마이펫자랑 | 10 Meetups About Personalized Depression Treatment You Should Attend
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작성자 Stefan Lack 작성일24-09-21 17:40관련링크
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Personalized Depression Treatment
For many suffering from depression, traditional therapy and medications are not effective. The individual approach to treatment could be the solution.
Cue is an intervention platform for digital devices that transforms passively acquired smartphone sensor data into personalized micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct features that are able to 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 who have the disorder receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat patients with the highest chance of responding to specific treatments.
The treatment of depression can be personalized to help. Utilizing sensors on mobile phones, an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to predict which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to determine the biological and behavioral predictors of response.
The majority of research to the present has been focused on clinical and sociodemographic characteristics. These include demographics such as gender, age, and education, as well as clinical aspects such as symptom severity and comorbidities as well as biological markers.
Very few studies have used longitudinal data to determine mood among individuals. Few also take into account the fact that mood varies significantly between individuals. It is therefore important to devise methods that allow for the analysis and measurement 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 detect patterns of behavior and emotions that are unique to each individual.
The team also developed a machine-learning algorithm that can create dynamic predictors for the mood of each person's depression. The algorithm combines these individual characteristics into a distinctive "digital phenotype" for each participant.
The digital phenotype was associated with CAT-DI scores, a psychometrically validated symptom severity scale. The correlation was not strong, however (Pearson r = 0,08, P-value adjusted by BH 3.55 x 10 03) and varied widely among individuals.
Predictors of symptoms
Depression is the leading reason for disability across the world1, however, it is often not properly diagnosed and treated. In addition the absence of effective interventions and stigma associated with depressive disorders prevent many individuals from seeking help.
To facilitate personalized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. However, current prediction methods rely on clinical interview, which is unreliable and only detects a tiny number of features associated with depression.2
Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements as well as capture a wide variety of unique behaviors and activity patterns that are difficult to capture using interviews.
The study involved University of California Los Angeles students with moderate to severe depression symptoms who were taking part in the Screening and natural treatment for anxiety and depression for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment according to the severity of their depression. Patients with a CAT DI score of 35 or 65 were given online support by an instructor and those with scores of 75 patients were referred to in-person psychotherapy.
At the beginning, participants answered a series of questions about their personal characteristics and psychosocial traits. The questions asked included age, sex, and education and marital status, financial status, whether they were divorced or not, current suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale of 0-100. CAT-DI assessments were conducted every week for those that received online support, and weekly for those receiving in-person treatment resistant anxiety and depression.
Predictors of Treatment Reaction
Research is focusing on personalized depression treatment. Many studies are focused on finding predictors that can aid clinicians in identifying the most effective drugs for each person. Pharmacogenetics in particular identifies genetic variations that determine how the human body metabolizes drugs. This lets doctors choose the medications that are likely to be the most effective for each patient, reducing the amount of time and effort required for trial-and error treatments and avoiding any side consequences.
Another promising approach is to develop predictive models that incorporate information from clinical studies and neural imaging data. These models can be used to identify which variables are most predictive of a specific outcome, like whether a medication will improve symptoms or mood. These models can also be used to predict the response of a patient to treatment that is already in place and help doctors maximize the effectiveness of the current therapy.
A new generation of studies utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be useful in predicting outcomes of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry and could become the standard of future medical practice.
In addition to the ML-based prediction models The study of the mechanisms behind depression continues. Recent findings suggest that depression is related to the dysfunctions of specific neural networks. This theory suggests that a individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.
One method of doing this is through internet-delivered interventions that can provide a more personalized and customized experience for patients. One study found that a web-based program improved symptoms and led to a better quality life for MDD patients. In addition, a controlled randomized study of a customized approach to depression treatment showed sustained improvement and reduced side effects in a significant percentage of participants.
Predictors of Side Effects
In the treatment of depression, one of the most difficult aspects is predicting and determining which antidepressant medication will have no or minimal negative side effects. Many patients take a trial-and-error approach, with a variety of medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting way to select antidepressant medications that is more effective and precise.
A variety of predictors are available to determine the best antidepressant to prescribe, including gene variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. However it is difficult to determine the most reliable and valid predictive factors for a specific treatment will probably require controlled, randomized trials with considerably larger samples than those that are typically part of clinical trials. This is because it may be more difficult to determine the effects of moderators or interactions in trials that comprise only one episode per person instead of multiple episodes spread over a period of time.
Additionally the prediction of a patient's reaction to a particular medication is likely to require information about comorbidities and symptom profiles, as well as the patient's personal experiences with the effectiveness and tolerability of the medication. Presently, only a handful of easily assessable sociodemographic and clinical variables are believed to be correlated with response to MDD factors, including age, gender race/ethnicity, BMI and the presence of alexithymia, and the severity of depressive symptoms.
There are many challenges to overcome in the use of pharmacogenetics to treat depression. first line treatment for depression and anxiety line treatment for anxiety and depression (you can find out more), a clear understanding of the genetic mechanisms is essential as well as a clear definition of what is a reliable indicator of treatment response. In addition, ethical concerns like privacy and the ethical use of personal genetic information, must be considered carefully. Pharmacogenetics could be able to, over the long term reduce stigma associated with mental health treatment and improve treatment outcomes. However, as with any approach to psychiatry careful consideration and planning is essential. The best method is to provide patients with a variety of effective depression medications and encourage them to speak freely with their doctors about their concerns and experiences.
For many suffering from depression, traditional therapy and medications are not effective. The individual approach to treatment could be the solution.
