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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 treatment could be the solution.

Cue is an intervention platform that converts sensor data collected from smartphones into personalised micro-interventions for improving 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 features that deterministically change mood as time passes.

Predictors of Mood

Depression is one of the leading causes of mental illness.1 Yet, only half of those who have the disorder receive treatment1. To improve the outcomes, doctors must be able to recognize and treat patients who are the most likely to respond to specific treatments.

Personalized depression treatment is one method to achieve this. Using sensors for mobile phones and an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to identify biological and behavior predictors of response.

So far, the majority of research on factors that predict depression treatment effectiveness has been focused on the sociodemographic and clinical aspects. These include factors that affect the demographics such as age, sex and education, clinical characteristics including symptom severity and comorbidities, and biological markers like neuroimaging and genetic variation.

While many of these factors can be predicted from information available in medical records, few studies have employed longitudinal data to study predictors of mood in individuals. Many studies do not take into consideration the fact that moods vary significantly between individuals. Therefore, it is essential to develop methods that allow for the determination of different mood predictors for each person 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 allows the team to develop algorithms that can systematically identify distinct patterns of behavior and emotions that vary between individuals.

The team also devised an algorithm for machine learning to model dynamic predictors for each person's depression mood. The algorithm integrates the individual differences to produce an individual "digital genotype" for each participant.

This digital phenotype was linked to CAT DI scores which is a psychometrically validated symptom severity scale. The correlation was weak however (Pearson r = 0,08; BH adjusted P-value 3.55 x 10 03) and varied greatly between individuals.

Predictors of Symptoms

Depression is among the world's leading causes of disability1 but is often untreated and not diagnosed. In addition an absence of effective treatments and stigmatization associated with depressive disorders stop many people from seeking help.

To facilitate personalized treatment to improve treatment, identifying the patterns that can predict symptoms is essential. However, the methods used to predict symptoms depend on the clinical interview which is not reliable and only detects a limited number of symptoms related to depression.2

Machine learning can be used to integrate continuous digital behavioral phenotypes captured by smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of symptom severity can increase the accuracy of diagnostics and the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements and capture a wide range of distinctive behaviors and activity patterns that are difficult to document with interviews.

The study included University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or in-person clinics in accordance with their severity of postpartum depression natural treatment. Those with a score on the CAT DI of 35 or 65 were assigned to online support via the help of a peer coach. those who scored 75 were routed to in-person clinics for psychotherapy.

Participants were asked a series questions at the beginning of the study concerning their psychosocial and demographic characteristics as well as their socioeconomic status. The questions asked included age, sex and education as well as financial status, marital status and whether they were divorced or not, the frequency of suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used for assessing the severity of depression-related symptoms on a scale ranging from 0-100. The CAT-DI assessment was carried out every two weeks for those who received online support, and weekly for those who received in-person care.

Predictors of Treatment Response

Research is focusing on personalization of depression treatment. Many studies are aimed at identifying predictors, which will help clinicians identify the most effective medications to treat depression each individual. Pharmacogenetics, for instance, identifies genetic variations that determine the way that our bodies process drugs. This lets doctors choose the medications that are likely to be the most effective for each patient, while minimizing the amount of time and effort required for trials and errors, while eliminating any adverse effects.

Another approach that is promising is to build models of prediction using a variety of data sources, including data from clinical studies and neural imaging data. These models can then be used to determine the most appropriate combination of variables predictive of a particular outcome, like whether or not a medication will improve symptoms and mood. These models can also be used to predict the response of a patient to an existing treatment and help doctors maximize the effectiveness of their current therapy.

A new type of research employs machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and increase predictive accuracy. These models have proven to be useful in the prediction of treatment outcomes like the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely be the norm in future treatment.

In addition to the ML-based prediction models research into the mechanisms that cause depression is continuing. Recent research suggests that depression is related to the malfunctions of certain neural networks. This suggests that individual depression treatment will be focused on therapies that target these circuits in order to restore normal function.

One way to do this is to use internet-based interventions that offer a more individualized and tailored experience for patients. One study found that an internet-based program improved symptoms and provided a better quality of life for MDD patients. A controlled, randomized study of an individualized natural treatment depression anxiety for depression showed that a significant percentage of patients saw improvement over time as well as fewer side negative effects.

Predictors of adverse effects

In the treatment resistant anxiety and depression - read review - of depression one of the most difficult aspects is predicting and determining which antidepressant medications will have no or minimal side negative effects. Many patients have a trial-and error approach, with various medications being prescribed before settling on one that is safe and effective. Pharmacogenetics offers a fascinating new method for an efficient and targeted method of selecting antidepressant therapies.

There are a variety of variables that can be used to determine which antidepressant should be prescribed, such as gene variations, phenotypes of patients such as ethnicity or gender, and comorbidities. However finding 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 normally enrolled in clinical trials. This is because the detection of interaction effects or moderators may be much more difficult in trials that only focus on a single instance of treatment per participant instead of multiple episodes of treatment over a period of time.

Additionally, the prediction of a patient's reaction to a specific medication is likely to require information about comorbidities and symptom profiles, and the patient's prior subjective experience with tolerability and efficacy. At present, only a few easily assessable sociodemographic and clinical variables are believed to be reliably associated with the severity of MDD, such as gender, age race/ethnicity, SES BMI, the presence of alexithymia, and the severity of depression symptoms.

The application of pharmacogenetics to treatment for depression is in its infancy and there are many obstacles to overcome. First, it is essential to have a clear understanding and definition of the genetic mechanisms that cause depression, as well as an accurate definition of a reliable predictor of treatment response. Ethics such as privacy and the responsible use of genetic information must also be considered. The use of pharmacogenetics may, in the long run reduce stigma associated with mental health treatments and improve the outcomes of treatment. However, as with any approach to psychiatry careful consideration and implementation is essential. In the moment, it's ideal to offer patients an array of depression medications that are effective and urge patients to openly talk with their doctor.