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Personalized Depression Treatment
For many suffering from depression, traditional therapy and medications are not effective. A customized treatment could be the solution.
Cue is an intervention platform that converts sensors that are passively gathered from smartphones 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 uncover distinct features that deterministically change mood as time passes.
Predictors of Mood
Depression is the leading cause of mental illness around the world.1 Yet only half of those suffering from the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat patients most likely to respond to specific treatments.
A customized alternative depression treatment options treatment is one method of doing this. 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 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 so far 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 by the data in medical records, only a few studies have employed longitudinal data to determine predictors of mood in individuals. A few studies also take into consideration the fact that moods can be very different between individuals. Therefore, it is crucial to develop methods that allow for the determination and quantification of the 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. The team will then create algorithms to recognize patterns of behaviour and emotions that are unique to each individual.
In addition to these modalities, the team created a machine learning algorithm to model the dynamic factors that determine a person's depressed mood. The algorithm integrates the individual differences to produce an individual "digital genotype" for each participant.
This digital phenotype has been correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely among individuals.
Predictors of Symptoms
Depression is among the leading causes of disability1 yet it is often untreated and not diagnosed. Depression disorders are usually not treated because of the stigma that surrounds them, as well as the lack of effective treatments.
To aid in the development of a personalized treatment plan, identifying factors that predict the severity of symptoms is crucial. The current prediction methods rely heavily on clinical interviews, which are unreliable and only detect a few features associated with depression.
Using machine learning to integrate continuous digital behavioral phenotypes that are captured through smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, CAT-DI) with other predictors of symptom severity can improve diagnostic accuracy and increase treatment efficacy for prenatal depression treatment. Digital phenotypes can provide continuous, high-resolution measurements and capture a variety of unique behaviors and activity patterns that are difficult to document with interviews.
The study included University of California Los Angeles (UCLA) students experiencing moderate to severe depressive symptoms. participating in the Screening and Treatment for Anxiety Depression Treatment (Qooh.Me) and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care according to the degree of their depression. Those with a score on the CAT DI of 35 or 65 were assigned online support via the help of a coach. Those with a score 75 patients were referred to psychotherapy in-person.
Participants were asked a series questions at the beginning of the study about their psychosocial and demographic characteristics as well as their socioeconomic status. These included sex, age education, work, and financial situation; whether they were partnered, divorced or single; their current suicidal thoughts, intentions or attempts; as well as the frequency at that they consumed alcohol. The CAT-DI was used for assessing the severity of depression symptoms on a scale from zero to 100. The CAT-DI test was performed every two weeks for those who received online support, and weekly for those who received in-person care.
Predictors of Treatment Response
Research is focusing on personalized treatment for depression. Many studies are focused on identifying predictors, which will help doctors determine the most effective drugs for each person. Particularly, pharmacogenetics is able to identify genetic variations that affect how the body metabolizes antidepressants. This enables doctors to choose the medications that are most likely to be most effective for each patient, while minimizing the time and effort in trial-and-error treatments and avoid any adverse effects that could otherwise hinder advancement.
Another option is to build prediction models combining information from clinical studies epilepsy and depression treatment neural imaging data. These models can be used to identify the most appropriate combination of variables that is predictive of a particular outcome, such as whether or not a medication will improve symptoms and mood. These models can be used to predict the response of a patient to a treatment, allowing doctors to maximize the effectiveness.
A new generation of studies uses machine learning methods like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and improve the accuracy of predictive. These models have been demonstrated to be useful in predicting the outcome of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry and will likely be the norm in future clinical practice.
In addition to ML-based prediction models The study of the mechanisms behind prenatal depression treatment continues. Recent findings suggest that the disorder is associated with dysfunctions in specific neural circuits. This theory suggests that an individualized treatment for depression treatment in pregnancy will be based on targeted therapies that restore normal functioning to these circuits.