Cue is an intervention platform for digital devices that transforms passively acquired smartphone sensor data into personalized micro-interventions that improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct features that are able to 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 who have the disorder receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat patients with the highest chance of responding to specific treatments.
The treatment of depression can be personalized to help. Utilizing sensors on mobile phones, an artificial intelligence voice assistant, and other digital tools, researchers at the University of Illinois Chicago (UIC) are working on new ways to predict which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to determine the biological and behavioral predictors of response.
The majority of research to the present has been focused on clinical and sociodemographic characteristics. These include demographics such as gender, age, and education, as well as clinical aspects such as symptom severity and comorbidities as well as biological markers.
Very few studies have used longitudinal data to determine mood among individuals. Few also take into account the fact that mood varies significantly between individuals. It is therefore important to devise methods that allow for the analysis and measurement 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 detect patterns of behavior and emotions that are unique to each individual.
The team also developed a machine-learning algorithm that can create dynamic predictors for the mood of each person's depression. The algorithm combines these individual characteristics into a distinctive "digital phenotype" for each participant.
The digital phenotype was associated with CAT-DI scores, a psychometrically validated symptom severity scale. The correlation was not strong, however (Pearson r = 0,08, P-value adjusted by BH 3.55 x 10 03) and varied widely among individuals.
Predictors of symptoms
Depression is the leading reason for disability across the world1, however, it is often not properly diagnosed and treated. In addition the absence of effective interventions and stigma associated with depressive disorders prevent many individuals from seeking help.
To facilitate personalized treatment in order to provide a more personalized treatment, identifying predictors of symptoms is important. However, current prediction methods rely on clinical interview, which is unreliable and only detects a tiny number of features associated with depression.2
Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral phenotypes collected from smartphone sensors along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes allow continuous, high-resolution measurements as well as capture a wide variety of unique behaviors and activity patterns that are difficult to capture using interviews.
The study involved University of California Los Angeles students with moderate to severe depression symptoms who were taking part in the Screening and natural treatment for anxiety and depression for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or to clinical treatment according to the severity of their depression. Patients with a CAT DI score of 35 or 65 were given online support by an instructor and those with scores of 75 patients were referred to in-person psychotherapy.
At the beginning, participants answered a series of questions about their personal characteristics and psychosocial traits. The questions asked included age, sex, and education and marital status, financial status, whether they were divorced or not, current suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. The CAT-DI was used to assess the severity of depression-related symptoms on a scale of 0-100. CAT-DI assessments were conducted every week for those that received online support, and weekly for those receiving in-person treatment resistant anxiety and depression.
Predictors of Treatment Reaction
Research is focusing on personalized depression treatment. Many studies are focused on finding predictors that can aid clinicians in identifying the most effective drugs for each person. Pharmacogenetics in particular identifies genetic variations that determine how the human body metabolizes drugs. This lets doctors choose the medications that are likely to be the most effective for each patient, reducing the amount of time and effort required for trial-and error treatments and avoiding any side consequences.
Another promising approach is to develop predictive models that incorporate information from clinical studies and neural imaging data. These models can be used to identify which variables are most predictive of a specific outcome, like whether a medication will improve symptoms or mood. These models can also be used to predict the response of a patient to treatment that is already in place and help doctors maximize the effectiveness of the current therapy.
A new generation of studies utilizes machine learning techniques such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to combine the effects of multiple variables to improve predictive accuracy. These models have been proven to be useful in predicting outcomes of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry and could become the standard of future medical practice.
In addition to the ML-based prediction models The study of the mechanisms behind depression continues. Recent findings suggest that depression is related to the dysfunctions of specific neural networks. This theory suggests that a individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.
One method of doing this is through internet-delivered interventions that can provide a more personalized and customized experience for patients. One study found that a web-based program improved symptoms and led to a better quality life for MDD patients. In addition, a controlled randomized study of a customized approach to depression treatment showed sustained improvement and reduced side effects in a significant percentage of participants.
Predictors of Side Effects
In the treatment of depression, one of the most difficult aspects is predicting and determining which antidepressant medication will have no or minimal negative side effects. Many patients take a trial-and-error approach, with a variety of medications being prescribed before settling on one that is effective and tolerable. Pharmacogenetics offers a fresh and exciting way to select antidepressant medications that is more effective and precise.
A variety of predictors are available to determine the best antidepressant to prescribe, including gene variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. However it is difficult to determine the most reliable and valid predictive factors for a specific treatment will probably require controlled, randomized trials with considerably larger samples than those that are typically part of clinical trials. This is because it may be more difficult to determine the effects of moderators or interactions in trials that comprise only one episode per person instead of multiple episodes spread over a period of time.
Additionally the prediction of a patient's reaction to a particular medication is likely to require information about comorbidities and symptom profiles, as well as the patient's personal experiences with the effectiveness and tolerability of the medication. Presently, only a handful of easily assessable sociodemographic and clinical variables are believed to be correlated with response to MDD factors, including age, gender race/ethnicity, BMI and the presence of alexithymia, and the severity of depressive symptoms.
There are many challenges to overcome in the use of pharmacogenetics to treat depression. first line treatment for depression and anxiety line treatment for anxiety and depression (you can find out more), a clear understanding of the genetic mechanisms is essential as well as a clear definition of what is a reliable indicator of treatment response. In addition, ethical concerns like privacy and the ethical use of personal genetic information, must be considered carefully. Pharmacogenetics could be able to, over the long term reduce stigma associated with mental health treatment and improve treatment outcomes. However, as with any approach to psychiatry careful consideration and planning is essential. The best method is to provide patients with a variety of effective depression medications and encourage them to speak freely with their doctors about their concerns and experiences.
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