Aaron Boussina

Graduate Trainee, University of California, San Diego

3 active projects

MED264_AKI_Readmission_Group

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation &…

Scientific Questions Being Studied

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation & follow-up for high-risk patients.

Project Purpose(s)

  • Disease Focused Research (Acute Kidney Injury)
  • Educational

Scientific Approaches

We'll be analyzing subsequent visits and looking at a range of features including vital signs, laboratory measurements, and survey responses. We anticipate we'll be using the PERSON, VISIT_OCCURRENCE, MEASUREMENT, and OBSERVATION tables. From these features we'll apply logistic regression and deep learning to build a predictive model for readmission risk. We'll identify the corresponding importance of features using odd-ratios or SHAP values.

Anticipated Findings

We anticipate that we'll be able to achieve a performant predictive model for the readmission of patients with AKI. To our knowledge this is the first such study conducted on All of Us.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Job Shiach - Graduate Trainee, University of California, San Diego
  • Aaron Boussina - Graduate Trainee, University of California, San Diego

MED264_AKI_Readmission

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation &…

Scientific Questions Being Studied

We'd like to study what features (including social determinants of health) contribute to readmission in patients with acute kidney injury (AKI). Understanding these features is important since the ability to proactively identify readmission risk could lead to improved consultation & follow-up for high-risk patients.

Project Purpose(s)

  • Disease Focused Research (Acute Kidney Injury)
  • Educational

Scientific Approaches

We'll be analyzing subsequent visits and looking at a range of features including vital signs, laboratory measurements, and survey responses. We anticipate we'll be using the PERSON, VISIT_OCCURRENCE, MEASUREMENT, and OBSERVATION tables. From these features we'll apply logistic regression and deep learning to build a predictive model for readmission risk. We'll identify the corresponding importance of features using odd-ratios or SHAP values.

Anticipated Findings

We anticipate that we'll be able to achieve a performant predictive model for the readmission of patients with AKI. To our knowledge this is the first such study conducted on All of Us.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Aaron Boussina - Graduate Trainee, University of California, San Diego

Collaborators:

  • Fatemeh Amrollahi - Graduate Trainee, University of California, San Diego

MED299

We intend to use this dataset in MED299 at UC San Diego in order to teach students about cloud computing and informatics.

Scientific Questions Being Studied

We intend to use this dataset in MED299 at UC San Diego in order to teach students about cloud computing and informatics.

Project Purpose(s)

  • Disease Focused Research (Sepsis )
  • Educational

Scientific Approaches

Sepsis is one of the country’s most urgent systemic health threats. Each year, more than 1.5 million people in the U.S. get sepsis. Sepsis kills a quarter-million Americans each year. Sepsis can occur when a trauma or an infection – often caused by a superbug or drug-resistant bacteria in the skin, lungs or urinary tract – triggers a chain reaction throughout the body. Without timely recognition and treatment, it can rapidly cause tissue damage, organ failure, and death; each hour treatment of sepsis is delayed mortality risk increases by an additional 4%. One in three patients who die in a hospital have sepsis. We intend to use regression analysis to study the factors that contribute to sepsis readmission.

Anticipated Findings

Successful prevention and management of sepsis, septic shock, and organ injury rely on the ability of clinicians to anticipate and estimate the risk, and administer the right life-saving treatments (e.g., antibiotics, fluids and vasopressors) at the right time.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Zaid Yousif - Research Fellow, University of California, San Diego
  • Supreeth Shashikumar - Research Fellow, University of California, San Diego
  • Shamim Nemati - Early Career Tenure-track Researcher, University of California, San Diego
  • Fatemeh Amrollahi - Graduate Trainee, University of California, San Diego
  • Aaron Boussina - Graduate Trainee, University of California, San Diego
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