11 "Faux Pas" That Are Actually OK To Use With Your Personalized Depression Treatment

Personalized Depression Treatment

Traditional therapy and medication do not work for many people who are depressed. The individual approach to treatment could be the solution.

Cue is an intervention platform for digital devices that translates passively acquired normal sensor data from smartphones into personalised micro-interventions designed to improve mental health. We examined the most effective-fitting personalized ML models to each subject using Shapley values to discover their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is one of the leading causes of mental illness.1 However, only about half of those who have the disorder receive treatment1. To improve  cost-effective depression treatment , clinicians need to be able to identify and treat patients with the highest chance of responding to specific treatments.

The ability to tailor depression treatments is one method to achieve this. By using sensors on mobile phones, 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 the treatments they receive. Two grants were awarded that total more than $10 million, they will employ these technologies to identify the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.

To date, the majority of research into predictors of depression treatment effectiveness has centered on sociodemographic and clinical characteristics. These include demographics such as age, gender, and education, as well as clinical characteristics like symptom severity and comorbidities as well as biological markers.

While many of these aspects can be predicted by the information in medical records, only a few studies have utilized longitudinal data to explore the causes of mood among individuals. A few studies also take into consideration the fact that moods can be very different between individuals. Therefore, it is crucial 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. The team can then develop algorithms to recognize patterns of behaviour and emotions that are unique to each individual.

The team also devised an algorithm for machine learning to identify dynamic predictors of each person's mood for depression. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.

This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was low however (Pearson r = 0,08; BH adjusted P-value 3.55 10 03) and varied widely among individuals.

Predictors of symptoms

Depression is one of the leading causes of disability1, but it is often untreated and not diagnosed. Depression disorders are usually not treated because of the stigma associated with them and the absence of effective interventions.

To aid in the development of a personalized treatment plan, identifying factors that predict the severity of symptoms is crucial. However, the methods used to predict symptoms rely on clinical interview, which has poor reliability and only detects a tiny number of symptoms related to depression.2

Machine learning is used to blend continuous digital behavioral phenotypes captured through smartphone sensors and a validated online mental health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) with other predictors of severity of symptoms has the potential to improve the accuracy of diagnosis and the effectiveness of treatment for depression. Digital phenotypes permit continuous, high-resolution measurements. They also capture a variety of distinctive behaviors and activity patterns that are difficult to record through interviews.

The study included University of California Los Angeles students with mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment depending on their depression severity. Participants who scored a high on the CAT-DI scale of 35 or 65 students were assigned online support via an instructor and those with scores of 75 patients were referred to psychotherapy in-person.

Participants were asked a series of questions at the beginning of the study regarding their demographics and psychosocial characteristics. These included age, sex education, work, and financial situation; whether they were partnered, divorced, or single; current suicidal thoughts, intentions or attempts; and the frequency with which they drank alcohol. The CAT-DI was used for assessing the severity of depression-related symptoms on a scale from zero to 100. The CAT DI assessment was performed every two weeks for those who received online support and weekly for those who received in-person care.

Predictors of Treatment Response

Research is focused on individualized depression treatment. Many studies are aimed at identifying predictors, which will aid clinicians in identifying the most effective drugs for each person. Pharmacogenetics, for instance, uncovers genetic variations that affect how the human body metabolizes drugs. This allows doctors select medications that are most likely to work for each patient, while minimizing time and effort spent on trial-and-error treatments and eliminating any adverse effects.

Another option is to create prediction models combining information from clinical studies and neural imaging data. These models can then be used to determine the most appropriate combination of variables that is predictive of a particular outcome, such as whether or not a medication is likely to improve mood and symptoms. These models can be used to determine the response of a patient to treatment, allowing doctors maximize the effectiveness.

A new 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 proven to be useful in predicting treatment outcomes like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to become the norm in the future clinical practice.

Research into the underlying causes of depression continues, as well as ML-based predictive models. Recent findings suggest that the disorder is linked with neural dysfunctions that affect specific circuits. This theory suggests that a individualized treatment for depression will be based upon targeted treatments that restore normal function to these circuits.

Internet-based interventions are a way to accomplish this. They can offer more customized and personalized experience for patients. A study showed that an internet-based program improved symptoms and improved quality life for MDD patients. Additionally, a randomized controlled study of a personalised treatment for depression demonstrated an improvement in symptoms and fewer adverse effects in a large proportion of participants.

Predictors of adverse effects

In the treatment of depression a major challenge is predicting and identifying the antidepressant that will cause minimal or zero negative side negative effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics provides an exciting new avenue for a more efficient and specific approach to selecting antidepressant treatments.

A variety of predictors are available to determine the best antidepressant to prescribe, including genetic variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and comorbidities. To determine the most reliable and reliable predictors of a specific treatment, controlled trials that are randomized with larger numbers of participants will be required. This is because it may be more difficult to identify the effects of moderators or interactions in trials that comprise only a single episode per person instead of multiple episodes over time.

In addition the prediction of a patient's response will likely require information on comorbidities, symptom profiles and the patient's subjective perception of effectiveness and tolerability. Presently, only a handful of easily measurable sociodemographic and clinical variables seem to be correlated with the response to MDD, such as gender, age, race/ethnicity and SES BMI, the presence of alexithymia, and the severity of depressive symptoms.

The application of pharmacogenetics in depression treatment is still in its early stages and there are many hurdles to overcome. It is crucial to be able to comprehend and understand the definition of the genetic factors that cause depression, as well as a clear definition of a reliable indicator of the response to treatment. In addition, ethical issues like privacy and the ethical use of personal genetic information, should be considered with care. In the long-term, pharmacogenetics may provide an opportunity to reduce the stigma associated with mental health treatment and improve the treatment outcomes for patients with depression. However, as with all approaches to psychiatry, careful consideration and application is required. 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.