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For many people gripped by depression, traditional therapies and medication are ineffective. The individual approach to treatment could be the answer.
Cue is a digital intervention platform that translates passively acquired normal sensor data from smartphones into customized micro-interventions designed to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to identify their feature predictors and reveal distinct features that deterministically change mood as time passes.
Predictors of Mood
Depression is one of the world's leading causes of mental illness.1 Yet, only half of those who have the disorder receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients who are most likely to respond to specific treatments.
The ability to tailor depression treatments is one way to do this. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit most from certain treatments. They are using sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavioral predictors of response.
The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include demographics like gender, age and education, as well as clinical characteristics like symptom severity, comorbidities and biological markers.
A few studies have utilized longitudinal data in order to determine mood among individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is crucial to devise methods that allow for the analysis and measurement of individual differences between mood predictors, treatment effects, 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. This enables the team to develop algorithms that can systematically identify different patterns of behavior and emotion that are different between people.
In addition to these modalities the team developed a machine-learning algorithm to model the changing variables that influence each person's mood. The algorithm integrates the individual characteristics to create a unique "digital genotype" for each participant.
This digital phenotype was found to be associated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.
Predictors of symptoms
Depression is among the world's leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated due to the stigma associated with them and the lack of effective treatments.
To help with personalized treatment, it is essential to determine the predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few characteristics that are associated with depression.
Machine learning can be used to integrate continuous digital behavioral phenotypes captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, CAT-DI) along with other indicators of symptom severity has the potential to increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes permit continuous, high-resolution measurements and capture a variety of distinctive behaviors and activity patterns that are difficult to record using interviews.
The study included University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. enrolled in the Screening and Treatment for Anxiety and agitated depression treatment (Learn Additional Here) (STAND) program29, which was developed under the UCLA depression private treatment Grand Challenge. Participants were routed to online support or in-person clinical care according to the severity of their depression. Patients with a CAT DI score of 35 or 65 were assigned to online support with an online peer coach, whereas those with a score of 75 were sent to in-person clinics for psychotherapy.
Participants were asked a series of questions at the beginning of the study regarding their psychosocial and demographic characteristics as well as their socioeconomic status. These included age, sex and education, as well as work and financial status; whether they were partnered, divorced, or single; current suicidal ideation, intent, or attempts; and the frequency with that they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale of 100 to. The CAT-DI tests were conducted every week for those who received online support and once a week for those receiving in-person treatment.
Predictors of Treatment Response
Personalized depression treatment is currently a major research area and many studies aim at identifying predictors that allow clinicians to identify the most effective medication for each individual. Pharmacogenetics in particular identifies genetic variations that determine the way that our bodies process drugs. This lets doctors choose the medications that are most likely to work for each patient, while minimizing the amount of time and effort required for trial-and error treatments and eliminating any adverse negative effects.
Another promising approach is building models of prediction using a variety of data sources, combining the clinical information with neural imaging data. These models can be used to identify which variables are most predictive of a specific outcome, like whether a drug will improve mood or symptoms. These models can also be used to predict a patient's response to treatment that is already in place and help doctors maximize the effectiveness of their 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 many variables and increase predictive accuracy. These models have shown to be useful in the prediction of treatment outcomes like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could become the standard of future medical practice.
The study of depression's underlying mechanisms continues, as do ML-based predictive models. Recent research suggests that the disorder is connected with neural dysfunctions that affect specific circuits. This suggests that individualized depression treatment will be built around targeted therapies that target these neural circuits to restore normal functioning.
One way to do this is by using internet-based programs which can offer an individualized and tailored experience for patients. One study found that a web-based program improved symptoms and improved quality life for MDD patients. A controlled, randomized study of a personalized treatment for depression showed that a significant percentage of patients saw improvement over time as well as fewer side effects.
Predictors of adverse effects
In the treatment of depression the biggest challenge is predicting and determining which antidepressant medications will have very little or no side negative effects. Many patients experience a trial-and-error method, involving several medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a fascinating new avenue for a more efficient and specific method of selecting antidepressant therapies.
A variety of predictors are available to determine the best antidepressant to prescribe, including gene variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. However, identifying the most reliable and valid predictors for a particular treatment is likely to require randomized controlled trials of significantly larger numbers of participants than those typically enrolled in clinical trials. This is because the detection of interaction effects or moderators could be more difficult in trials that focus on a single instance of treatment per participant instead of multiple episodes of treatment over a period of time.
Furthermore, the prediction of a patient's reaction to a specific medication is likely to require information about comorbidities and symptom profiles, as well as the patient's previous experience of its tolerability and effectiveness. There are currently only a few easily identifiable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.
Many challenges remain in the application of pharmacogenetics in the treatment of depression. First, it is important to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, as well as an understanding of an accurate indicator of the response to treatment. Ethics, such as privacy, and the responsible use of genetic information should also be considered. Pharmacogenetics could be able to, over the long term help reduce stigma around mental health treatments and improve the outcomes of electric shock treatment for depression. But, like any other psychiatric treatment, careful consideration and application is essential. In the moment, it's ideal to offer patients an array of depression medications that work and encourage them to talk openly with their physicians.
For many people gripped by depression, traditional therapies and medication are ineffective. The individual approach to treatment could be the answer.
Cue is a digital intervention platform that translates passively acquired normal sensor data from smartphones into customized micro-interventions designed to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to identify their feature predictors and reveal distinct features that deterministically change mood as time passes.