Internet-based-based therapies can be a way to accomplish this. They can offer a more tailored and individualized experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality of life for MDD patients. A controlled, randomized study of a customized treatment for depression found that a significant number of participants experienced sustained improvement and had fewer adverse negative effects.
Predictors of side effects
In the treatment of depression, a major challenge is predicting and determining which antidepressant medications will have minimal or zero side effects. Many patients experience a trial-and-error approach, using various medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method of selecting antidepressant medicines that are more efficient and targeted.
There are a variety of predictors that can be used to determine the antidepressant that should be prescribed, including genetic variations, phenotypes of patients like gender or ethnicity and comorbidities. However finding the most reliable and valid predictors for a particular treatment will probably require randomized controlled trials of significantly larger numbers of participants than those that are typically part of clinical trials. This is because it may be more difficult to detect moderators or interactions in trials that only include a single episode per person rather than multiple episodes over a period of time.
Additionally the prediction of a patient's reaction to a specific medication is likely to require information on symptoms and comorbidities in addition to the patient's prior subjective experiences with the effectiveness and tolerability of the medication. At present, only a few easily measurable sociodemographic and clinical variables seem to be reliable in predicting response to MDD factors, including age, gender race/ethnicity, SES, BMI and the presence of alexithymia and the severity of depressive symptoms.
Many issues remain to be resolved in the application of pharmacogenetics for depression treatment. First, a clear understanding of the underlying genetic mechanisms is needed and an understanding of what is a reliable predictor of treatment response. Additionally, ethical issues, such as privacy and the responsible use of personal genetic information, must be carefully considered. In the long run pharmacogenetics can offer a chance to lessen the stigma that surrounds mental health care and improve treatment outcomes for those struggling with depression. But, like any approach to psychiatry careful consideration and implementation is necessary. At present, the most effective method is to offer patients various effective medications for depression and encourage them to talk with their physicians about their concerns and experiences.
For many suffering from depression, traditional therapy and medications are not effective. A customized treatment could be the solution.
Cue is an intervention platform that converts sensors that are passively gathered from smartphones 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 uncover distinct features that deterministically change mood as time passes.
Predictors of Mood
Depression is the leading cause of mental illness around the world.1 Yet only half of those suffering from the condition receive treatment. To improve outcomes, clinicians must be able to identify and treat patients most likely to respond to specific treatments.
A customized alternative depression treatment options treatment is one method of doing this. 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 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 so far 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 by the data in medical records, only a few studies have employed longitudinal data to determine predictors of mood in individuals. A few studies also take into consideration the fact that moods can be very different between individuals. Therefore, it is crucial to develop methods that allow for the determination and quantification of the 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. The team will then create algorithms to recognize patterns of behaviour and emotions that are unique to each individual.
In addition to these modalities, the team created a machine learning algorithm to model the dynamic factors that determine a person's depressed mood. The algorithm integrates the individual differences to produce an individual "digital genotype" for each participant.This digital phenotype has been correlated with CAT DI scores that are a psychometrically validated symptoms severity scale. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 x 10-03) and varied widely among individuals.
Predictors of Symptoms
Depression is among the leading causes of disability1 yet it is often untreated and not diagnosed. Depression disorders are usually not treated because of the stigma that surrounds them, as well as the lack of effective treatments.
To aid in the development of a personalized treatment plan, identifying factors that predict the severity of symptoms is crucial. The current prediction methods rely heavily on clinical interviews, which are unreliable and only detect a few features associated with depression.
Using machine learning to integrate continuous digital behavioral phenotypes that are captured through smartphone sensors and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, CAT-DI) with other predictors of symptom severity can improve diagnostic accuracy and increase treatment efficacy for prenatal depression treatment. Digital phenotypes can provide continuous, high-resolution measurements and capture a variety of unique behaviors and activity patterns that are difficult to document with interviews.
