Personalized Depression Treatment
Traditional treatment and medications are not effective for a lot of people suffering from depression. Personalized treatment may be the answer.
Cue is an intervention platform that converts passively acquired sensor data from smartphones into personalized micro-interventions for improving mental health. We analyzed the best-fitting personalized ML models to each subject, using Shapley values to discover their features and predictors. This revealed distinct features that changed mood in a predictable manner over time.
Predictors of Mood
Depression is one of the leading causes of mental illness.1 Yet, only half of those who have the condition receive treatment1. To improve the outcomes, doctors must be able identify and treat patients who are most likely to respond to certain treatments.
The treatment of depression treatment exercise can be personalized to help. Utilizing sensors on mobile phones and an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from the treatments they receive. Two grants totaling more than $10 million will be used to determine the biological and behavioral indicators of response.
To date, the majority of research into predictors of depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographics such as gender, age and education and clinical characteristics like symptom severity and comorbidities as well as biological markers.
A few studies have utilized longitudinal data to predict mood of individuals. They have not taken into account the fact that mood can vary significantly between individuals. Therefore, it is critical to develop methods that permit the determination of different mood predictors for each person 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 enables the team to develop algorithms that can systematically identify different patterns of behavior and emotions that differ between individuals.
The team also developed a machine learning algorithm to identify dynamic predictors of 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, adjusted BH-adjusted P-value of 3.55 1003) and varied widely among individuals.
Predictors of symptoms
Depression is one of the world’s leading causes of disability1 but is often underdiagnosed and undertreated2. Depression disorders are rarely treated because of the stigma attached to them and the lack of effective treatments.
To aid in the development of a personalized treatment, it is important to identify predictors of symptoms. However, the methods used to predict symptoms are based on the clinical interview, which has poor reliability and only detects a small number of features associated with depression.2
Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can be used to provide a wide range of distinct actions and behaviors that are difficult to capture through interviews, and also allow for continuous, high-resolution measurements.
The study comprised University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care according to the degree of their depression. Patients who scored high on the CAT-DI scale of 35 65 were assigned to online support via the help of a peer coach. those who scored 75 patients were referred to psychotherapy in-person.
Participants were asked a set of questions at the beginning of the study about their demographics and psychosocial traits. The questions asked included age, sex and education as well as marital status, financial status as well as whether they divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used for assessing the severity of depression-related symptoms on a scale ranging from 100 to. The CAT-DI tests were conducted every other week for participants that received online support, and every week for those who received in-person care.
Predictors of the Reaction to Treatment
Research is focused on individualized depression treatment. Many studies are aimed at finding predictors, which can aid clinicians in identifying the most effective drugs to treat each patient. Pharmacogenetics, in particular, identifies genetic variations that determine how the human body metabolizes drugs. This enables doctors to choose medications that are likely to work best antidepressant for treatment resistant depression for each patient, minimizing the time and effort in trial-and-error treatments and avoiding side effects that might otherwise slow advancement.
Another approach that is promising is to create prediction models combining information from clinical studies and neural imaging data. These models can be used to identify which variables are most likely to predict a specific outcome, like whether a medication can improve symptoms or mood. These models can be used to predict the response of a patient to treatment, allowing doctors to maximize the effectiveness.
A new generation employs machine learning techniques like the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of several variables and improve predictive accuracy. These models have been shown to be useful in predicting the outcome of treatment for example, the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the norm in the future clinical practice.
In addition to prediction models based on ML research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This suggests that individual depression treatment will be built around targeted treatments that target these neural circuits to restore normal function.
One method to achieve this is to use internet-based interventions that offer a more individualized and personalized experience for patients. One study found that a web-based program was more effective than standard treatment in reducing symptoms and ensuring an improved quality of life for those suffering from MDD. In addition, a controlled randomized study of a customized treatment for depression demonstrated sustained improvement and reduced side effects in a significant proportion of participants.
Predictors of side effects
In the treatment of depression, a major challenge is predicting and determining the antidepressant that will cause no or minimal adverse negative effects. Many patients have a trial-and error method, involving a variety of medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics offers a fascinating new method for an effective and precise method of selecting antidepressant therapies.
Many predictors can be used to determine the best antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. To determine the most reliable and reliable predictors of a specific treatment, random controlled trials with larger numbers of participants will be required. This is because it could be more difficult to determine the effects of moderators or interactions in trials that contain only one episode per person instead of multiple episodes spread over a period of time.
Furthermore the prediction of a patient’s response to a particular medication will also likely require information on symptoms and comorbidities and the patient’s previous experiences with the effectiveness and tolerability of the medication. Currently, only some easily identifiable sociodemographic and clinical variables are believed to be correlated with the severity of MDD like age, gender race/ethnicity, BMI, the presence of alexithymia and the severity of depressive symptoms.
The application of pharmacogenetics to treatment for depression treatment medicine is in its early stages and there are many hurdles to overcome. First it is necessary to have a clear understanding of the underlying genetic mechanisms is needed as well as an understanding of what is a reliable indicator of treatment response. Ethics, such as privacy, and the responsible use of genetic information should also be considered. Pharmacogenetics can, in the long run help reduce stigma around mental health treatments and improve the quality of treatment. However, as with any approach to psychiatry careful consideration and planning is necessary. At present, the most effective course of action is to offer patients various effective medications for depression and encourage them to talk openly with their doctors about their concerns and experiences.