12 Companies Leading The Way In Personalized Depression Treatment
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WriterAnnie
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Date24.09.15
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Personalized Depression Treatment
Traditional treatment and medications don't work for a majority of patients suffering from depression. A customized treatment may be the solution.
Cue is a digital intervention platform that converts passively collected sensor data from smartphones into customized micro-interventions designed to improve mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to understand their predictors of feature and reveal distinct features that deterministically change mood over time.
Predictors of Mood
Depression is one of the leading causes of mental illness.1 However, only half of those who have the condition receive treatment1. In order to improve outcomes, healthcare professionals must be able to recognize and treat patients who have the highest probability of responding to particular treatments.
A customized depression shock treatment for depression is one method of doing this. Using 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 natural ways to treat depression and anxiety to predict which patients will benefit from the treatments they receive. With two grants awarded totaling over $10 million, they will use these technologies to identify biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.
The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include demographics such as age, gender, and education, as well as clinical aspects like severity of symptom and comorbidities as well as biological markers.
Very few studies have used longitudinal data in order to predict mood in individuals. A few studies also consider the fact that moods can be very different between individuals. It is therefore important to develop methods which permit the analysis and measurement of personal differences between mood predictors and treatment effects, for instance.
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 behavior and emotions that are unique to each individual.
The team also created an algorithm for machine learning to model dynamic predictors for each person's mood for depression. The algorithm integrates the individual differences to produce 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. The correlation was not strong, however (Pearson r = 0,08, BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals.
Predictors of Symptoms
Depression is a leading cause of disability around the world1, but it is often not properly diagnosed and treated. In addition, a lack of effective interventions and stigmatization associated with depressive disorders prevent many from seeking treatment.
To aid in the development of a personalized treatment plan, identifying patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few symptoms associated with depression.
Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes collected from smartphone sensors with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes are able to capture a variety of distinct behaviors and activities that are difficult to capture through interviews, and also allow for high-resolution, continuous measurements.
The study involved University of California Los Angeles students who had mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online ect for treatment resistant depression support or to clinical treatment according to the severity of their depression. Those with a score on the CAT DI of 35 or 65 were assigned online support via an instructor and those with scores of 75 were routed to clinics in-person for psychotherapy.
At the beginning of the interview, participants were asked an array of questions regarding their personal demographics and psychosocial features. The questions included education, age, sex and gender, financial status, marital status, whether they were divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. Participants also rated their degree of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI test was conducted every two weeks for participants who received online support and weekly for those who received in-person support.
Predictors of Treatment Reaction
A customized treatment for depression is currently a research priority and many studies aim to identify predictors that allow clinicians to identify the most effective medications for each person. Pharmacogenetics, for instance, identifies genetic variations that determine how the body's metabolism reacts to drugs. This allows doctors to select drugs that are likely to work best for each patient, while minimizing the time and effort involved in trial-and-error procedures and eliminating any side effects that could otherwise hinder advancement.
Another promising method is to construct models for prediction using multiple data sources, combining data from clinical studies and neural imaging data. These models can be used to determine which variables are most likely to predict a specific outcome, like whether a medication can help with symptoms or mood. These models can be used to determine the patient's response to a treatment, which will help doctors to maximize the effectiveness of their treatment.
A new generation employs machine learning techniques such as algorithms for classification and supervised learning, regularized logistic regression and tree-based techniques to combine the effects of several variables and increase the accuracy of predictions. These models have proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to be the norm in future treatment.
In addition to prediction models based on ML, research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is related to the dysfunctions of specific neural networks. This theory suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.
Internet-based interventions are an option to accomplish this. They can provide an individualized and tailored experience for patients. One study found that a web-based program was more effective than standard care in reducing symptoms and ensuring a better quality of life for those with MDD. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed steady improvement and decreased adverse effects in a large proportion of participants.
Predictors of side effects
A major obstacle in individualized depression treatment is predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed various medications before finding a medication that is safe and effective. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and specific.
Several predictors may be used to determine which antidepressant to prescribe, including gene variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. However finding the most reliable and reliable factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials with considerably larger samples than those that are typically part of clinical trials. This is due to the fact that the identification of interactions or moderators may be much more difficult in trials that focus on a single instance of treatment per person instead of multiple episodes of treatment over a period of time.
Furthermore, predicting a patient's response will likely require information on comorbidities, symptom profiles and the patient's own perception of effectiveness and tolerability. Currently, only a few easily identifiable sociodemographic variables and clinical variables seem to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.
There are many challenges to overcome in the use of pharmacogenetics to treat depression. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, as well as an understanding of a reliable indicator of the response to treatment. Ethics, such as privacy, and the responsible use of genetic information must also be considered. Pharmacogenetics can be able to, over the long term reduce stigma associated with treatments for mental illness and improve the quality of treatment. But, like all approaches to psychiatry, careful consideration and application is necessary. The best method is to provide patients with various effective depression medications and encourage them to speak openly with their doctors about their experiences and concerns.
Traditional treatment and medications don't work for a majority of patients suffering from depression. A customized treatment may be the solution.
Cue is a digital intervention platform that converts passively collected sensor data from smartphones into customized micro-interventions designed to improve mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to understand their predictors of feature and reveal distinct features that deterministically change mood over time.
Predictors of Mood
Depression is one of the leading causes of mental illness.1 However, only half of those who have the condition receive treatment1. In order to improve outcomes, healthcare professionals must be able to recognize and treat patients who have the highest probability of responding to particular treatments.
