Why We Are In Love With Personalized Depression Treatment (And You Sho…
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Personalized recurrent depression treatment Treatment
For many suffering from depression, traditional therapy and medications are not effective. The individual approach to treatment could be the answer.
Cue is an intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions to improve mental health. We examined the most effective-fitting personalized ML models to each subject, using Shapley values, in order to understand their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.
Predictors of Mood
Depression is the leading cause of mental illness in the world.1 Yet, only half of those suffering from the condition receive treatment. To improve outcomes, clinicians need to be able to identify and treat patients who have the highest probability of responding to particular treatments.
Personalized depression treatment can help. By using mobile phone sensors, 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 worth more than $10 million will be used to determine biological and behavioral indicators of response.
The majority of research on factors that predict depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographic factors like age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted from the data in medical records, very few studies have utilized longitudinal data to study predictors of mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is important to devise methods that allow for the identification and quantification of individual differences in 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 allows the team to develop algorithms that can detect distinct patterns of behavior and emotions that differ between individuals.
In addition to these modalities, the team created a machine learning algorithm to model the dynamic predictors of each person's depressed mood. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.
This digital phenotype has been associated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was weak however (Pearson r = 0,08; P-value adjusted by BH 3.55 x 10 03) and varied widely between individuals.
Predictors of symptoms
Depression is among the world's leading causes of disability1 yet it is often untreated and not diagnosed. Depression disorders are rarely treated due to the stigma attached to them and the lack of effective treatments.
To allow for individualized treatment to improve treatment, identifying the factors that predict the severity of symptoms is crucial. However, the methods used to predict symptoms rely on clinical interview, which is not reliable and only detects a tiny number of symptoms associated with depression.2
Machine learning can be used to integrate continuous digital behavioral phenotypes of a person captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory the CAT-DI) together with other predictors of severity of symptoms could increase the accuracy of diagnostics and treatment efficacy for depression. These digital phenotypes allow continuous, high-resolution measurements and capture a wide variety of unique behaviors and activity patterns that are difficult to document through interviews.
The study comprised University of California Los Angeles students with mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical treatment according to the severity of their depression. Patients with a CAT DI score of 35 65 were allocated online support via a peer coach, while those who scored 75 were routed to clinics in-person for psychotherapy.
Participants were asked a series questions at the beginning of the study concerning their demographics and psychosocial characteristics. The questions asked included age, sex and education and financial status, marital status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their level of postnatal depression treatment symptom severity on a 0-100 scale using the CAT-DI. The CAT-DI tests were conducted every other week for participants that received online support, and once a week for those receiving in-person support.
Predictors of Treatment Reaction
Research is focused on individualized depression treatment. Many studies are aimed at identifying predictors, which will help clinicians identify the most effective medications to treat each patient. Pharmacogenetics, for instance, identifies genetic variations that determine the way that our bodies process drugs. This allows doctors to select drugs that are likely to work best for each patient, minimizing the time and effort in trial-and-error treatments and avoid any adverse effects that could otherwise hinder progress.
Another promising approach is to create prediction models combining clinical data and neural imaging data. These models can be used to determine which variables are the most predictive of a specific outcome, like whether a drug will improve symptoms or mood. These models can be used to determine the patient's response to a treatment, which will help doctors maximize the effectiveness.
A new generation employs machine learning techniques like the supervised and classification algorithms, regularized logistic regression and tree-based techniques to combine the effects from multiple variables and improve predictive accuracy. These models have been proven to be effective in the prediction of treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry and it is expected that they will become the standard for future clinical practice.
Research into depression's underlying mechanisms continues, as do predictive models based on ML. Recent findings suggest that depression is connected to dysfunctions in specific neural networks. This theory suggests that the psychological treatment for depression for depression will be individualized built around targeted therapies that target these neural circuits to restore normal function.
One way to do this is by using internet-based programs which can offer an individualized and tailored experience for patients. For instance, one study found that a web-based program was more effective than standard care in reducing symptoms and ensuring an improved quality of life for patients suffering from MDD. In addition, a controlled randomized study of a customized approach to treating depression showed steady improvement and decreased adverse effects in a large proportion of participants.
Predictors of Side Effects
A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed a variety of medications before finding a medication that is effective and tolerated. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and precise.
Several predictors may be used to determine the best drug to treat anxiety and depression antidepressant to prescribe, including genetic variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. To identify the most reliable and valid predictors for a specific treatment, random controlled trials with larger samples will be required. This is due to the fact that the identification of interaction effects or moderators can be a lot more difficult in trials that take into account a single episode of treatment per participant, rather than multiple episodes of treatment over time.
Additionally to that, predicting a patient's reaction will likely require information about the comorbidities, symptoms profiles and the patient's personal perception of effectiveness and tolerability. There are currently only a few easily assessable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.
The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many hurdles to overcome. First is a thorough understanding of the genetic mechanisms is essential and a clear definition of what is a reliable predictor of treatment response. In addition, ethical concerns such as privacy and the responsible use of personal genetic information should be considered with care. Pharmacogenetics can be able to, over the long term reduce stigma associated with treatments for mental illness and improve treatment outcomes. However, as with any approach to psychiatry careful consideration and application is required. In the moment, it's recommended to provide patients with a variety of medications for depression that are effective and urge them to speak openly with their doctors.
For many suffering from depression, traditional therapy and medications are not effective. The individual approach to treatment could be the answer.
