Personalized Depression Treatment
For many suffering from depression, traditional therapies and medications are not effective. Personalized
what treatment is there for depression may be the solution.
Cue is an intervention platform for digital devices that converts passively collected smartphone sensor data into personalized micro-interventions designed to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to discover their feature predictors and reveal distinct features that deterministically change mood over time.
Predictors of Mood
Depression is one of the world's leading causes of mental illness.1 Yet, only half of those suffering from the disorder receive treatment1. To improve outcomes, clinicians must be able to identify and treat patients who are the most likely to benefit from certain treatments.
Personalized
depression treatment free treatment can help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit most from specific treatments. They are using sensors for mobile phones and a voice assistant incorporating artificial intelligence as well as other digital tools. Two grants were awarded that total over $10 million, they will make use of these techniques to determine the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.
The majority of research done to so far has focused on sociodemographic and clinical characteristics. These include demographics such as gender, age and education, as well as clinical aspects like severity of symptom and comorbidities, as well as biological markers.
Very few studies have used longitudinal data in order 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 the individual differences in mood predictors and treatments 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 systematically identify different patterns of behavior and emotion that are different between people.
In addition to these modalities, the team created a machine learning algorithm to model the dynamic variables that influence each person's mood. The algorithm blends 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 severity scale for symptom severity. The correlation was low however (Pearson r = 0,08; BH adjusted P-value 3.55 10 03) and varied significantly between individuals.
Predictors of symptoms
Depression is among the leading causes of disability1 but is often not properly diagnosed and treated. Depression disorders are usually not treated because of the stigma attached to them, as well as the lack of effective interventions.
To aid in the development of a personalized treatment, it is important to identify the factors that predict symptoms. However, current prediction methods rely on clinical interview, which has poor reliability and only detects a limited number of features associated with depression.2
Machine learning is used to integrate continuous digital behavioral phenotypes captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of severity of symptoms has the potential to increase the accuracy of diagnostics and treatment efficacy for depression. Digital phenotypes permit continuous, high-resolution measurements as well as capture a variety of distinctive behaviors and activity patterns that are difficult to capture using interviews.
The study involved University of California Los Angeles (UCLA) students who were suffering from moderate to severe depressive symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA
depression and anxiety treatment near me Grand Challenge. Participants were directed to online support or to clinical treatment according to the severity of their depression. Those with a score on the CAT DI of 35 or 65 were assigned online support via a peer coach, while those who scored 75 were routed to in-person clinical care for psychotherapy.
Participants were asked a series questions at the beginning of the study regarding their demographics and psychosocial traits. These included age, sex and education, as well as work and financial situation; whether they were partnered, divorced or single; their current suicidal thoughts, intentions or attempts; as well as the frequency at the frequency they consumed alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale from zero to 100. The CAT-DI test 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
Research is focused on individualized treatment for depression. Many studies are aimed at finding predictors, which can aid clinicians in identifying the most effective medications to treat each individual. Pharmacogenetics in particular uncovers genetic variations that affect how the human body metabolizes drugs. This lets doctors select the medication that will likely work best for every patient, minimizing the time and effort needed for trials and errors, while avoiding any side consequences.
Another approach that is promising is to develop prediction models that combine clinical data and neural imaging data. These models can be used to determine which variables are the most predictive of a specific outcome, such as whether a drug will help with symptoms or mood. These models can be used to predict the response of a patient to treatment, allowing doctors maximize the effectiveness.
A new generation of machines employs machine learning methods such as the supervised and classification algorithms, regularized logistic regression and tree-based methods to integrate the effects of multiple variables and improve predictive accuracy. These models have been demonstrated to be useful in predicting treatment outcomes like the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the norm for the future of clinical practice.
Research into depression's underlying mechanisms continues, in addition to predictive models based on ML. Recent findings suggest that depression is linked to the malfunctions of certain neural networks. This theory suggests that individualized depression treatment will be built around targeted treatments that target these neural circuits to restore normal functioning.
Internet-based-based therapies can be an option to accomplish this. They can provide more customized and personalized experience for patients. For instance, one study discovered that a web-based treatment was more effective than standard care in improving symptoms and providing the best natural treatment for depression (
botdb.win) quality of life for patients suffering from MDD. Furthermore, a randomized controlled study of a personalised
sleep deprivation treatment for depression for depression demonstrated sustained improvement and reduced adverse effects in a significant number of participants.
Predictors of Side Effects
In the treatment of depression, one of the most difficult aspects is predicting and identifying which antidepressant medications will have very little or no side effects.
