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Personalized Depression Treatment

Traditional treatment and medications are not effective for a lot of people who are depressed. The individual approach to electric shock treatment for depression could be the solution.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into personalized micro-interventions 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 characteristics that can be used to predict changes in mood over time.

Predictors of Mood

Depression is among the leading causes of mental illness.1 Yet, only half of those who have the disorder receive treatment1. To improve outcomes, doctors must be able to recognize and treat patients who have the highest likelihood of responding to specific treatments.

A customized depression treatment is one method of doing this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from specific treatments. They are using sensors on mobile phones, a voice assistant with artificial intelligence and other digital tools. With two grants awarded totaling more than $10 million, they will employ these techniques to determine the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

The majority of research on factors that predict depression treatment effectiveness - Scientific Programs explains - has been focused on the sociodemographic and clinical aspects. These include demographics such as gender, age and education as well as clinical aspects such as symptom severity, comorbidities and biological markers.

Very few studies have used longitudinal data in order to determine mood among individuals. Few also take into account the fact that mood can vary significantly between individuals. Therefore, it is important to develop methods that permit the identification and quantification of personal differences between 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 allows the team to create algorithms that can detect various patterns of behavior and emotion that differ between individuals.

The team also created a machine-learning algorithm that can identify dynamic predictors of each person's mood for depression. The algorithm integrates 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 not strong (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of symptoms

Depression is the most common cause of disability in the world1, however, it is often misdiagnosed and untreated2. In addition, a lack of effective interventions and stigmatization associated with depressive disorders prevent many from seeking treatment.

To assist in individualized treatment, it is crucial to determine the predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which are not reliable and only identify a handful of characteristics that are associated with depression.

Machine learning can enhance the accuracy of the diagnosis and treatment of depression by combining continuous digital behavioral phenotypes collected from smartphone sensors with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can be used to capture a large number of unique behaviors and activities that are difficult to record through interviews and permit continuous and high-resolution measurements.

The study included University of California Los Angeles students with mild to severe postpartum depression natural treatment 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 referred to online assistance or medical care depending on the severity of their depression. Patients with a CAT DI score of 35 65 were assigned online support via the help of a coach. Those with scores of 75 patients were referred for psychotherapy in person.

Participants were asked a set of questions at the beginning of the study about their demographics and psychosocial characteristics. These included age, sex education, work, and financial status; if they were divorced, married, or single; current suicidal ideation, intent or attempts; and the frequency with the frequency they consumed alcohol. The CAT-DI was used for assessing the severity of depression-related symptoms on a scale ranging from zero to 100. The CAT-DI test was performed every two weeks for participants 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 focused on finding predictors, which can help doctors determine the most effective drugs for each person. In particular, pharmacogenetics identifies genetic variants that influence the way that the body processes antidepressants. This enables doctors to choose medications that are likely to be most effective for each patient, reducing the time and effort in trials and errors, while avoid any adverse effects that could otherwise slow the progress of the patient.

Another option is to develop prediction models that combine information from clinical studies and neural imaging data. These models can then be used to identify the most appropriate combination of variables that are 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 a treatment they are currently receiving, allowing doctors to maximize the effectiveness of treatment currently being administered.

A new type of research uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of many variables and improve the accuracy of predictive. These models have been proven to be effective in predicting treatment outcomes such as the response to antidepressants. These methods are becoming popular in psychiatry and it is likely that they will become the norm for the future of clinical practice.

Research into depression's underlying mechanisms continues, as do predictive models based on ML. Recent research suggests that the disorder is associated with neural dysfunctions that affect specific circuits. This suggests that an individualized depression treatment will be based on targeted therapies that target these circuits in order to restore normal function.

Internet-based interventions are an effective method to accomplish this. They can offer more customized and personalized experience for patients. One study found that an internet-based program improved symptoms and improved quality life for MDD patients. Furthermore, a randomized controlled study of a personalised approach to depression treatment showed an improvement in symptoms and fewer side effects in a significant percentage of participants.

Predictors of adverse effects

In the treatment of depression one of the most difficult aspects is predicting and identifying the antidepressant that will cause minimal or zero negative side negative effects. Many patients are prescribed a variety medications before finding a medication that is safe and effective. Pharmacogenetics offers a fresh and exciting method of selecting antidepressant medicines that are more efficient and targeted.

There are a variety of predictors that can be used to determine the antidepressant to be prescribed, including gene variations, phenotypes of the patient such as ethnicity or gender, and co-morbidities. To determine the most reliable and valid predictors for a particular treatment, random controlled trials with larger numbers of participants will be required. This is because it may be more difficult to identify interactions or moderators in trials that contain only one episode per person rather than multiple episodes over a long period of time.

Additionally to that, predicting a patient's reaction will likely require information on comorbidities, symptom profiles and the patient's own perception of effectiveness and tolerability. Presently, only a handful of easily identifiable sociodemographic and clinical variables are believed to be reliably associated with response to MDD, such as gender, age, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depression symptoms.

Many challenges remain in the use of pharmacogenetics to treat depression. First it is necessary to have a clear understanding of the underlying genetic mechanisms is needed, as is an understanding of what is the best treatment for anxiety and depression is a reliable predictor of treatment response. Ethics like privacy, and the responsible use genetic information must also be considered. Pharmacogenetics could, in the long run reduce stigma associated with treatments for mental illness and improve treatment outcomes. But, like any other psychiatric treatment, careful consideration and application is necessary. At present, the most effective method is to offer patients an array of effective medications for depression and encourage them to speak openly with their doctors about their concerns and experiences.