Kammarauche Aneni
Yale University
3 active projects
Events occurring before age 18 years
Scientific Questions Being Studied
The purpose of this study is to investigate social and behavioral features that are predictive of substance misuse among young adults (18-30 years) using machine learning algorithms. Aim 1: Identify electronic health record data features that predict substance use disorder, depression and anxiety among young adults Aim 2: Determine if electronic health record features that are predictive of above identified behavioral disorders differ by racial/ethnic groups using the National Institutes of Health racial-ethnic categories.
Project Purpose(s)
- Social / Behavioral
Scientific Approaches
Machine learning – through the use of large-scale electronic health records data and predictive analytics – offers an innovative approach for identifying adolescents who are at risk for future substance misuse and mental disorders. Electronic health records represent a large amount of information over the care journey of a patient and for some adolescents, data from birth is available. As such, the electronic health record is a rich dataset that allows for the use of machine learning methods to identify at-risk adolescents early. The overall aim of this study is to use electronic health record data from the AllofUs research database to develop a machine learning model that predicts adolescents struggling with mental health disorders and substance use disorders. The AllofUs database will allow us extract events recorded in the EHR prior to 18 years
Anticipated Findings
An automated method of identifying adolescents at risk has several benefits: it can allow for early identification and treatment referral which can potentially prevent the onset of future mental health problems; it can alleviate the current care burden on providers with preventive, automated screening; it can have downstream effects on reducing wait-times for outpatient mental health visits and at-capacity emergency rooms due to implementation of targeted preventive interventions early; it can reveal bias in that can exist in the identification and referral for substance use or mental health treatment and thus improve health equity.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
- Access to Care
- Income Level
Data Set Used
Controlled Tier
Duplicate of Skills Assessment Training Notebooks For Users
Scientific Questions Being Studied
This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.
Project Purpose(s)
- Educational
Scientific Approaches
There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.
Anticipated Findings
We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Kammarauche Aneni - Other, Yale University
- Hiral Master - Project Personnel, All of Us Program Operational Use
- Chenchal Subraveti - Project Personnel, All of Us Program Operational Use
- Aymone Kouame - Other, All of Us Program Operational Use
Collaborators:
- Michael Lyons - Project Personnel, All of Us Program Operational Use
- Hunter Hollis - Project Personnel, All of Us Program Operational Use
- Christopher Lord - Project Personnel, All of Us Program Operational Use
Substance misuse predictions
Scientific Questions Being Studied
The purpose of this study is to investigate social and behavioral features that are predictive of substance misuse among young adults (18-30 years) using machine learning algorithms.
Aim 1: Identify electronic health record data features that predict substance use disorder, depression and anxiety among young adults
Aim 2: Determine if electronic health record features that are predictive of above identified behavioral disorders differ by racial/ethnic groups using the National Institutes of Health racial ethnic categories.
Project Purpose(s)
- Disease Focused Research (substance misuse)
- Population Health
- Social / Behavioral
- Methods Development
Scientific Approaches
Machine learning – through the use of large-scale electronic health records data and predictive analytics – offers an innovative approach for identifying adolescents who are at risk for future substance misuse and mental disorders. Electronic health records represent a large amount of information over the care journey of a patient and for some adolescents, data from birth is available. As such, the electronic health record is a rich dataset that allows for the use of machine learning methods to identify at-risk adolescents early. The overall aim of this study is to use electronic health record data from the AllofUs research database to develop a machine learning model that predicts adolescents struggling with mental health disorders and substance use disorders among adolescents receiving care at Fair Haven.
Anticipated Findings
An automated method of identifying adolescents at risk has several benefits: it can allow for early identification and treatment referral which can potentially prevent the onset of future mental health problems; it can alleviate the current care burden on providers with preventive, automated screening; it can have downstream effects on reducing wait-times for outpatient mental health visits and at-capacity emergency rooms due to implementation of targeted preventive interventions early; it can reveal bias in that can exist in the identification and referral for substance use or mental health treatment and thus improve health equity.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
- Gender Identity
- Sexual Orientation
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Controlled TierResearch Team
Owner:
- Kammarauche Aneni - Other, Yale University
- Kakuyon Mataeh - Project Personnel, One Fact Foundation
- Jaan Altosaar - Other, One Fact Foundation
Request a Review of this Research Project
You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.