Cue is an intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions to improve mental health. We examined the most effective-fitting personalized ML models to each subject, using Shapley values, in order to understand their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.
Predictors of Mood
Depression is the leading cause of mental illness in the world.1 Yet, only half of those suffering from the condition receive treatment. To improve outcomes, clinicians need to be able to identify and treat patients who have the highest probability of responding to particular treatments.
Personalized depression treatment can help. By using mobile phone sensors, 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 worth more than $10 million will be used to determine biological and behavioral indicators of response.
The majority of research on factors that predict depression treatment effectiveness has centered on the sociodemographic and clinical aspects. These include demographic factors like age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.
While many of these aspects can be predicted from the data in medical records, very few studies have utilized longitudinal data to study predictors of mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is important to devise methods that allow for the identification and quantification of individual differences in 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 allows the team to develop algorithms that can detect distinct patterns of behavior and emotions that differ between individuals.
In addition to these modalities, the team created a machine learning algorithm to model the dynamic predictors of each person's depressed mood. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.
This digital phenotype has been associated with CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was weak however (Pearson r = 0,08; P-value adjusted by BH 3.55 x 10 03) and varied widely between individuals.
Predictors of symptoms
Depression is among the world's leading causes of disability1 yet it is often untreated and not diagnosed. Depression disorders are rarely treated due to the stigma attached to them and the lack of effective treatments.
To allow for individualized treatment to improve treatment, identifying the factors that predict the severity of symptoms is crucial. However, the methods used to predict symptoms rely on clinical interview, which is not reliable and only detects a tiny number of symptoms associated with depression.2
Machine learning can be used to integrate continuous digital behavioral phenotypes of a person captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory the CAT-DI) together with other predictors of severity of symptoms could increase the accuracy of diagnostics and treatment efficacy for depression. These digital phenotypes allow continuous, high-resolution measurements and capture a wide variety of unique behaviors and activity patterns that are difficult to document through interviews.
The study comprised University of California Los Angeles students with mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical treatment according to the severity of their depression. Patients with a CAT DI score of 35 65 were allocated online support via a peer coach, while those who scored 75 were routed to clinics in-person for psychotherapy.
Participants were asked a series questions at the beginning of the study concerning their demographics and psychosocial characteristics. The questions asked included age, sex and education and financial status, marital status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their level of postnatal depression treatment symptom severity on a 0-100 scale using the CAT-DI. The CAT-DI tests were conducted every other week for participants that received online support, and once a week for those receiving in-person support.
Predictors of Treatment Reaction
Research is focused on individualized depression treatment. Many studies are aimed at identifying predictors, which will help clinicians identify the most effective medications to treat each patient. Pharmacogenetics, for instance, identifies genetic variations that determine the way that our bodies process drugs. This allows doctors to select drugs that are likely to work best for each patient, minimizing the time and effort in trial-and-error treatments and avoid any adverse effects that could otherwise hinder progress.
Another promising approach is to create prediction models combining clinical data and neural imaging data. These models can be used to determine which variables are the most predictive of a specific outcome, like whether a drug will improve symptoms or mood. These models can be used to determine the patient's response to a treatment, which will help doctors maximize the effectiveness.
A new generation employs machine learning techniques like the supervised and classification algorithms, regularized logistic regression and tree-based techniques to combine the effects from multiple variables and improve predictive accuracy. These models have been proven to be effective in the prediction of treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry and it is expected that they will become the standard for future clinical practice.
Research into depression's underlying mechanisms continues, as do predictive models based on ML. Recent findings suggest that depression is connected to dysfunctions in specific neural networks. This theory suggests that the psychological treatment for depression for depression will be individualized built around targeted therapies that target these neural circuits to restore normal function.
One way to do this is by using internet-based programs which can offer an individualized and tailored experience for patients. For instance, one study found that a web-based program was more effective than standard care in reducing symptoms and ensuring an improved quality of life for patients suffering from MDD. In addition, a controlled randomized study of a customized approach to treating depression showed steady improvement and decreased adverse effects in a large proportion of participants.
Predictors of Side Effects
A major issue in personalizing depression treatment involves identifying and predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed a variety of medications before finding a medication that is effective and tolerated. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and precise.
Several predictors may be used to determine the best drug to treat anxiety and depression antidepressant to prescribe, including genetic variants, phenotypes of patients (e.g. sexual orientation, gender or ethnicity) and co-morbidities. To identify the most reliable and valid predictors for a specific treatment, random controlled trials with larger samples will be required. This is due to the fact that the identification of interaction effects or moderators can be a lot more difficult in trials that take into account a single episode of treatment per participant, rather than multiple episodes of treatment over time.
Additionally to that, predicting a patient's reaction will likely require information about the comorbidities, symptoms profiles and the patient's personal perception of effectiveness and tolerability. There are currently only a few easily assessable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.
The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many hurdles to overcome. First is a thorough understanding of the genetic mechanisms is essential and a clear definition of what is a reliable predictor of treatment response. In addition, ethical concerns such as privacy and the responsible use of personal genetic information should be considered with care. Pharmacogenetics can be able to, over the long term reduce stigma associated with treatments for mental illness and improve treatment outcomes. However, as with any approach to psychiatry careful consideration and application is required. In the moment, it's recommended to provide patients with a variety of medications for depression that are effective and urge them to speak openly with their doctors.
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