Predictors of Mood
Depression is one of the world's leading causes of mental illness.1 Yet, only half of those who have the disorder receive treatment1. To improve outcomes, clinicians must be able to recognize and treat patients who are most likely to respond to specific treatments.
The ability to tailor depression treatments is one way to do this. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit most from certain treatments. They are using sensors on mobile phones as well as a voice assistant that incorporates artificial intelligence as well as other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavioral predictors of response.
The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include demographics like gender, age and education, as well as clinical characteristics like symptom severity, comorbidities and biological markers.
A few studies have utilized longitudinal data in order to determine mood among individuals. They have not taken into account the fact that moods vary significantly between individuals. Therefore, it is crucial to devise methods that allow for the analysis and measurement of individual differences between mood predictors, treatment effects, 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. This enables the team to develop algorithms that can systematically identify different patterns of behavior and emotion that are different between people.
In addition to these modalities the team developed a machine-learning algorithm to model the changing variables that influence each person's mood. The algorithm integrates the individual characteristics to create a unique "digital genotype" for each participant.
This digital phenotype was found to be associated with CAT DI scores, a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.
Predictors of symptoms
Depression is among the world's leading causes of disability1 yet it is often underdiagnosed and undertreated2. Depression disorders are rarely treated due to the stigma associated with them and the lack of effective treatments.
To help with personalized treatment, it is essential to determine the predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few characteristics that are associated with depression.
Machine learning can be used to integrate continuous digital behavioral phenotypes captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, CAT-DI) along with other indicators of symptom severity has the potential to increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes permit continuous, high-resolution measurements and capture a variety of distinctive behaviors and activity patterns that are difficult to record using interviews.
The study included University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. enrolled in the Screening and Treatment for Anxiety and agitated depression treatment (Learn Additional Here) (STAND) program29, which was developed under the UCLA depression private treatment Grand Challenge. Participants were routed to online support or in-person clinical care according to the severity of their depression. Patients with a CAT DI score of 35 or 65 were assigned to online support with an online peer coach, whereas those with a score of 75 were sent to in-person clinics for psychotherapy.
Participants were asked a series of questions at the beginning of the study regarding their psychosocial and demographic characteristics as well as their socioeconomic status. These included age, sex and education, as well as work and financial status; whether they were partnered, divorced, or single; current suicidal ideation, intent, or attempts; and the frequency with that they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale of 100 to. The CAT-DI tests were conducted every week for those who received online support and once a week for those receiving in-person treatment.
Predictors of Treatment Response
Personalized depression treatment is currently a major research area and many studies aim at identifying predictors that allow clinicians to identify the most effective medication for each individual. Pharmacogenetics in particular identifies genetic variations that determine the way that our bodies process drugs. This lets doctors choose the medications that are most likely to work for each patient, while minimizing the amount of time and effort required for trial-and error treatments and eliminating any adverse negative effects.
Another promising approach is building models of prediction using a variety of data sources, combining the clinical information with neural imaging data. These models can be used to identify which variables are most predictive of a specific outcome, like whether a drug will improve mood or symptoms. These models can also be used to predict a patient's response to treatment that is already in place and help doctors maximize the effectiveness of their 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 many variables and increase predictive accuracy. These models have shown to be useful in the prediction of treatment outcomes like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and could become the standard of future medical practice.
The study of depression's underlying mechanisms continues, as do ML-based predictive models. Recent research suggests that the disorder is connected with neural dysfunctions that affect specific circuits. This suggests that individualized depression treatment will be built around targeted therapies that target these neural circuits to restore normal functioning.
One way to do this is by using internet-based programs which can offer an individualized and tailored experience for patients. One study found that a web-based program improved symptoms and improved quality life for MDD patients. A controlled, randomized study of a personalized treatment for depression showed that a significant percentage of patients saw improvement over time as well as fewer side effects.
Predictors of adverse effects
In the treatment of depression the biggest challenge is predicting and determining which antidepressant medications will have very little or no side negative effects. Many patients experience a trial-and-error method, involving several medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a fascinating new avenue for a more efficient and specific method of selecting antidepressant therapies.
A variety of predictors are available to determine the best antidepressant to prescribe, including gene variations, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and the presence of comorbidities. However, identifying the most reliable and valid predictors for a particular treatment is likely to require randomized controlled trials of significantly larger numbers of participants than those typically enrolled in clinical trials. This is because the detection of interaction effects or moderators could be more difficult in trials that focus on a single instance of treatment per participant instead of multiple episodes of treatment over a period of time.
Furthermore, the prediction of a patient's reaction to a specific medication is likely to require information about comorbidities and symptom profiles, as well as the patient's previous experience of its tolerability and effectiveness. There are currently only a few easily identifiable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.
Many challenges remain in the application of pharmacogenetics in the treatment of depression. First, it is important to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, as well as an understanding of an accurate indicator of the response to treatment. Ethics, such as privacy, and the responsible use of genetic information should also be considered. Pharmacogenetics could be able to, over the long term help reduce stigma around mental health treatments and improve the outcomes of electric shock treatment for depression. But, like any other psychiatric treatment, careful consideration and application is essential. In the moment, it's ideal to offer patients an array of depression medications that work and encourage them to talk openly with their physicians.- 이전글Think You're Cut Out For Patio Doors Repairs? Try This Quiz 24.10.24
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