The study included University of California Los Angeles (UCLA) students experiencing moderate to severe depressive symptoms. participating in the Screening and Treatment for Anxiety Depression Treatment (Qooh.Me) and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were sent online for support or clinical care according to the degree of their depression. Those with a score on the CAT DI of 35 or 65 were assigned online support via the help of a coach. Those with a score 75 patients were referred to psychotherapy in-person.
Participants were asked a series questions at the beginning of the study about their psychosocial and demographic characteristics as well as their socioeconomic status. These included sex, age education, work, and financial situation; whether they were partnered, divorced or single; their current suicidal thoughts, intentions or attempts; as well as the frequency at that they consumed alcohol. The CAT-DI was used for assessing the severity of depression symptoms on a scale from zero to 100. The CAT-DI test was performed every two weeks for those who received online support, and weekly for those who received in-person care.
Predictors of Treatment Response
Research is focusing on personalized treatment for depression. Many studies are focused on identifying predictors, which will help doctors determine the most effective drugs for each person. Particularly, pharmacogenetics is able to identify genetic variations that affect how the body metabolizes antidepressants. This enables doctors to choose the medications that are most likely to be most effective for each patient, while minimizing the time and effort in trial-and-error treatments and avoid any adverse effects that could otherwise hinder advancement.
Another option is to build prediction models combining information from clinical studies epilepsy and depression treatment neural imaging data. These models can be used to identify the most appropriate combination of variables that is predictive of a particular outcome, such as whether or not a medication will improve symptoms and mood. These models can be used to predict the response of a patient to a treatment, allowing doctors to maximize the effectiveness.
A new generation of studies uses machine learning methods like supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and improve the accuracy of predictive. These models have been demonstrated to be useful in predicting the outcome of treatment for example, the response to antidepressants. These methods are becoming more popular in psychiatry and will likely be the norm in future clinical practice.
In addition to ML-based prediction models The study of the mechanisms behind prenatal depression treatment continues. Recent findings suggest that the disorder is associated with dysfunctions in specific neural circuits. This theory suggests that an individualized treatment for depression treatment in pregnancy will be based on targeted therapies that restore normal functioning to these circuits.
Internet-based-based therapies can be a way to accomplish this. They can offer a more tailored and individualized experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality of life for MDD patients. A controlled, randomized study of a customized treatment for depression found that a significant number of participants experienced sustained improvement and had fewer adverse negative effects.
Predictors of side effectsIn the treatment of depression, a major challenge is predicting and determining which antidepressant medications will have minimal or zero side effects. Many patients experience a trial-and-error approach, using various medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method of selecting antidepressant medicines that are more efficient and targeted.
There are a variety of predictors that can be used to determine the antidepressant that should be prescribed, including genetic variations, phenotypes of patients like gender or ethnicity and comorbidities. However finding the most reliable and valid predictors for a particular treatment will probably require randomized controlled trials of significantly larger numbers of participants than those that are typically part of clinical trials. This is because it may be more difficult to detect moderators or interactions in trials that only include a single episode per person rather than multiple episodes over a period of time.
Additionally the prediction of a patient's reaction to a specific medication is likely to require information on symptoms and comorbidities in addition to the patient's prior subjective experiences with the effectiveness and tolerability of the medication. At present, only a few easily measurable sociodemographic and clinical variables seem to be reliable in predicting response to MDD factors, including age, gender race/ethnicity, SES, BMI and the presence of alexithymia and the severity of depressive symptoms.
Many issues remain to be resolved in the application of pharmacogenetics for depression treatment. First, a clear understanding of the underlying genetic mechanisms is needed and an understanding of what is a reliable predictor of treatment response. Additionally, ethical issues, such as privacy and the responsible use of personal genetic information, must be carefully considered. In the long run pharmacogenetics can offer a chance to lessen the stigma that surrounds mental health care and improve treatment outcomes for those struggling with depression. But, like any approach to psychiatry careful consideration and implementation is necessary. At present, the most effective method is to offer patients various effective medications for depression and encourage them to talk with their physicians about their concerns and experiences.
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