Why People Don't Care About Personalized Depression Treatment
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Kandi Kenney 작성일24-10-26 00:59본문
Personalized Depression Treatment
For a lot of people suffering from depression, traditional therapies and medications are not effective. Personalized treatment could be the solution.
Cue is an intervention platform for digital devices that translates passively acquired normal 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 discover their feature predictors and reveal 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 people suffering from the condition receive treatment1. To improve outcomes, clinicians need to be able to identify and treat patients with the highest likelihood of responding to specific treatments.
Personalized depression treatment can help. Using sensors for mobile phones as well as 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 the treatments they receive. Two grants totaling more than $10 million will be used to identify the biological and behavioral indicators of response.
The majority of research done to date has focused on sociodemographic and clinical characteristics. These include demographic factors such as age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological markers like neuroimaging and genetic variation.
While many of these factors can be predicted from the information in medical records, very few studies have employed longitudinal data to explore the causes of mood among individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is 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 person.
The team also developed an algorithm for machine learning to identify dynamic predictors of each person's mood for depression treatment medications. The algorithm blends the individual differences to create an individual "digital genotype" for each participant.
This digital phenotype has been correlated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely across individuals.
Predictors of symptoms
Depression is one of the leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depre they were divorced or not, current suicidal ideas, intent or attempts, and how often they drank. The CAT-DI was used to assess the severity of depression symptoms on a scale from zero to 100. The CAT-DI tests were conducted every week for those who received online support and every week for those who received in-person treatment.
Predictors of Treatment Response
Personalized depression treatment is currently a major research area and a lot of studies are aimed at identifying predictors that will allow clinicians to identify the most effective medication for each person. Particularly, pharmacogenetics is able to identify genetic variations that affect the way that the body processes antidepressants. This lets doctors select the medication that are likely to be the most effective for each patient, reducing the time and effort needed for trial-and error treatments and eliminating any adverse effects.
Another approach that is promising is to build predictive models that incorporate clinical data and neural imaging data. These models can then be used to determine the most appropriate combination of variables that is predictive of a particular outcome, like whether or not a drug will improve the mood and symptoms. These models can be used to determine the patient's response to an existing treatment which allows doctors to maximize the effectiveness of current therapy.
A new type 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 multiple variables and increase predictive accuracy. These models have proven to be useful in the prediction of treatment outcomes like the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the standard for the future of clinical practice.
In addition to the ML-based prediction models research into the underlying mechanisms of depression continues. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This theory suggests that individual depression treatment will be based on targeted therapies that target these neural circuits to restore normal functioning.
One way to do this is through internet-delivered interventions which can offer an personalized and customized experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality life for MDD patients. Additionally, a randomized controlled study of a personalised approach to treating depression showed steady improvement and decreased adverse effects in a large percentage of participants.
Predictors of Side Effects
A major obstacle in individualized residential depression treatment uk treatment is predicting which antidepressant medications will cause minimal or no side effects. Many patients are prescribed a variety of drugs before they find a drug that is safe and effective. Pharmacogenetics provides a novel and exciting way to select antidepressant medicines that are more effective and specific.
A variety of predictors are available to determine which antidepressant is best to prescribe, including genetic variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. However finding the most reliable and valid predictors ect for treatment resistant depression a particular treatment is likely to require controlled, randomized trials with significantly larger numbers of participants than those normally enrolled in clinical trials. This is due to the fact that the identification of moderators or interaction effects may be much more difficult in trials that consider a single episode of treatment per participant instead of multiple sessions of treatment over a period of time.
In addition to that, predicting a patient's reaction will likely require information about the severity of symptoms, comorbidities and the patient's subjective perception of the effectiveness and tolerability. Currently, only a few easily measurable sociodemographic variables as well as clinical variables seem to be reliably related to response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.
The application of pharmacogenetics to depression treatment history treatment is still in its beginning stages, and many challenges remain. First is a thorough understanding of the underlying genetic mechanisms is needed and an understanding of what constitutes a reliable predictor for treatment response. In addition, ethical issues, such as privacy and the appropriate use of personal genetic information, must be considered carefully. In the long term, pharmacogenetics may provide an opportunity to reduce the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. As with all psychiatric approaches it is crucial to carefully consider and implement the plan. For now, it is recommended to provide patients with a variety of medications for depression that work and encourage patients to openly talk with their physicians.
