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Personalized Depression Treatment
For a lot of people suffering from depression private treatment, traditional therapy and medications are not effective. Personalized treatment could 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 analyzed the best-fitting personalized ML models to each person using Shapley values to determine their characteristic predictors. The results revealed distinct characteristics that deterministically changed mood over time.
Predictors of Mood
Depression is one of the leading causes of mental illness.1 However, only half of those suffering from the disorder receive treatment1. 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. Utilizing sensors for 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 determine which patients will benefit from the treatments they receive. Two grants were awarded that total over $10 million, they will make use of these tools to identify the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.
The majority of research into predictors of depression treatment effectiveness has been focused on sociodemographic and clinical characteristics. These include demographic factors like age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.
While many of these aspects can be predicted from the information in medical records, only a few studies have utilized longitudinal data to determine the factors that influence mood in people. A few studies also take into consideration the fact that moods can differ significantly between individuals. Therefore, it is essential to develop methods that permit the recognition of the individual differences in mood predictors and treatments 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 created a machine learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.
This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. 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 a leading cause of disability in the world1, but it is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigma associated with depression disorders hinder many from seeking treatment.
To allow for individualized treatment, identifying predictors of symptoms is important. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few symptoms associated with depression.
Machine learning is used to integrate continuous digital behavioral phenotypes captured by sensors on smartphones and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) together with other predictors of severity of symptoms can improve diagnostic accuracy and increase the effectiveness of treatment for depression. These digital phenotypes are able to capture a variety of unique actions and behaviors that are difficult to document through interviews, and also allow for high-resolution, continuous measurements.
The study involved University of California Los Angeles students with mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or in-person clinics depending on their depression severity. Those with a CAT-DI score of 35 65 were assigned online support via a coach and those with scores of 75 were sent to in-person clinics for psychotherapy.
Participants were asked a set of questions at the beginning of the study about their demographics and psychosocial traits. The questions covered education, age, sex and gender and financial status, marital status as well as whether they divorced or not, the frequency of suicidal ideas, intent or attempts, as well as how often they drank. Participants also scored their level of depression severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted every other week for the participants that received online support, and weekly for those receiving in-person care.
Predictors of the Reaction to Treatment
Research is focusing on personalized treatment for depression and treatment. Many studies are focused on finding predictors, which can aid clinicians in identifying the most effective drugs to treat each individual. Particularly, pharmacogenetics is able to identify genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors select medications that will likely work best for each patient, reducing time and effort spent on trial-and-error treatments and eliminating any adverse negative effects.
Another approach that is promising is to build prediction models combining clinical data and neural imaging data. These models can be used to determine which variables are the most likely to predict a specific outcome, such as whether a drug will help with symptoms or mood. These models can be used to determine the patient's response to treatment, allowing doctors to maximize the effectiveness.
A new type of research utilizes machine learning techniques like 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 shown to be effective in predicting the outcome of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry, and are likely to become the norm in the future medical practice.
In addition to the ML-based prediction models research into the mechanisms that cause depression continues. Recent research suggests that depression is related to the malfunctions of certain neural networks. This suggests that individualized depression treatment will be based on targeted therapies that target these circuits in order to restore normal functioning.
Internet-based interventions are a way to achieve this. They can provide an individualized and tailored experience for patients. One study found that a web-based program improved symptoms and led to a better quality of life for MDD patients. Furthermore, a randomized controlled study of a customized approach to treating depression showed sustained improvement and reduced adverse effects in a significant proportion of participants.
Predictors of Side Effects
In the treatment resistant anxiety and depression of depression the biggest challenge is predicting and identifying which antidepressant medications will have minimal or zero side effects. Many patients experience a trial-and-error approach, with several medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant medicines that are more effective and specific.
Many predictors can be used to determine the best antidepressant to prescribe, including gene variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. However finding the most reliable and valid predictors for a particular treatment will probably require randomized controlled trials with much larger samples than those typically enrolled in clinical trials. This is due to the fact that it can be more difficult to identify moderators or interactions in trials that comprise only one episode per participant rather than multiple episodes over a period of time.
Furthermore, the estimation of a patient's response to a particular medication is likely to require information on comorbidities and symptom profiles, as well as the patient's prior subjective experience of its tolerability and effectiveness. At present, only a few easily identifiable sociodemographic and clinical variables seem to be reliably associated with the response to MDD like gender, age race/ethnicity, BMI, the presence of alexithymia and the severity of depressive symptoms.
There are many challenges to overcome when it comes to the use of pharmacogenetics in the treatment of depression. First, a clear understanding of the genetic mechanisms is required and an understanding of what is a reliable predictor of treatment response. Ethics, such as privacy, and the responsible use genetic information should also be considered. In the long run the use of pharmacogenetics could provide an opportunity to reduce the stigma associated with mental health treatment and improve the outcomes of those suffering with depression. But, like any approach to psychiatry careful consideration and planning is necessary. At present, it's recommended to provide patients with an array of depression treatment centers near me medications that are effective and encourage them to speak openly with their doctor.
