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Personalized Depression Treatment
Traditional therapies and medications are not effective for a lot of people who are depressed. A customized treatment could be the solution.
Cue is an intervention platform that transforms 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 identify their feature predictors and uncover distinct features that deterministically change mood as time passes.
Predictors of Mood
Depression is among the leading causes of mental illness.1 However, only half of those who have the disorder receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat patients who are most likely to respond to specific treatments.
The treatment of depression can be personalized to help. By using sensors for mobile phones and 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 the treatments they receive. With two grants awarded totaling more than $10 million, they will make use of these technologies to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.
The majority of research conducted to so far has focused on clinical and sociodemographic characteristics. These include demographic variables such as age, gender and education, clinical characteristics such as symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted from data in medical records, very few studies have employed longitudinal data to explore predictors of mood in individuals. Many studies do not consider the fact that moods can be very different between individuals. Therefore, it is essential to develop methods that permit the determination of individual differences in mood predictors and the effects of treatment.
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 detect patterns of behaviour and emotions that are unique to each individual.
The team also devised a machine learning algorithm to model dynamic predictors for each person's depression mood. The algorithm combines 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 scale for assessing severity of symptom. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.
Predictors of symptoms
Depression is a leading reason for disability across the world1, however, it is often misdiagnosed and untreated2. Depression Treatment No Medication (Bryan-Galbraith.Mdwrite.Net) disorders are rarely treated because of the stigma that surrounds them, as well as the lack of effective interventions.
To help with personalized treatment, it is important to identify predictors of symptoms. However, the methods used to predict symptoms rely on clinical interview, which is not reliable and only detects a tiny variety of characteristics related to depression.2
Using machine learning to blend continuous digital behavioral phenotypes of a person captured by sensors on smartphones and an online mental depression treatment health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of severity of symptoms could increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements as well as capture a wide range of unique behaviors and activity patterns that are difficult to record with interviews.
The study involved University of California Los Angeles students who had mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment depending on their depression severity. Participants who scored a high on the CAT-DI of 35 or 65 were assigned online support with a peer coach, while those who scored 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 age, sex, education, work, and financial situation; whether they were divorced, married, or single; current suicidal ideas, intent or attempts; as well as the frequency with the frequency they consumed alcohol. Participants also scored their level of depression symptom severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI tests were conducted every other week for the participants that received online support, and once a week for those receiving in-person support.
Predictors of Treatment Response
The development of a personalized depression treatment is currently a major research area and many studies aim at identifying predictors that will enable clinicians to determine the most effective medications for each person. Pharmacogenetics, in particular, identifies genetic variations that determine the way that our bodies process drugs. This allows doctors select medications that are likely to be the most effective for each patient, while minimizing time and effort spent on trial-and-error treatments and avoiding any side negative effects.
Another approach that is promising is to develop 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, such as whether or not a particular medication will improve the mood and symptoms. These models can also be used to predict a patient's response to treatment that is already in place, allowing doctors to maximize the effectiveness of current treatment.
A new generation employs machine learning techniques such as supervised and classification algorithms such as regularized logistic regression, and tree-based methods to combine the effects of several variables and improve predictive accuracy. These models have been demonstrated to be effective in predicting outcomes of treatment like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and will likely be the norm in future clinical practice.
In addition to ML-based prediction models, research into the underlying mechanisms of depression is continuing. Recent research suggests that the disorder is connected with neural dysfunctions that affect specific circuits. This theory suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.
Internet-based-based therapies can be an effective method to achieve this. They can provide a more tailored and individualized experience for patients. One study found that an internet-based program helped improve symptoms and provided a better quality of life for MDD patients. Furthermore, a randomized controlled study of a personalised treatment for post stroke depression treatment demonstrated an improvement in symptoms and fewer adverse effects in a significant proportion of participants.
Predictors of Side Effects
A major obstacle in individualized depression treatment involves identifying and predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed a variety medications before settling on a treatment that is effective and tolerated. Pharmacogenetics offers a fascinating new way to take an effective and precise method of selecting antidepressant therapies.
There are many predictors that can be used to determine the antidepressant that should be prescribed, such as gene variations, patient phenotypes such as gender or ethnicity, and comorbidities. However it is difficult to determine the most reliable and accurate predictors for a particular treatment is likely to require randomized controlled trials with much larger samples 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 contain only a single episode per person instead of multiple episodes spread over time.
Additionally, the estimation of a patient's response to a particular medication will likely also require information on comorbidities and symptom profiles, and the patient's previous experience with tolerability and efficacy. At present, only a handful of easily identifiable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.
There are many challenges to overcome when it comes to the use of pharmacogenetics to treat depression. First it is necessary to have a clear understanding of the underlying genetic mechanisms is essential and a clear definition 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. Pharmacogenetics can, in the long run help reduce stigma around mental health treatment and improve treatment outcomes. But, like any other psychiatric treatment, careful consideration and application is necessary. At present, the most effective method is to offer patients a variety of effective depression medications and encourage them to speak freely with their doctors about their experiences and concerns.
Traditional therapies and medications are not effective for a lot of people who are depressed. A customized treatment could be the solution.
Cue is an intervention platform that transforms 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 identify their feature predictors and uncover distinct features that deterministically change mood as time passes.
