Kelli M Rasmussen
Biographic Data
| ID | 6636767 |
|---|---|
| NAME | Kelli M Rasmussen |
| GIVEN NAMES | Kelli M |
| FAMILY NAME | Rasmussen |
| SIGNATURE | RASMUSSEN K M |
| AFFILIATIONS | Huntsman Cancer Institute |
| ORCID | 0000-0002-2946-9211 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| 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…
Is Cancer Protective for Subsequent Alzheimer’s Disease Risk? Evidence From the Utah Population Database
Careful consideration of model specification and the profound effects of mortality selection in the older adult population is essential when investigating the relationship between aging-related diseases such as cancer and AD. We show that cancer does not provide protection from AD as previously described in the literature. Social scientists seeking to understand social disparities in disease outcomes among older adults may therefore want to stron…
No prominent works on this page.
Is Cancer Protective for Subsequent Alzheimer’s Disease Risk? Evidence From the Utah Population Database
Careful consideration of model specification and the profound effects of mortality selection in the older adult population is essential when investigating the relationship between aging-related diseases such as cancer and AD. We show that cancer does not provide protection from AD as previously described in the literature. Social scientists seeking to understand social disparities in disease outcomes among older adults may therefore want to stron…
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…
Internal Medicine (2 works) · Medicine (2 works) · Alzheimer's disease (1 work) · Artificial Intelligence (1 work) · Cancer (1 work) · Cancer survivorship and care (1 work) · Cancer-related cognitive impairment studies (1 work) · Cohort (1 work) · Colorectal Cancer Screening and Detection (1 work) · Computer Science (1 work)