For a lot of people suffering from depression private treatment, traditional therapy and medications are not effective. Personalized treatment could 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 analyzed the best-fitting personalized ML models to each person using Shapley values to determine their characteristic predictors. The results revealed distinct characteristics that deterministically changed mood over time.
Predictors of Mood
Depression is one of the leading causes of mental illness.1 However, only half of those suffering from the disorder receive treatment1. 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. Utilizing sensors for 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 determine which patients will benefit from the treatments they receive. Two grants were awarded that total over $10 million, they will make use of these tools to identify the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.
The majority of research into predictors of depression treatment effectiveness has been focused on sociodemographic and clinical characteristics. These include demographic factors like age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.
While many of these aspects can be predicted from the information in medical records, only a few studies have utilized longitudinal data to determine the factors that influence mood in people. A few studies also take into consideration the fact that moods can differ significantly between individuals. Therefore, it is essential to develop methods that permit the recognition of the individual differences in mood predictors and treatments 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 created a machine learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these personal variations into a distinct "digital phenotype" for each participant.
This digital phenotype was correlated with CAT DI scores, a psychometrically validated symptom severity scale. 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 a leading cause of disability in the world1, but it is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigma associated with depression disorders hinder many from seeking treatment.
To allow for individualized treatment, identifying predictors of symptoms is important. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few symptoms associated with depression.
Machine learning is used to integrate continuous digital behavioral phenotypes captured by sensors on smartphones and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) together with other predictors of severity of symptoms can improve diagnostic accuracy and increase the effectiveness of treatment for depression. These digital phenotypes are able to capture a variety of unique actions and behaviors that are difficult to document through interviews, and also allow for high-resolution, continuous measurements.
The study involved University of California Los Angeles students with mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were referred to online assistance or in-person clinics depending on their depression severity. Those with a CAT-DI score of 35 65 were assigned online support via a coach and those with scores of 75 were sent to in-person clinics for psychotherapy.
Participants were asked a set of questions at the beginning of the study about their demographics and psychosocial traits. The questions covered education, age, sex and gender and financial status, marital status as well as whether they divorced or not, the frequency of suicidal ideas, intent or attempts, as well as how often they drank. Participants also scored their level of depression severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted every other week for the participants that received online support, and weekly for those receiving in-person care.
Predictors of the Reaction to Treatment
Research is focusing on personalized treatment for depression and treatment. Many studies are focused on finding predictors, which can aid clinicians in identifying the most effective drugs to treat each individual. Particularly, pharmacogenetics is able to identify genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors select medications that will likely work best for each patient, reducing time and effort spent on trial-and-error treatments and eliminating any adverse negative effects.
Another approach that is promising is to build prediction models combining clinical data and neural imaging data. These models can be used to determine which variables are the most likely to predict a specific outcome, such as whether a drug will help with symptoms or mood. These models can be used to determine the patient's response to treatment, allowing doctors to maximize the effectiveness.
A new type of research utilizes machine learning techniques like 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 shown to be effective in predicting the outcome of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry, and are likely to become the norm in the future medical practice.
In addition to the ML-based prediction models research into the mechanisms that cause depression continues. Recent research suggests that depression is related to the malfunctions of certain neural networks. This suggests that individualized depression treatment will be based on targeted therapies that target these circuits in order to restore normal functioning.
Internet-based interventions are a way to achieve this. They can provide an individualized and tailored experience for patients. One study found that a web-based program improved symptoms and led to a better quality of life for MDD patients. Furthermore, a randomized controlled study of a customized approach to treating depression showed sustained improvement and reduced adverse effects in a significant proportion of participants.
Predictors of Side Effects
In the treatment resistant anxiety and depression of depression the biggest challenge is predicting and identifying which antidepressant medications will have minimal or zero side effects. Many patients experience a trial-and-error approach, with several medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant medicines that are more effective and specific.
Many predictors can be used to determine the best antidepressant to prescribe, including gene variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. However finding the most reliable and valid predictors for a particular treatment will probably require randomized controlled trials with much larger samples than those typically enrolled in clinical trials. This is due to the fact that it can be more difficult to identify moderators or interactions in trials that comprise only one episode per participant rather than multiple episodes over a period of time.
Furthermore, the estimation of a patient's response to a particular medication is likely to require information on comorbidities and symptom profiles, as well as the patient's prior subjective experience of its tolerability and effectiveness. At present, only a few easily identifiable sociodemographic and clinical variables seem to be reliably associated with the response to MDD like gender, age race/ethnicity, BMI, the presence of alexithymia and the severity of depressive symptoms.
There are many challenges to overcome when it comes to the use of pharmacogenetics in the treatment of depression. First, a clear understanding of the genetic mechanisms is required and an understanding of what is a reliable predictor of treatment response. Ethics, such as privacy, and the responsible use genetic information should also be considered. In the long run the use of pharmacogenetics could provide an opportunity to reduce the stigma associated with mental health treatment and improve the outcomes of those suffering with depression. But, like any approach to psychiatry careful consideration and planning is necessary. At present, it's recommended to provide patients with an array of depression treatment centers near me medications that are effective and encourage them to speak openly with their doctor.
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