Predictors of Mood
Depression is among the leading causes of mental illness.1 However, only half of those who have the disorder receive treatment1. To improve outcomes, healthcare professionals must be able to identify and treat patients who are most likely to respond to specific treatments.
The treatment of depression can be personalized to help. By using sensors for mobile phones and 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 the treatments they receive. With two grants awarded totaling more than $10 million, they will make use of these technologies to identify biological and behavioral predictors of the response to antidepressant medication and psychotherapy.
The majority of research conducted to so far has focused on clinical and sociodemographic characteristics. These include demographic variables such as age, gender and education, clinical characteristics such as symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted from data in medical records, very few studies have employed longitudinal data to explore predictors of mood in individuals. Many studies do not consider the fact that moods can be very different between individuals. Therefore, it is essential to develop methods that permit the determination of individual differences in mood predictors and the effects of treatment.
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 detect patterns of behaviour and emotions that are unique to each individual.
The team also devised a machine learning algorithm to model dynamic predictors for each person's depression mood. The algorithm combines 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 scale for assessing severity of symptom. However the correlation was tinny (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely among individuals.
Predictors of symptoms
Depression is a leading reason for disability across the world1, however, it is often misdiagnosed and untreated2. Depression Treatment No Medication (Bryan-Galbraith.Mdwrite.Net) disorders are rarely treated because of the stigma that surrounds them, as well as the lack of effective interventions.
To help with personalized treatment, it is important to identify predictors of symptoms. However, the methods used to predict symptoms rely on clinical interview, which is not reliable and only detects a tiny variety of characteristics related to depression.2
Using machine learning to blend continuous digital behavioral phenotypes of a person captured by sensors on smartphones and an online mental depression treatment health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) along with other indicators of severity of symptoms could increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements as well as capture a wide range of unique behaviors and activity patterns that are difficult to record with interviews.
The study involved University of California Los Angeles students who had mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical treatment depending on their depression severity. Participants who scored a high on the CAT-DI of 35 or 65 were assigned online support with a peer coach, while those who scored 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 age, sex, education, work, and financial situation; whether they were divorced, married, or single; current suicidal ideas, intent or attempts; as well as the frequency with the frequency they consumed alcohol. Participants also scored their level of depression symptom severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI tests were conducted every other week for the participants that received online support, and once a week for those receiving in-person support.
Predictors of Treatment Response
The development of a personalized depression treatment is currently a major research area and many studies aim at identifying predictors that will enable clinicians to determine the most effective medications for each person. Pharmacogenetics, in particular, identifies genetic variations that determine the way that our bodies process drugs. This allows doctors select medications that are likely to be the most effective for each patient, while minimizing time and effort spent on trial-and-error treatments and avoiding any side negative effects.
Another approach that is promising is to develop 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, such as whether or not a particular medication will improve the mood and symptoms. These models can also be used to predict a patient's response to treatment that is already in place, allowing doctors to maximize the effectiveness of current treatment.
A new generation employs machine learning techniques such as supervised and classification algorithms such as regularized logistic regression, and tree-based methods to combine the effects of several variables and improve predictive accuracy. These models have been demonstrated to be effective in predicting outcomes of treatment like the response to antidepressants. These techniques are becoming increasingly popular in psychiatry and will likely be the norm in future clinical practice.
In addition to ML-based prediction models, research into the underlying mechanisms of depression is continuing. Recent research suggests that the disorder is connected with neural dysfunctions that affect specific circuits. This theory suggests that an individualized treatment for depression will be based on targeted treatments that restore normal function to these circuits.
Internet-based-based therapies can be an effective method to achieve this. They can provide a more tailored and individualized experience for patients. One study found that an internet-based program helped improve symptoms and provided a better quality of life for MDD patients. Furthermore, a randomized controlled study of a personalised treatment for post stroke depression treatment demonstrated an improvement in symptoms and fewer adverse effects in a significant proportion of participants.
Predictors of Side Effects
A major obstacle in individualized depression treatment involves identifying and predicting the antidepressant medications that will have very little or no side effects. Many patients are prescribed a variety medications before settling on a treatment that is effective and tolerated. Pharmacogenetics offers a fascinating new way to take an effective and precise method of selecting antidepressant therapies.
There are many predictors that can be used to determine the antidepressant that should be prescribed, such as gene variations, patient phenotypes such as gender or ethnicity, and comorbidities. However it is difficult to determine the most reliable and accurate predictors for a particular treatment is likely to require randomized controlled trials with much larger samples 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 contain only a single episode per person instead of multiple episodes spread over time.
Additionally, the estimation of a patient's response to a particular medication will likely also require information on comorbidities and symptom profiles, and the patient's previous experience with tolerability and efficacy. At present, only a handful of easily identifiable sociodemographic variables and clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.
There are many challenges to overcome when it comes to the use of pharmacogenetics to treat depression. First it is necessary to have a clear understanding of the underlying genetic mechanisms is essential and a clear definition 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. Pharmacogenetics can, in the long run help reduce stigma around mental health treatment and improve treatment outcomes. But, like any other psychiatric treatment, careful consideration and application is necessary. At present, the most effective method is to offer patients a variety of effective depression medications and encourage them to speak freely with their doctors about their experiences and concerns.
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