• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

Personalized Depression Treatment

Traditional treatment and medications do not work for many people who are depressed. A customized treatment may be the answer.

Cue is a digital intervention platform that translates passively acquired normal 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 identify their feature predictors and uncover distinct features that deterministically change mood with time.

Predictors of Mood

Depression is among the world's leading causes of mental illness.1 However, only about half of those suffering from the disorder receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients who are most likely to respond to certain treatments.

Personalized Depression during pregnancy treatment treatment is one method to achieve this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit most from specific alternative treatments for depression. They use sensors on mobile phones and a voice assistant incorporating artificial intelligence and other digital tools. Two grants worth more than $10 million will be used to discover biological and behavioral predictors of response.

The majority of research to so far has focused on sociodemographic and clinical characteristics. These include demographic variables like age, sex and educational level, clinical characteristics like symptom severity and comorbidities, and biological indicators such as neuroimaging and genetic variation.

While many of these variables can be predicted by the information in medical records, few studies have utilized longitudinal data to study the factors that influence mood in people. Few studies also take into consideration the fact that mood can vary significantly between individuals. Therefore, it is essential to develop methods that allow for the recognition of different mood predictors for each person and treatment 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. The team can then develop algorithms to identify patterns of behaviour and emotions that are unique to each individual.

In addition to these methods, the team also developed a machine-learning algorithm that models the dynamic variables that influence each person's mood. The algorithm integrates the individual differences to create a unique "digital genotype" for each participant.

This digital phenotype was found to be associated with CAT DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was weak, however (Pearson r = 0,08, P-value adjusted by BH 3.55 10 03) and varied greatly among individuals.

Predictors of symptoms

Depression is the most common cause of disability around the world, but it is often untreated and misdiagnosed. Depression disorders are usually not treated because of the stigma attached to them, as well as the lack of effective interventions.

To aid in the development of a personalized treatment, it is important to identify predictors of symptoms. However, the current methods for predicting symptoms are based on the clinical interview, which has poor reliability and only detects a limited variety of characteristics that are associated with dementia depression treatment.2

Machine learning can increase the accuracy of the diagnosis and treatment of depression by combining continuous, digital behavioral patterns gathered from sensors on smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to provide a wide range of unique behaviors and activities, which are difficult to record through interviews, and allow for continuous and high-resolution measurements.

The study involved University of California Los Angeles students who had mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA depression treatment brain stimulation Grand Challenge. Participants were sent online for support or to clinical treatment based on the degree of their depression. Those with a score on the CAT-DI scale of 35 65 were assigned online support with the help of a peer coach. those who scored 75 patients were referred to psychotherapy in person.

At the beginning, participants answered the answers to a series of questions concerning their personal characteristics and psychosocial traits. These included age, sex and education, as well as work and financial status; if they were divorced, married, or single; current suicidal thoughts, intentions or attempts; and the frequency with that they consumed alcohol. The CAT-DI was used to assess the severity of depression symptoms on a scale from 0-100. The CAT DI assessment was conducted every two weeks for those who received online support and weekly for those who received in-person care.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic and many studies aim at identifying predictors that allow clinicians to identify the most effective drugs for each individual. Particularly, pharmacogenetics is able to identify genetic variants that influence how the body metabolizes antidepressants. This lets doctors choose the medications that are likely to be the most effective for every patient, minimizing the time and effort needed for trial-and error treatments and avoid any negative side consequences.

Another promising approach is to build predictive models that incorporate clinical data and neural imaging data. These models can be used to determine the variables that are most likely to predict a specific outcome, such as whether a medication will improve mood or symptoms. These models can also be used to predict the patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of current therapy.

A new generation employs machine learning techniques like the supervised and classification algorithms such as regularized logistic regression, and tree-based techniques to combine the effects of multiple variables to improve the accuracy of predictive. These models have been shown to be useful in predicting outcomes of treatment like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could be the norm in future treatment.

In addition to the ML-based prediction models research into the underlying mechanisms of depression is continuing. Recent findings suggest that the disorder is connected with dysfunctions in specific neural circuits. This suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.

Internet-based-based therapies can be a way to achieve this. They can provide a more tailored and individualized experience for patients. One study found that a web-based program was more effective than standard treatment in reducing symptoms and ensuring a better quality of life for people with MDD. A controlled study that was randomized to an individualized treatment for depression found that a significant percentage of participants experienced sustained improvement and fewer side effects.

Predictors of Side Effects

In the treatment of depression, one of the most difficult aspects is predicting and determining the antidepressant that will cause minimal or zero side effects. Many patients experience a trial-and-error method, involving several medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics provides a novel and exciting way to select antidepressant medications that is more effective and specific.

There are many variables that can be used to determine the antidepressant to be prescribed, including genetic variations, patient phenotypes such as gender or ethnicity, and the presence of comorbidities. To determine the most reliable and reliable predictors for a particular treatment, controlled trials that are randomized with larger sample sizes will be required. This is because the detection of interaction effects or moderators could be more difficult in trials that only take into account a single episode of treatment per patient instead of multiple sessions of treatment over a period of time.

Additionally the prediction of a patient's response will likely require information on comorbidities, symptom profiles and the patient's personal perception of the effectiveness and tolerability. Presently, only a handful of easily measurable sociodemographic and clinical variables are believed to be reliably associated with the response to MDD like gender, age, race/ethnicity and SES, BMI, the presence of alexithymia, and the severity of depressive symptoms.

There are many challenges to overcome in the application of pharmacogenetics in the treatment of depression. First is a thorough understanding of the genetic mechanisms is needed, as is an understanding of what is a reliable indicator of treatment response. Ethics like privacy, and the responsible use genetic information should also be considered. In the long term pharmacogenetics can be a way to lessen the stigma that surrounds mental health care and improve the outcomes of those suffering with depression. As with any psychiatric approach it is essential to carefully consider and implement the plan. At present, it's ideal to offer patients various depression medications that are effective and encourage them to talk openly with their doctors.