Debbie Morreall
Biographic Data
| ID | 7876131 |
|---|---|
| NAME | Debbie Morreall |
| GIVEN NAMES | Debbie |
| FAMILY NAME | Morreall |
| SIGNATURE | MORREALL D |
| AFFILIATIONS | University of Utah |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 1 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning
The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years' observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival at L1 initiation. We grouped 523 observations and 1933 variables from a nationwide cohort of FL patients diagnosed 2006-2014 in the Ve…
No prominent works on this page.
Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning
The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years' observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival at L1 initiation. We grouped 523 observations and 1933 variables from a nationwide cohort of FL patients diagnosed 2006-2014 in the Ve…
Artificial Intelligence (1 work) · Cohort (1 work) · Colorectal Cancer Screening and Detection (1 work) · Computer Science (1 work) · Electronic health record (1 work) · Follicular lymphoma (1 work) · Global Cancer Incidence and Screening (1 work) · Health care (1 work) · Health records (1 work) · Internal Medicine (1 work)