10 Meetups On Personalized Depression Treatment You Should Attend
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작성자 Vanessa McPhill…
작성일 24-10-18 02:14 조회 6회 댓글 0
작성일 24-10-18 02:14 조회 6회 댓글 0
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Personalized Depression Treatment
For many suffering from depression, traditional therapies and medication isn't effective. A customized treatment may be the solution.
Cue is an intervention platform that converts sensors that are passively gathered from smartphones into customized micro-interventions for improving mental health. We examined the most effective-fitting personalized ML models for each individual using Shapley values, in order to understand their feature predictors. The results revealed distinct characteristics that were deterministically changing mood over time.
Predictors of Mood
Depression is a leading cause of mental illness in the world.1 Yet, only half of those suffering from the condition receive treatment. In order to improve outcomes, healthcare professionals must be able to recognize and treat patients who have the highest probability of responding to particular treatments.
The ability to tailor depression treatments is one method of doing this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from certain treatments. They make use of mobile phone sensors, a voice assistant with artificial intelligence and other digital tools. 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 focused on the sociodemographic and clinical aspects. These include demographic factors such as age, gender and education, clinical characteristics including symptom severity and comorbidities, and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted by the information available in medical records, few studies have utilized longitudinal data to study the factors that influence mood in people. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that permit the determination and quantification of the individual differences between mood predictors and treatment effects, for instance.
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 is able to develop algorithms to recognize patterns of behaviour and emotions that are unique to each person.
In addition to these methods, the team developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm blends the individual characteristics to create an individual "digital genotype" for each participant.
The digital phenotype was associated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was low however (Pearson r = 0,08; BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals.
Predictors of symptoms
Depression is one of the leading causes of disability1, but it is often untreated and not diagnosed. Depression disorders are usually not treated due to the stigma associated with them, as well as the lack of effective interventions.
To assist in individualized treatment, it is essential to identify predictors of symptoms. However, the current methods for predicting symptoms rely on clinical interview, which has poor reliability and only detects a tiny variety of characteristics related to depression.2
Machine learning can increase the accuracy of the diagnosis and treatment of depression treatment centers by combining continuous digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing hormonal depression treatment (Securityholes.science) Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements. They also capture a wide range of distinctive behaviors and activity patterns that are difficult to capture through interviews.
The study included University of California Los Angeles (UCLA) students with mild to severe depression treatment depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care based on the severity of their depression. Patients who scored high on the CAT DI of 35 or 65 were allocated online support via a peer coach, while those with a score of 75 were sent to in-person clinics for psychotherapy.
At baseline, participants provided a series of questions about their personal characteristics and psychosocial traits. The questions covered age, sex and education as well as financial status, marital status as well as whether they divorced or not, the frequency of suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their degree of depression symptom severity on a 0-100 scale using the CAT-DI. The CAT DI assessment was carried out every two weeks for participants who received online support and weekly for those who received in-person care.
Predictors of Treatment Response
Research is focusing on personalization of treatment for depression. Many studies are aimed at finding predictors that can help clinicians identify the most effective drugs for each person. Pharmacogenetics, for instance, uncovers genetic variations that affect how the body's metabolism reacts to 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 trial-and-error treatments and avoid any negative side consequences.
Another option is to build prediction models that combine clinical data and neural imaging data. These models can be used to determine which variables are the most likely to predict a specific outcome, like whether a drug will improve symptoms or mood. These models can also be used to predict the patient's response to treatment that is already in place which allows doctors to maximize the effectiveness of the current therapy.
A new generation of machines employs machine learning techniques like algorithms for classification and supervised learning, regularized logistic regression and tree-based methods to integrate the effects from multiple variables and increase the accuracy of predictions. These models have been shown to be useful in predicting outcomes of treatment, such as response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to become the standard of future medical practice.
In addition to the ML-based prediction models research into the underlying mechanisms of depression continues. Recent findings suggest that depression is linked to the dysfunctions of specific neural networks. This suggests that an individualized treatment for depression will be based on targeted therapies that restore normal function to these circuits.
One method of doing this is by using internet-based programs that offer a more individualized and tailored experience for patients. A study showed that a web-based program improved symptoms and provided a better quality life for MDD patients. A controlled, randomized study of a customized treatment for depression found that a significant number of participants experienced sustained improvement as well as fewer side effects.
Predictors of side effects
In the treatment of depression, a major challenge is predicting and determining which antidepressant medication will have minimal or zero negative side effects. Many patients take a trial-and-error method, involving various medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant medications that is more effective and specific.
A variety of predictors are available to determine the best antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. To determine the most reliable and accurate predictors for a particular treatment, random controlled trials with larger sample sizes will be required. This is because the identifying of interaction effects or moderators can be a lot more difficult in trials that only consider a single episode of treatment per patient instead of multiple sessions of treatment over time.
Additionally, the prediction of a patient's response to a specific medication will also likely require information on comorbidities and symptom profiles, as well as the patient's personal experience with tolerability and efficacy. Currently, only some easily measurable sociodemographic and clinical variables seem to be correlated with the severity of MDD, such as gender, age, race/ethnicity and SES BMI, the presence of alexithymia, and the severity of depressive symptoms.
The application of pharmacogenetics to treatment for situational depression treatment is in its early stages and there are many obstacles to overcome. First it is necessary to have a clear understanding of the genetic mechanisms is needed as well as an understanding of what is a reliable indicator of treatment response. Ethics, such as privacy, and the ethical use of genetic information should also be considered. Pharmacogenetics can, in the long run reduce stigma associated with mental health treatments and improve the outcomes of treatment. As with all psychiatric approaches it is essential to give careful consideration and implement the plan. The best course of action is to provide patients with an array of effective depression medications and encourage them to talk freely with their doctors about their experiences and concerns.
