12 Companies That Are Leading The Way In Personalized Depression Treat…
Florence Lowrie
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3
10.25 14:52
Personalized Depression Treatment
For many people gripped by depression, traditional therapy and medications are not effective. Personalized treatment may be the answer.
Cue is an intervention platform that converts sensor data collected from smartphones into personalized micro-interventions for improving mental health. We examined the most effective-fitting personalized ML models for each individual, using Shapley values to determine their characteristic predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.
Predictors of Mood
Depression is a major cause of mental illness around the world.1 Yet the majority of people affected receive treatment. To improve outcomes, clinicians must be able to recognize and treat patients most likely to respond to specific treatments.
A customized depression treatment is one method of doing this. Using mobile phone sensors and an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods 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 techniques to determine the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.
The majority of research to so far has focused on sociodemographic and clinical characteristics. These include demographics like age, gender and education as well as clinical characteristics like severity of symptom, comorbidities and biological markers.
While many of these aspects can be predicted from information available in medical records, very few studies have utilized longitudinal data to explore predictors of mood in individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to create methods that allow the determination of the 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 will then create algorithms to detect patterns of behavior and emotions that are unique to each person.
In addition to these modalities the team developed a machine-learning algorithm that models the dynamic predictors of each person's depressed mood. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.
The digital phenotype was associated 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 among individuals.
Predictors of symptoms
Depression is among the world's leading causes of disability1, but it is often not properly diagnosed and treated. In addition an absence of effective treatments and stigma associated with depression disorders hinder many individuals from seeking help.
To assist in individualized treatment resistant depression, it is important to identify the factors that predict symptoms. Current prediction methods rely heavily on clinical interviews, which are unreliable and only identify a handful of symptoms associated with depression treatment effectiveness.
Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can be used to are able to capture a variety of distinct behaviors and activities, which are difficult to record through interviews, and allow for high-resolution, continuous measurements.
The study involved University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical care in accordance with their severity of depression. Those with a CAT-DI score of 35 65 were assigned online support via a coach and those with a score 75 were routed to in-person clinical care for psychotherapy.
Participants were asked a set of questions at the beginning of the study about their demographics and psychosocial characteristics. These included sex, age education, work, and financial situation; whether they were divorced, partnered or single; their current suicidal thoughts, intentions or attempts; and the frequency with which they drank alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale from zero to 100. The CAT-DI assessment was conducted every two weeks for those who received online support, and weekly for those who received in-person care.
Predictors of the Reaction to Treatment
Research is focusing on personalized depression treatment medications treatment. Many studies are focused on finding predictors, which can help doctors determine the most effective medications for each person. Pharmacogenetics, for instance, is a method of identifying genetic variations that affect the way that our bodies process drugs. This allows doctors to select drugs that are likely to work best for each patient, reducing the time and effort involved in trial-and-error treatments and avoiding side effects that might otherwise hinder advancement.
Another promising approach is building models of prediction using a variety of data sources, including data from clinical studies and neural imaging data. These models can be used to determine which variables are most predictive of a particular outcome, like whether a medication can improve mood or symptoms. These models can be used to predict the patient's response to a treatment, allowing doctors to maximize the effectiveness.
A new generation of studies 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 improve the accuracy of predictive. These models have been proven to be useful in predicting treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry, and it is likely that they will become the norm for future clinical practice.
In addition to the ML-based prediction models The study of the underlying mechanisms of depression continues. Recent research suggests that depression is linked to the dysfunctions of specific neural networks. This theory suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.
One way to do this is to use internet-based interventions which can offer an individualized and personalized experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality life for MDD patients. A randomized controlled study of an individualized treatment for depression found that a significant percentage of patients experienced sustained improvement and fewer side negative effects.
Predictors of Side Effects
In the treatment of depression, one of the most difficult aspects is predicting and identifying the antidepressant that will cause no or minimal side effects. Many patients are prescribed various medications before settling on a treatment that is safe and effective. Pharmacogenetics offers a fresh and exciting way to select antidepressant drugs that are more effective and precise.
There are a variety of variables that can be used to determine which antidepressant should be prescribed, including genetic variations, phenotypes of patients such as ethnicity or gender and the presence of comorbidities. To determine the most reliable and valid predictors for a particular treatment for panic attacks and depression, randomized controlled trials with larger samples will be required. This is due to the fact that it can be more difficult how to treatment depression determine the effects of moderators or interactions in trials that comprise only one episode per person rather than multiple episodes over a period of time.
Additionally, the prediction of a patient's response to a particular medication will likely also require information on symptoms and comorbidities in addition to the patient's personal experiences with the effectiveness and tolerability of the medication. Currently, only a few easily measurable sociodemographic variables as well as clinical variables seem to be reliably related to response to MDD. These include gender, age, race/ethnicity, BMI, SES and the presence of alexithymia.
Many issues remain to be resolved in the application of pharmacogenetics to treat depression treatment in pregnancy. First it is necessary to have a clear understanding of the underlying genetic mechanisms is required and a clear definition of what is a reliable indicator of treatment response. Ethics such as privacy and the responsible use genetic information should also be considered. In the long term, pharmacogenetics may provide an opportunity to reduce the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. However, as with all approaches to psychiatry, careful consideration and application is required. At present, it's ideal to offer patients various depression medications that are effective and urge patients to openly talk with their doctors.
For many people gripped by depression, traditional therapy and medications are not effective. Personalized treatment may be the answer.