A customized depression shock treatment for depression is one method of doing this. Using 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 natural ways to treat depression and anxiety to predict which patients will benefit from the treatments they receive. With two grants awarded totaling over $10 million, they will use these technologies to identify biological and behavioral predictors of responses to antidepressant medications as well as psychotherapy.
The majority of research conducted to so far has focused on sociodemographic and clinical characteristics. These include demographics such as age, gender, and education, as well as clinical aspects like severity of symptom and comorbidities as well as biological markers.
Very few studies have used longitudinal data in order to predict mood in individuals. A few studies also consider the fact that moods can be very different between individuals. It is therefore important to develop methods which permit the analysis and measurement of personal differences between mood predictors and treatment effects, for instance.
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 behavior and emotions that are unique to each individual.
The team also created an algorithm for machine learning to model dynamic predictors for each person's mood for depression. The algorithm integrates the individual differences to produce 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. The correlation was not strong, however (Pearson r = 0,08, BH adjusted P-value 3.55 x 10 03) and varied significantly between individuals.
Predictors of Symptoms
Depression is a leading cause of disability around the world1, but it is often not properly diagnosed and treated. In addition, a lack of effective interventions and stigmatization associated with depressive disorders prevent many from seeking treatment.
To aid in the development of a personalized treatment plan, identifying patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few symptoms associated with depression.
Machine learning can increase the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes collected from smartphone sensors with a validated mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). These digital phenotypes are able to capture a variety of distinct behaviors and activities that are difficult to capture through interviews, and also allow for high-resolution, continuous measurements.
The study involved University of California Los Angeles students who had mild to severe depression symptoms who were participating in the Screening and Treatment for Anxiety and Depression program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online ect for treatment resistant depression support or to clinical treatment according to the severity of their depression. Those with a score on the CAT DI of 35 or 65 were assigned online support via an instructor and those with scores of 75 were routed to clinics in-person for psychotherapy.
At the beginning of the interview, participants were asked an array of questions regarding their personal demographics and psychosocial features. The questions included education, age, sex and gender, financial status, marital status, whether they were divorced or not, current suicidal thoughts, intent or attempts, and how often they drank. Participants also rated their degree of depression severity on a scale of 0-100 using the CAT-DI. The CAT-DI test was conducted every two weeks for participants who received online support and weekly for those who received in-person support.
Predictors of Treatment Reaction
A customized treatment for depression is currently a research priority and many studies aim to identify predictors that allow clinicians to identify the most effective medications for each person. Pharmacogenetics, for instance, identifies genetic variations that determine how the body's metabolism reacts to drugs. This allows doctors to select drugs that are likely to work best for each patient, while minimizing the time and effort involved in trial-and-error procedures and eliminating any side effects that could otherwise hinder advancement.
Another promising method is to construct models for prediction using multiple data sources, combining data from clinical studies and neural imaging data. These models can be used to determine which variables are most likely to predict a specific outcome, like whether a medication can help with symptoms or mood. These models can be used to determine the patient's response to a treatment, which will help doctors to maximize the effectiveness of their treatment.
A new generation employs machine learning techniques such as algorithms for classification and supervised learning, regularized logistic regression and tree-based techniques to combine the effects of several variables and increase the accuracy of predictions. These models have proven to be effective in forecasting treatment outcomes, such as the response to antidepressants. These approaches are becoming more popular in psychiatry, and are likely to be the norm in future treatment.
In addition to prediction models based on ML, research into the underlying mechanisms of depression is continuing. Recent research suggests that depression is related to the dysfunctions of specific neural networks. This theory suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal function to these circuits.
Internet-based interventions are an option to accomplish this. They can provide an individualized and tailored experience for patients. One study found that a web-based program was more effective than standard care in reducing symptoms and ensuring a better quality of life for those with MDD. Furthermore, a randomized controlled trial of a personalized approach to treating depression showed steady improvement and decreased adverse effects in a large proportion of participants.
Predictors of side effects
A major obstacle in individualized depression treatment is predicting which antidepressant medications will have the least amount of side effects or none at all. Many patients are prescribed various medications before finding a medication that is safe and effective. Pharmacogenetics provides a novel and exciting way to select antidepressant drugs that are more effective and specific.
Several predictors may be used to determine which antidepressant to prescribe, including gene variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. However finding the most reliable and reliable factors that can predict the effectiveness of a particular treatment is likely to require randomized controlled trials with considerably larger samples than those that are typically part of clinical trials. This is due to the fact that the identification of interactions or moderators may be much more difficult in trials that focus on a single instance of treatment per person instead of multiple episodes of treatment over a period of time.
Furthermore, predicting a patient's response will likely require information on comorbidities, symptom profiles and the patient's own perception of effectiveness and tolerability. Currently, only a few easily identifiable sociodemographic variables and clinical variables seem to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.
There are many challenges to overcome in the use of pharmacogenetics to treat depression. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, as well as an understanding of a reliable indicator of the response to treatment. Ethics, such as privacy, and the responsible use of genetic information must also be considered. Pharmacogenetics can be able to, over the long term reduce stigma associated with treatments for mental illness and improve the quality of treatment. But, like all approaches to psychiatry, careful consideration and application is necessary. The best method is to provide patients with various effective depression medications and encourage them to speak openly with their doctors about their experiences and concerns.