For many suffering from depression, traditional therapies and medication isn't effective. A customized treatment may be the solution.
Cue is an intervention platform that converts sensors that are passively gathered from smartphones into customized micro-interventions for improving mental health. We examined the most effective-fitting personalized ML models for each individual using Shapley values, in order to understand their feature predictors. The results revealed distinct characteristics that were deterministically changing mood over time.
Predictors of Mood
Depression is a leading cause of mental illness in the world.1 Yet, only half of those suffering from the condition receive treatment. In order to improve outcomes, healthcare professionals must be able to recognize and treat patients who have the highest probability of responding to particular treatments.
The ability to tailor depression treatments is one method of doing this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from certain treatments. They make use of mobile phone sensors, a voice assistant with artificial intelligence and other digital tools. 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 focused on the sociodemographic and clinical aspects. These include demographic factors such as age, gender and education, clinical characteristics including symptom severity and comorbidities, and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted by the information available in medical records, few studies have utilized longitudinal data to study the factors that influence mood in people. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that permit the determination and quantification of the individual differences between mood predictors and treatment effects, for instance.
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 is able to develop algorithms to recognize patterns of behaviour and emotions that are unique to each person.
In addition to these methods, the team developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm blends the individual characteristics to create an individual "digital genotype" for each participant.
The digital phenotype was associated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. The correlation was low however (Pearson r = 0,08; BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals.
Predictors of symptoms
Depression is one of the leading causes of disability1, but it is often untreated and not diagnosed. Depression disorders are usually not treated due to the stigma associated with them, as well as the lack of effective interventions.
To assist in individualized treatment, it is essential to identify predictors of symptoms. However, the current methods for predicting symptoms rely on clinical interview, which has poor reliability and only detects a tiny variety of characteristics related to depression.2
Machine learning can increase the accuracy of the diagnosis and treatment of depression treatment centers by combining continuous digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing hormonal depression treatment (Securityholes.science) Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements. They also capture a wide range of distinctive behaviors and activity patterns that are difficult to capture through interviews.
The study included University of California Los Angeles (UCLA) students with mild to severe depression treatment depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care based on the severity of their depression. Patients who scored high on the CAT DI of 35 or 65 were allocated online support via a peer coach, while those with a score of 75 were sent to in-person clinics for psychotherapy.
At baseline, participants provided a series of questions about their personal characteristics and psychosocial traits. The questions covered age, sex and education as well as financial status, marital status as well as whether they divorced or not, the frequency of suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their degree of depression symptom severity on a 0-100 scale using the CAT-DI. The CAT DI assessment was carried out every two weeks for participants who received online support and weekly for those who received in-person care.
Predictors of Treatment Response
Research is focusing on personalization of treatment for depression. Many studies are aimed at finding predictors that can help clinicians identify the most effective drugs for each person. Pharmacogenetics, for instance, uncovers genetic variations that affect how the body's metabolism reacts to 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 trial-and-error treatments and avoid any negative side consequences.
Another option is to build prediction models that combine clinical data and neural imaging data. These models can be used to determine which variables are the most likely to predict a specific outcome, like whether a drug will improve symptoms or mood. These models can also be used to predict the patient's response to treatment that is already in place which allows doctors to maximize the effectiveness of the current therapy.
A new generation of machines employs machine learning techniques like algorithms for classification and supervised learning, regularized logistic regression and tree-based methods to integrate the effects from multiple variables and increase the accuracy of predictions. These models have been shown to be useful in predicting outcomes of treatment, such as response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to become the standard of future medical practice.
In addition to the ML-based prediction models research into the underlying mechanisms of depression continues. Recent findings suggest that depression is linked to the dysfunctions of specific neural networks. This suggests that an individualized treatment for depression will be based on targeted therapies that restore normal function to these circuits.
One method of doing this is by using internet-based programs that offer a more individualized and tailored experience for patients. A study showed that a web-based program improved symptoms and provided a better quality life for MDD patients. A controlled, randomized study of a customized treatment for depression found that a significant number of participants experienced sustained improvement as well as fewer side effects.
Predictors of side effects
In the treatment of depression, a major challenge is predicting and determining which antidepressant medication will have minimal or zero negative side effects. Many patients take a trial-and-error method, involving various medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant medications that is more effective and specific.
A variety of predictors are available to determine the best antidepressant to prescribe, including genetic variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. To determine the most reliable and accurate predictors for a particular treatment, random controlled trials with larger sample sizes will be required. This is because the identifying of interaction effects or moderators can be a lot more difficult in trials that only consider a single episode of treatment per patient instead of multiple sessions of treatment over time.
Additionally, the prediction of a patient's response to a specific medication will also likely require information on comorbidities and symptom profiles, as well as the patient's personal experience with tolerability and efficacy. Currently, only some easily measurable sociodemographic and clinical variables seem to be correlated with the severity of MDD, such as gender, age, race/ethnicity and SES BMI, the presence of alexithymia, and the severity of depressive symptoms.
The application of pharmacogenetics to treatment for situational depression treatment is in its early stages and there are many obstacles to overcome. First it is necessary to have a clear understanding of the genetic mechanisms is needed as well as an understanding of what is a reliable indicator of treatment response. Ethics, such as privacy, and the ethical use of genetic information should also be considered. Pharmacogenetics can, in the long run reduce stigma associated with mental health treatments and improve the outcomes of treatment. As with all psychiatric approaches it is essential to give careful consideration and implement the plan. The best course of action is to provide patients with an array of effective depression medications and encourage them to talk freely with their doctors about their experiences and concerns.
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