Cue is an intervention platform that converts sensor data collected from smartphones into personalized micro-interventions for improving mental health. We examined the most effective-fitting personalized ML models for each individual, using Shapley values to determine their characteristic predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.Predictors of Mood
Depression is a major cause of mental illness around the world.1 Yet the majority of people affected receive treatment. To improve outcomes, clinicians must be able to recognize and treat patients most likely to respond to specific treatments.
A customized depression treatment is one method of doing this. Using mobile phone sensors and an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods 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 techniques to determine the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.
The majority of research to so far has focused on sociodemographic and clinical characteristics. These include demographics like age, gender and education as well as clinical characteristics like severity of symptom, comorbidities and biological markers.
While many of these aspects can be predicted from information available in medical records, very few studies have utilized longitudinal data to explore predictors of mood in individuals. They have not taken into account the fact that mood varies significantly between individuals. Therefore, it is crucial to create methods that allow the determination of the 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 will then create algorithms to detect patterns of behavior and emotions that are unique to each person.
In addition to these modalities the team developed a machine-learning algorithm that models the dynamic predictors of each person's depressed mood. The algorithm integrates the individual differences to produce a unique "digital genotype" for each participant.
The digital phenotype was associated 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 among individuals.
Predictors of symptoms
Depression is among the world's leading causes of disability1, but it is often not properly diagnosed and treated. In addition an absence of effective treatments and stigma associated with depression disorders hinder many individuals from seeking help.
To assist in individualized treatment resistant depression, it is important to identify the factors that predict symptoms. Current prediction methods rely heavily on clinical interviews, which are unreliable and only identify a handful of symptoms associated with depression treatment effectiveness.
Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes gathered from smartphones with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can be used to are able to capture a variety of distinct behaviors and activities, which are difficult to record through interviews, and allow for high-resolution, continuous measurements.
The study involved University of California Los Angeles students who had mild to severe depression symptoms who were taking part in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical care in accordance with their severity of depression. Those with a CAT-DI score of 35 65 were assigned online support via a coach and those with a score 75 were routed to in-person clinical care for psychotherapy.
Participants were asked a set of questions at the beginning of the study about their demographics and psychosocial characteristics. These included sex, age education, work, and financial situation; whether they were divorced, partnered or single; their current suicidal thoughts, intentions or attempts; and the frequency with which they drank alcohol. The CAT-DI was used to rate the severity of depression symptoms on a scale from zero to 100. The CAT-DI assessment was conducted every two weeks for those who received online support, and weekly for those who received in-person care.
Predictors of the Reaction to Treatment
Research is focusing on personalized depression treatment medications treatment. Many studies are focused on finding predictors, which can help doctors determine the most effective medications for each person. Pharmacogenetics, for instance, is a method of identifying genetic variations that affect the way that our bodies process drugs. This allows doctors to select drugs that are likely to work best for each patient, reducing the time and effort involved in trial-and-error treatments and avoiding side effects that might otherwise hinder advancement.
Another promising approach is building models of prediction using a variety of data sources, including data from clinical studies and neural imaging data. These models can be used to determine which variables are most predictive of a particular outcome, like whether a medication can improve mood or symptoms. These models can be used to predict the patient's response to a treatment, allowing doctors to maximize the effectiveness.
A new generation of studies 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 improve the accuracy of predictive. These models have been proven to be useful in predicting treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry, and it is likely that they will become the norm for future clinical practice.
In addition to the ML-based prediction models The study of the underlying mechanisms of depression continues. Recent research suggests that depression is linked to the dysfunctions of specific neural networks. This theory suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.
One way to do this is to use internet-based interventions which can offer an individualized and personalized experience for patients. A study showed that an internet-based program improved symptoms and provided a better quality life for MDD patients. A randomized controlled study of an individualized treatment for depression found that a significant percentage of patients experienced sustained improvement and fewer side negative effects.
Predictors of Side Effects
In the treatment of depression, one of the most difficult aspects is predicting and identifying the antidepressant that will cause no or minimal side effects. Many patients are prescribed various medications before settling on a treatment that is safe and effective. Pharmacogenetics offers a fresh and exciting way to select antidepressant drugs that are more effective and precise.
There are a variety of variables that can be used to determine which antidepressant should be prescribed, including genetic variations, phenotypes of patients such as ethnicity or gender and the presence of comorbidities. To determine the most reliable and valid predictors for a particular treatment for panic attacks and depression, randomized controlled trials with larger samples will be required. This is due to the fact that it can be more difficult how to treatment depression determine the effects of moderators or interactions in trials that comprise only one episode per person rather than multiple episodes over a period of time.
Additionally, the prediction of a patient's response to a particular medication will likely also require information on symptoms and comorbidities in addition to the patient's personal experiences with the effectiveness and tolerability of the medication. Currently, only a few easily measurable sociodemographic variables as well as clinical variables seem to be reliably related to response to MDD. These include gender, age, race/ethnicity, BMI, SES and the presence of alexithymia.
Many issues remain to be resolved in the application of pharmacogenetics to treat depression treatment in pregnancy. First it is necessary to have a clear understanding of the underlying genetic mechanisms is required and a clear definition of what is a reliable indicator of treatment response. Ethics such as privacy and the responsible use genetic information should also be considered. In the long term, pharmacogenetics may provide an opportunity to reduce the stigma that surrounds mental health treatment and improve the outcomes of those suffering with depression. However, as with all approaches to psychiatry, careful consideration and application is required. At present, it's ideal to offer patients various depression medications that are effective and urge patients to openly talk with their doctors.
