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Personalized Depression Treatment

Traditional treatment and medications don't work for a majority of patients suffering from depression. Personalized treatment could be the answer.

Cue is an intervention platform for digital devices that transforms passively acquired sensor data from smartphones into personalised micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to understand their feature predictors and reveal distinct characteristics that can be used to predict changes in mood with time.

Predictors of Mood

Depression is a leading cause of mental illness across the world.1 Yet the majority of people affected receive treatment. To improve the outcomes, doctors must be able identify and treat patients most likely to benefit from certain treatments.

The treatment of depression can be personalized to help. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit the most from specific treatments. They use sensors for mobile phones, a voice assistant with artificial intelligence as well as other digital tools. Two grants worth more than $10 million will be used to discover the biological and behavioral factors that predict response.

The majority of research to date has focused on clinical and sociodemographic characteristics. These include factors that affect the demographics like age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.

A few studies have utilized longitudinal data to predict mood of individuals. Few studies also take into account the fact that moods can be very different between individuals. Therefore, [Redirect-302] it is important to devise methods that permit the identification and quantification of individual differences between mood predictors treatments, mood predictors, 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. The team will then create algorithms to recognize patterns of behaviour and emotions that are unique to each person.

In addition to these modalities the team also developed a machine-learning algorithm to model the dynamic factors that determine a person's depressed mood. The algorithm combines these personal differences into a unique "digital phenotype" for each participant.

This digital phenotype was correlated with CAT DI scores which is a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.

Predictors of symptoms

Depression is among the leading causes of disability1 but is often underdiagnosed and undertreated2. In addition an absence of effective treatments and stigmatization associated with depressive disorders stop many individuals from seeking help.

To assist in individualized treatment, it is essential to identify predictors of symptoms. However, the methods used to predict symptoms depend on the clinical interview which is not reliable and only detects a tiny number of features related to depression.2

Machine learning can improve the accuracy of the diagnosis and treatment of depression by combining continuous digital behavior phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can provide continuous, high-resolution measurements as well as capture a variety of distinctive behaviors and activity patterns that are difficult to capture with interviews.

The study involved 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, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for assistance or medical care depending on the severity of their depression. Participants with a CAT-DI score of 35 or 65 were assigned online support with an online peer coach, whereas those with a score of 75 patients were referred to in-person psychotherapy.

Participants were asked a series questions at the beginning of the study concerning their demographics and psychosocial traits. These included sex, age education, work, and financial status; if they were partnered, divorced or single; the frequency of suicidal ideas, intent, linked web site or attempts; and the frequency with that they consumed alcohol. Participants also rated their level of depression severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted each other week for the participants that received online support, and once a week for those receiving in-person treatment.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic, and many studies aim at identifying predictors that help clinicians determine the most effective drugs for each person. Pharmacogenetics, in particular, identifies genetic variations that determine how the human body metabolizes drugs. This allows doctors select medications that will likely work best for each patient, while minimizing time and effort spent on trial-and error treatments and avoiding any side effects.

Another option is to create prediction models combining the clinical data with neural imaging data. These models can be used to determine the best treatment for anxiety depression combination of variables predictive of a particular outcome, like whether or not a particular medication is likely to improve symptoms and mood. These models can be used to determine the response of a patient to an existing treatment which allows doctors to maximize the effectiveness of their current treatment.

A new type of research utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of many variables and increase predictive accuracy. These models have been proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These models are getting more popular in psychiatry and it is expected that they will become the standard for the future of clinical practice.

In addition to prediction models based on ML The study of the mechanisms behind depression is continuing. Recent research suggests that extreme depression treatment is connected to the malfunctions of certain neural networks. This suggests that an individual depression treatment residential treatment will be based on targeted treatments that target these circuits in order to restore normal function.

Internet-delivered interventions can be an effective method to achieve this. They can provide an individualized and tailored experience for patients. For instance, one study found that a program on the internet was more effective than standard treatment in improving symptoms and providing an improved quality of life for patients with MDD. A controlled study that was randomized to a personalized treatment for untreatable depression found that a significant percentage of patients saw improvement over time as well as fewer side consequences.

Predictors of Side Effects

In the treatment of depression, the biggest challenge is predicting and identifying which antidepressant medication will have no or minimal negative side effects. Many patients are prescribed a variety of medications before settling on a treatment that is safe and effective. Pharmacogenetics provides an exciting new way to take an efficient and specific approach to choosing antidepressant medications.

There are many variables that can be used to determine the antidepressant to be prescribed, including genetic variations, phenotypes of the patient such as gender or ethnicity, and the presence of comorbidities. However, identifying the most reliable and accurate predictors for a particular treatment is likely to require randomized controlled trials of significantly larger numbers of participants than those typically enrolled in clinical trials. This is because it may be more difficult to identify the effects of moderators or interactions in trials that contain only a single episode per person instead of multiple episodes spread over a long period of time.

Additionally the prediction of a patient's response to a particular medication will likely also require information about the symptom profile and comorbidities, and the patient's previous experience with tolerability and efficacy. Presently, only a handful of easily identifiable sociodemographic and clinical variables appear to be reliably associated with the severity of MDD like gender, age, race/ethnicity and SES BMI, the presence of alexithymia and the severity of depression symptoms.

There are many challenges to overcome in the use of pharmacogenetics in the treatment of depression. First, a clear understanding of the underlying genetic mechanisms is required as well as a clear definition of what constitutes a reliable predictor for treatment response. In addition, ethical issues such as privacy and the ethical use of personal genetic information must be considered carefully. In the long-term the use of pharmacogenetics could be a way to lessen the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. But, like any other psychiatric treatment, careful consideration and planning is required. At present, the most effective option is to offer patients a variety of effective medications for depression and encourage them to talk openly with their doctors about their concerns and experiences.