Kammarauche Aneni

Yale University

4 active projects

Machine learning methods for substance use detections

Can we use machine learning approaches to detect substance use among individuals based on EHR and wearable data?

Scientific Questions Being Studied

Can we use machine learning approaches to detect substance use among individuals based on EHR and wearable data?

Project Purpose(s)

  • Population Health
  • Methods Development

Scientific Approaches

Long short term memory and natural language processing, (e.g. Clinical Bert pretrained models will be used for the classification between substance use or not).

Anticipated Findings

LSTM or NLP model can be effective for substance use detections.

Models built with EHR data and/or wearable data are effective.

Wearable data help us improve the results of models based on EHR data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Events occurring before age 18 years

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,…

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

Research Team

Owner:

Duplicate of Skills Assessment Training Notebooks For Users

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…

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 Tier

Research Team

Owner:

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

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,…

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 Tier

Research Team

Owner:

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