examined the most effective-fitting personalized ML models to each person using Shapley values to discover their features and predictors. The results revealed distinct characteristics that were deterministically changing mood over time.
Predictors of Mood
Depression is one of the most prevalent causes of mental illness.1 However, only about half of those who have the disorder receive treatment1. To improve outcomes, clinicians must be able to identify and treat patients most likely to respond to certain treatments.
A customized depression treatment plan can aid. By using mobile phone sensors, 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 totaling more than $10 million will be used to determine biological and behavioral indicators of response.
The majority of research conducted to date has focused on sociodemographic and clinical characteristics. These include demographics such as gender, age, and education, as well as clinical characteristics such as symptom severity and comorbidities, as well as biological markers.
While many of these variables can be predicted from the information available in medical records, few studies have utilized longitudinal data to explore predictors of mood in individuals. Many studies do not consider the fact that mood can be very different between individuals. Therefore, it is crucial to devise methods that allow for the analysis and measurement of 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. This enables the team to create algorithms that can detect different patterns of behavior and emotions that vary between individuals.
In addition to these modalities, the team created a machine learning algorithm to model the changing variables that influence each person’s mood. The algorithm combines these individual 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, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.
Predictors of Symptoms
Depression is among the most prevalent causes of disability1 yet it is often not properly diagnosed and treated. In addition the absence of effective interventions and stigmatization associated with depressive disorders stop many people from seeking help.
To aid in the development of a personalized treatment plan, identifying patterns that can predict symptoms is essential. The current methods for predicting symptoms rely heavily on clinical interviews, which are unreliable and only reveal a few features associated with depression.
Machine learning can enhance the accuracy of the diagnosis and treatment of 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). These digital phenotypes capture a large number of unique behaviors and activities that are difficult to document through interviews, and allow for continuous, high-resolution measurements.
The study enrolled University of California Los Angeles (UCLA) students experiencing mild 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 sent online for assistance or medical care based on the severity of their depression. Those with a score on the CAT DI of 35 or 65 were assigned to online support via the help of a peer coach. those with a score of 75 patients were referred to psychotherapy in-person.
At baseline, participants provided a series of questions about their personal demographics and psychosocial features. The questions covered age, sex, and education and financial status, marital status, whether they were divorced or not, their current suicidal thoughts, intent or attempts, as well as how often they drank. Participants also scored their level of depression severity on a 0-100 scale using the CAT-DI. The CAT DI assessment was carried out every two weeks for participants who received online support and weekly for those who received in-person assistance.
Predictors of Treatment Response
Personalized depression treatment is currently a top research topic and a lot of studies are aimed at identifying predictors that allow clinicians to identify the most effective medication for each person. In particular, pharmacogenetics identifies genetic variants that influence how the body’s metabolism reacts to antidepressants. This allows doctors select medications that are most likely to work for each patient, reducing the time and effort needed for trial-and error treatments and avoid any negative side consequences.
Another option is to build prediction models that combine clinical data and neural imaging data. These models can be used to determine the best combination of variables predictors of a specific outcome, like whether or not a medication is likely to improve the mood and symptoms. These models can also be used to predict the response of a patient to an existing treatment and help doctors maximize the effectiveness of the current treatment.
A new type of research utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables to improve predictive accuracy. These models have been demonstrated to be effective in predicting the outcome of treatment for example, the response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to become the standard of future treatment.
In addition to the ML-based prediction models research into the mechanisms behind depression treatment without Medication is continuing. Recent research suggests that the disorder is associated with neurodegeneration in particular circuits. This theory suggests that individual depression and treatment treatment will be built around targeted therapies that target these circuits to restore normal function.
One method of doing this is through internet-delivered interventions which can offer an individualized and tailored experience for patients. For instance, one study discovered that a web-based treatment was more effective than standard care in alleviating symptoms and ensuring the best quality of life for those with MDD. Additionally, a randomized controlled trial of a personalized approach to depression treatment showed an improvement in symptoms and fewer adverse effects in a significant number of participants.
Predictors of Side Effects
A major obstacle in individualized herbal depression treatments treatment is predicting the antidepressant medications that will have the least amount of side effects or none at all. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics offers a new and exciting way to select antidepressant drugs that are more effective and precise.
A variety of predictors are available to determine the best antidepressant to prescribe, including genetic variants, phenotypes of patients (e.g., sex or ethnicity) and co-morbidities. To identify the most reliable and valid predictors for a specific treatment, random controlled trials with larger sample sizes will be required. This is because the detection of interactions or moderators could be more difficult in trials that take into account a single episode of treatment per participant instead of multiple sessions of treatment over time.
Additionally, the estimation of a patient’s response to a particular medication will also likely need to incorporate information regarding comorbidities and symptom profiles, in addition to the patient’s previous experience with tolerability and efficacy. Currently, only a few easily identifiable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.
The application of pharmacogenetics in depression treatment is still in its infancy and there are many obstacles to overcome. First, a clear understanding of the genetic mechanisms is needed, as is an understanding of what constitutes a reliable predictor for treatment response. Ethics such as privacy and the responsible use genetic information must also be considered. In the long run pharmacogenetics can offer a chance to lessen the stigma that surrounds mental health care and improve the treatment outcomes for patients with depression. As with any psychiatric approach it is essential to take your time and carefully implement the plan. For now, the best option is to offer patients various effective dementia depression treatment medication options and encourage them to speak with their physicians about their concerns and experiences.