For a lot of people suffering from depression, traditional therapies and medications are not effective. Personalized treatment could be the solution.
Cue is an intervention platform for digital devices that translates passively acquired normal 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 discover their feature predictors and reveal 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 people suffering from the condition receive treatment1. To improve outcomes, clinicians need to be able to identify and treat patients with the highest likelihood of responding to specific treatments.
Personalized depression treatment can help. Using sensors for mobile phones as well as 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 the treatments they receive. Two grants totaling more than $10 million will be used to identify the biological and behavioral indicators of response.
The majority of research done to date has focused on sociodemographic and clinical characteristics. These include demographic factors such as age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological markers like neuroimaging and genetic variation.
While many of these factors can be predicted from the information in medical records, very few studies have employed longitudinal data to explore the causes of mood among individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is 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 person.
The team also developed an algorithm for machine learning to identify dynamic predictors of each person's mood for depression treatment medications. The algorithm blends the individual differences to create an individual "digital genotype" for each participant.
This digital phenotype has been correlated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely across individuals.
Predictors of symptoms
Depression is one of the leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depre they were divorced or not, current suicidal ideas, intent or attempts, and how often they drank. The CAT-DI was used to assess the severity of depression symptoms on a scale from zero to 100. The CAT-DI tests were conducted every week for those who received online support and every week for those who received in-person treatment.
Predictors of Treatment Response
Personalized depression treatment is currently a major research area and a lot of studies are aimed at identifying predictors that will allow clinicians to identify the most effective medication for each person. Particularly, pharmacogenetics is able to identify genetic variations that affect the way that the body processes antidepressants. This lets doctors select the medication that are likely to be the most effective for each patient, reducing the time and effort needed for trial-and error treatments and eliminating any adverse effects.
Another approach that is promising is to build predictive models that incorporate clinical data and neural imaging data. These models can then be used to determine the most appropriate combination of variables that is predictive of a particular outcome, like whether or not a drug will improve the mood and symptoms. These models can be used to determine the patient's response to an existing treatment which allows doctors to maximize the effectiveness of current therapy.
A new type 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 multiple variables and increase predictive accuracy. These models have proven to be useful in the prediction of treatment outcomes like the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the standard for the future of clinical practice.
In addition to the ML-based prediction models research into the underlying mechanisms of depression continues. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This theory suggests that individual depression treatment will be based on targeted therapies that target these neural circuits to restore normal functioning.
One way to do this is through internet-delivered interventions which can offer an personalized and customized experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality life for MDD patients. Additionally, a randomized controlled study of a personalised approach to treating depression showed steady improvement and decreased adverse effects in a large percentage of participants.
Predictors of Side Effects
A major obstacle in individualized residential depression treatment uk treatment is predicting which antidepressant medications will cause minimal or no side effects. Many patients are prescribed a variety of drugs before they find a drug that is safe and effective. Pharmacogenetics provides a novel and exciting way to select antidepressant medicines that are more effective and specific.
A variety of predictors are available to determine which antidepressant is best to prescribe, including genetic variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. However finding the most reliable and valid predictors ect for treatment resistant depression a particular treatment is likely to require controlled, randomized trials with significantly larger numbers of participants than those normally enrolled in clinical trials. This is due to the fact that the identification of moderators or interaction effects may be much more difficult in trials that consider a single episode of treatment per participant instead of multiple sessions of treatment over a period of time.
In addition to that, predicting a patient's reaction will likely require information about the severity of symptoms, comorbidities and the patient's subjective perception of the effectiveness and tolerability. Currently, only a few easily measurable sociodemographic variables as well as clinical variables seem to be reliably related to response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.
The application of pharmacogenetics to depression treatment history treatment is still in its beginning stages, and many challenges remain. First is a thorough understanding of the underlying genetic mechanisms is needed and an understanding of what constitutes a reliable predictor for treatment response. In addition, ethical issues, such as privacy and the appropriate use of personal genetic information, must be considered carefully. In the long term, pharmacogenetics may provide an opportunity to reduce the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. As with all psychiatric approaches it is crucial to carefully consider and implement the plan. For now, it is recommended to provide patients with a variety of medications for depression that work and encourage patients to openly talk with their physicians.
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