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This Most Common Personalized Depression Treatment Debate Doesn't Have… |
작성일24-10-19 15:58 |
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Personalized Depression Treatment
Traditional treatment and medications are not effective for a lot of patients suffering from depression. The individual approach to treatment could be the answer.
Cue is an intervention platform for digital devices that converts passively collected smartphone sensor data into personalized micro-interventions to improve mental health. We looked at the best-fitting personal ML models to each subject using Shapley values, in order to understand their characteristic predictors. This revealed distinct features that changed mood in a predictable manner over time.
Predictors of Mood
Depression is a leading cause of mental illness across the world.1 Yet only half of those suffering from the condition receive treatment. To improve the outcomes, healthcare professionals must be able to identify and treat patients who have the highest likelihood of responding to certain treatments.
The treatment of depression can be personalized to help. By using sensors on mobile phones and an artificial intelligence voice assistant and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new natural ways to treat depression and anxiety to predict which patients will benefit from the treatments they receive. Two grants worth more than $10 million will be used to identify biological and behavior indicators of response.
To date, the majority of research on factors that predict depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographic factors such as age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted from the data in medical records, very 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 critical to create methods that allow the identification 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 person.
The team also devised a machine learning algorithm to identify dynamic predictors of each person's mood for depression. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.
The digital phenotype was associated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. However, the correlation was weak (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.
Predictors of Symptoms
Depression is the most common cause of disability around the world1, however, it is often not properly diagnosed and treated. Depressive disorders are often not treated due to the stigma attached to them and the absence of effective interventions.
To allow for individualized treatment in order to provide a more personalized treatment, identifying factors that predict the severity of symptoms is crucial. The current methods for predicting symptoms rely heavily on clinical interviews, which are unreliable and only identify a handful of symptoms associated with depression.
Machine learning is used to combine continuous digital behavioral phenotypes that are captured by smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of severity of symptoms has the potential to increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes can be used to capture a large number of unique actions and behaviors that are difficult to capture through interviews and permit continuous and high-resolution measurements.
The study enrolled University of California Los Angeles (UCLA) students experiencing mild to severe depressive symptoms enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care depending on the severity of their depression. Participants who scored a high on the CAT-DI scale of 35 or 65 were assigned to online support with a peer coach, while those who scored 75 patients were referred for psychotherapy in-person.
At the beginning of the interview, participants were asked a series of questions about their personal demographics and psychosocial features. The questions covered education, age, sex and gender and marital status, financial status, whether they were divorced or not, current suicidal thoughts, intentions or attempts, and the frequency with which they consumed alcohol. Participants also rated their degree of depression symptom severity on a scale of 0-100 using the CAT-DI. The CAT-DI tests were conducted every week for those who received online support and once a week for those receiving in-person care.
Predictors of Treatment Response
A customized treatment for depression is currently a top research topic and many studies aim at identifying predictors that enable clinicians to determine the most effective drugs for each person. Pharmacogenetics in particular is a method of identifying genetic variations that affect the way that our bodies process drugs. This allows doctors to select the medications that are most likely to be most effective for each patient, while minimizing the time and effort in trials and errors, while avoid any adverse effects that could otherwise hinder advancement.
Another approach that is promising is to build models of prediction using a variety of data sources, including clinical information and neural imaging data. These models can be used to determine the variables that are most predictive of a particular outcome, like whether a medication will improve symptoms or 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 treatment.
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 many variables and increase predictive accuracy. These models have been demonstrated to be effective in predicting the outcome of treatment, such as response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the norm in the future clinical practice.
In addition to ML-based prediction models, research into the mechanisms that cause depression treatment centers near me is continuing. Recent findings suggest that depression is linked to the dysfunctions of specific neural networks. This suggests that the treatment for depression will be individualized based on targeted treatments that target these neural circuits to restore normal function.
Internet-based-based therapies can be an effective method to achieve this. They can offer an individualized and tailored experience for patients. A study showed that an internet-based program improved symptoms and led to a better quality of life for MDD patients. Furthermore, a randomized controlled trial of a personalized treatment for depression demonstrated an improvement in symptoms and fewer side effects in a significant proportion of participants.
Predictors of Side Effects
In the treatment of depression, the biggest challenge is predicting and identifying which antidepressant medications will have minimal or zero negative side effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics is an exciting new method for an efficient and targeted method of selecting antidepressant therapies.
Several predictors may be used to determine which antidepressant is best to prescribe, such as gene variants, phenotypes of patients (e.g., sex or ethnicity) and the presence of comorbidities. To determine the most reliable and valid 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 determine interactions or moderators in trials that contain only one episode per person rather than multiple episodes over a long period of time.
Furthermore, the prediction of a patient's response to a specific medication will also likely need to incorporate information regarding the symptom profile and comorbidities, and the patient's prior subjective experience of its tolerability and effectiveness. There are currently only a few easily identifiable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.
The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many hurdles to overcome. First is a thorough understanding of the underlying genetic mechanisms is needed as well as a clear definition of what is a reliable predictor of treatment response. Ethics like privacy, and the ethical use of genetic information are also important to consider. In the long run, pharmacogenetics may be a way to lessen the stigma associated with mental health care and improve treatment outcomes for those struggling with depression. Like any other psychiatric ketamine treatment for depression, it is important to take your time and carefully implement the plan. The best course of action is to provide patients with a variety of effective depression medication to treat anxiety and depression options and encourage them to speak with their physicians about their concerns and experiences.
