Approved Research
Exploratory study to develop a machine learning predictive algorithm by characterizing and evaluating potential digital biomarkers of disease prognosis and severity in COVID-19 infected patients
Lay summary
We will analyze healthcare data associated with COVID-tested individuals (including test results and clinical outcomes) to extract relationships between these data elements that correlate to development of a more severe disease state requiring timely and intensive medical intervention. The strengths of data feature relationships will be elucidated using machine-learning data science techniques to develop a clinical decision support alert system. This alert system will input patient data elements discovered during the research process and deemed relevant to disease severity prediction to compute a risk score for patients presenting to the clinic to help clinicians triage patients, interventions, and resources better.
Scope extension:
Original scope
- Characterize COVID-19 patients in terms of data elements routinely available in electronic health records (EHR)
- Evaluate potential ML algorithm features and their importance during development of predictive algorithm
- Evaluate performance of ML algorithm to predict COVID-19 patient disease prognosis and severity
New scope
- Characterize COVID-19 patients in terms of data elements routinely available in electronic health records (EHR)
- Evaluate potential ML algorithm features and their importance during development of predictive algorithm
- Evaluate performance of ML algorithm to predict COVID-19 patient disease prognosis and severity
4. Development of polygenic risk scores (PRS) for known co-morbidities for COVID-19 severity