Shayla Skeyhill asked 4 hafta ago
Personalized Depression Treatment
Traditional therapies and medications are not effective for a lot of people suffering from depression. A customized treatment could be the solution.
Cue is an intervention platform that converts passively acquired sensor data from smartphones into personalized micro-interventions for improving mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct characteristics that can be used to predict changes in mood with time.
Predictors of Mood
Depression is one of the most prevalent causes of mental illness.1 However, only half of people suffering from the condition receive treatment1. In order to improve outcomes, healthcare professionals must be able to identify and treat patients who have the highest probability of responding to certain treatments.
The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will gain the most from specific treatments. They are using sensors on mobile phones, a voice assistant with artificial intelligence and other digital tools. With two grants totaling over $10 million, they will employ these technologies to identify biological and behavioral predictors of response to antidepressant medications and psychotherapy.
The majority of research conducted to date has focused on clinical and sociodemographic characteristics. These include demographics like age, gender and education as well as clinical characteristics like severity of symptom and comorbidities as well as biological markers.
While many of these factors can be predicted from information available in medical records, only a few studies have used longitudinal data to study predictors of mood in 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 allows the team to create algorithms that can detect various patterns of behavior and emotions that vary between individuals.
The team also developed an algorithm for machine learning to model dynamic predictors for each person’s mood for depression. The algorithm combines the individual characteristics to create a unique “digital genotype” for each participant.
The digital phenotype was 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 one of the most prevalent causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated due to the stigma associated with them and the absence of effective interventions.
To facilitate personalized treatment in order to provide a more personalized holistic treatment for depression, identifying factors that predict the severity of symptoms is crucial. However, the current methods for predicting symptoms are based on the clinical interview, which is unreliable and only detects a small number of symptoms related to depression.2
Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavior phenotypes collected from smartphone sensors with a validated 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 range of distinctive behaviors and activity patterns that are difficult to capture with interviews.
The study included University of California Los Angeles students with mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care depending on the degree of their depression treatment diet. Those with a score on the CAT DI of 35 or 65 were assigned online support by the help of a coach. Those with scores of 75 patients were referred for psychotherapy in-person.
At the beginning, participants answered a series of questions about their personal demographics and psychosocial characteristics. These included age, sex education, work, and financial status; whether they were divorced, married, or single; current suicidal thoughts, intentions or attempts; as well as the frequency with that 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 week for those who received online support and once a week for those receiving in-person support.
Predictors of Treatment Response
Research is focusing on personalized treatment for depression. Many studies are aimed at identifying predictors, which will aid clinicians in identifying the most effective medications to treat each individual. In particular, pharmacogenetics identifies genetic variations that affect how to treatment depression the body metabolizes antidepressants. This allows doctors select medications that are most likely to work for each patient, reducing the amount of time and effort required for trial-and-error treatments and avoiding any side consequences.
Another promising approach is to build predictive models that incorporate the clinical data with neural imaging data. These models can be used to determine which variables are most predictive of a particular outcome, such as whether a medication can improve mood or symptoms. These models can be used to predict the response of a patient to a treatment, allowing doctors to maximize the effectiveness.
A new treatments For depression era of research uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables and improve the accuracy of predictive. These models have been demonstrated to be effective in predicting the outcome of treatment for example, the response to antidepressants. These models are getting more popular in psychiatry and it is expected that they will become the norm for future clinical practice.
Research into depression’s underlying mechanisms continues, as well as ML-based predictive models. Recent research suggests that the disorder is associated with neural dysfunctions that affect specific circuits. This suggests that individualized depression treatment will be built around targeted treatments that target these circuits to restore normal function.
Internet-based interventions are an option to accomplish this. They can offer more customized and personalized experience for patients. For example, one study discovered that a web-based treatment was more effective than standard treatment in improving symptoms and providing the best quality of life for those with MDD. Additionally, a randomized controlled trial of a personalized approach to chronic depression treatment treatment showed an improvement in symptoms and fewer adverse effects in a significant percentage of participants.
Predictors of Side Effects
In the treatment of depression, a major challenge is predicting and determining which antidepressant medications will have no or minimal side effects. Many patients are prescribed various drugs before they find a drug that is safe and effective. Pharmacogenetics offers a new and exciting method of selecting antidepressant medicines that are more efficient and targeted.
A variety of predictors are available to determine which antidepressant is best to prescribe, including gene variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and comorbidities. To identify the most reliable and valid predictors for a specific treatment, controlled trials that are randomized with larger numbers of participants will be required. This is because it could be more difficult to detect the effects of moderators or interactions in trials that only include one episode per person rather than multiple episodes over time.
Furthermore the prediction of a patient’s reaction to a specific medication is likely to need to incorporate information regarding comorbidities and symptom profiles, and the patient’s previous experiences with the effectiveness and tolerability of the medication. Presently, only a handful of easily measurable sociodemographic and clinical variables seem to be reliable in predicting the severity of MDD factors, including gender, age, race/ethnicity and SES, BMI, the presence of alexithymia, and the severity of depression symptoms.
The application of pharmacogenetics in inpatient depression treatment centers treatment is still in its infancy and there are many obstacles to overcome. First is a thorough understanding of the genetic mechanisms is essential, as is a clear definition of what is a reliable indicator of treatment response. Additionally, ethical issues such as privacy and the ethical use of personal genetic information must be carefully considered. Pharmacogenetics could eventually, reduce stigma surrounding mental health treatment and improve treatment outcomes. As with any psychiatric approach it is essential to give careful consideration and implement the plan. For now, the best course of action is to provide patients with an array of effective depression medication options and encourage them to speak openly with their doctors about their concerns and experiences.