Traditional treatment and medications are not effective for a lot of patients suffering from depression. The individual approach to treatment could be the answer.
Cue is an intervention platform for digital devices that converts passively collected smartphone sensor data into personalized micro-interventions to improve mental health. We looked at the best-fitting personal ML models to each subject using Shapley values, in order to understand their characteristic predictors. This revealed distinct features that changed mood in a predictable manner over time.Predictors of Mood
Depression is a leading cause of mental illness across the world.1 Yet only half of those suffering from the condition receive treatment. To improve the outcomes, healthcare professionals must be able to identify and treat patients who have the highest likelihood of responding to certain treatments.
The treatment of depression can be personalized to help. By using sensors on mobile phones and an artificial intelligence voice assistant and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new natural ways to treat depression and anxiety to predict which patients will benefit from the treatments they receive. Two grants worth more than $10 million will be used to identify biological and behavior indicators of response.
To date, the majority of research on factors that predict depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographic factors such as age, sex and education, clinical characteristics such as the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted from the data in medical records, very 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 critical to create methods that allow the identification 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 person.
The team also devised a machine learning algorithm to identify dynamic predictors of each person's mood for depression. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.
The digital phenotype was associated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. However, the correlation was weak (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.
Predictors of Symptoms
Depression is the most common cause of disability around the world1, however, it is often not properly diagnosed and treated. Depressive disorders are often not treated due to the stigma attached to them and the absence of effective interventions.
To allow for individualized treatment in order to provide a more personalized treatment, identifying factors that predict the severity of symptoms is crucial. The current methods for predicting symptoms rely heavily on clinical interviews, which are unreliable and only identify a handful of symptoms associated with depression.
Machine learning is used to combine continuous digital behavioral phenotypes that are captured by smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory CAT-DI) with other predictors of severity of symptoms has the potential to increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes can be used to capture a large number of unique actions and behaviors that are difficult to capture through interviews and permit continuous and high-resolution measurements.
The study enrolled University of California Los Angeles (UCLA) students experiencing mild to severe depressive symptoms enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were directed to online support or clinical care depending on the severity of their depression. Participants who scored a high on the CAT-DI scale of 35 or 65 were assigned to online support with a peer coach, while those who scored 75 patients were referred for psychotherapy in-person.
At the beginning of the interview, participants were asked a series of questions about their personal demographics and psychosocial features. The questions covered education, age, sex and gender and marital status, financial status, whether they were divorced or not, current suicidal thoughts, intentions or attempts, and the frequency with which they consumed alcohol. Participants also rated their degree of depression symptom severity on a scale of 0-100 using the CAT-DI. The CAT-DI tests were conducted every week for those who received online support and once a week for those receiving in-person care.
Predictors of Treatment Response
A customized treatment for depression is currently a top research topic and many studies aim at identifying predictors that enable clinicians to determine the most effective drugs for each person. Pharmacogenetics in particular is a method of identifying genetic variations that affect the way that our bodies process drugs. This allows doctors to select the medications that are most likely to be most effective for each patient, while minimizing the time and effort in trials and errors, while avoid any adverse effects that could otherwise hinder advancement.
Another approach that is promising is to build models of prediction using a variety of data sources, including clinical information and neural imaging data. These models can be used to determine the variables that are most predictive of a particular outcome, like whether a medication will improve symptoms or 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 treatment.
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 many variables and increase predictive accuracy. These models have been demonstrated to be effective in predicting the outcome of treatment, such as response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the norm in the future clinical practice.
In addition to ML-based prediction models, research into the mechanisms that cause depression treatment centers near me is continuing. Recent findings suggest that depression is linked to the dysfunctions of specific neural networks. This suggests that the treatment for depression will be individualized based on targeted treatments that target these neural circuits to restore normal function.
Internet-based-based therapies can be an effective method to achieve this. They can offer an individualized and tailored experience for patients. A study showed that an internet-based program improved symptoms and led to a better quality of life for MDD patients. Furthermore, a randomized controlled trial of a personalized treatment for depression demonstrated an improvement in symptoms and fewer side effects in a significant proportion of participants.
Predictors of Side Effects
In the treatment of depression, the biggest challenge is predicting and identifying which antidepressant medications will have minimal or zero negative side effects. Many patients are prescribed various medications before finding a medication that is effective and tolerated. Pharmacogenetics is an exciting new method for an efficient and targeted method of selecting antidepressant therapies.
Several predictors may be used to determine which antidepressant is best to prescribe, such as gene variants, phenotypes of patients (e.g., sex or ethnicity) and the presence of comorbidities. To determine the most reliable and valid 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 determine interactions or moderators in trials that contain only one episode per person rather than multiple episodes over a long period of time.
Furthermore, the prediction of a patient's response to a specific medication will also likely need to incorporate information regarding the symptom profile and comorbidities, and the patient's prior subjective experience of its tolerability and effectiveness. There are currently only a few easily identifiable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.
The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many hurdles to overcome. First is a thorough understanding of the underlying genetic mechanisms is needed as well as a clear definition of what is a reliable predictor of treatment response. Ethics like privacy, and the ethical use of genetic information are also important to consider. In the long run, pharmacogenetics may be a way to lessen the stigma associated with mental health care and improve treatment outcomes for those struggling with depression. Like any other psychiatric ketamine treatment for depression, it is important to take your time and carefully implement the plan. The best course of action is to provide patients with a variety of effective depression medication to treat anxiety and depression options and encourage them to speak with their physicians about their concerns and experiences.등록된 댓글이 없습니다.