Ahmad Halwani
Biographic Data
| ID | 5539537 |
|---|---|
| NAME | Ahmad Halwani |
| GIVEN NAMES | Ahmad |
| FAMILY NAME | Halwani |
| SIGNATURE | HALWANI A |
| AFFILIATIONS | University of Utah |
| ORCID | 0000-0001-6183-6491 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| 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…
Women With Breast Cancer in the Veterans Health Administration
An increasing number of women are being cared for within the Veterans Health Administration (VA). However, the demographics and trends of women with breast cancer at the VA has not been documented. We describe the demographics and breast cancer characteristics of the 4445 women enrolled in the VA and reported to the Department of Veterans Affairs Central Cancer Registry diagnosed with breast cancer from 1995 to 2012. The cases of breast cancer pe…
No prominent works on this page.
Women With Breast Cancer in the Veterans Health Administration
An increasing number of women are being cared for within the Veterans Health Administration (VA). However, the demographics and trends of women with breast cancer at the VA has not been documented. We describe the demographics and breast cancer characteristics of the 4445 women enrolled in the VA and reported to the Department of Veterans Affairs Central Cancer Registry diagnosed with breast cancer from 1995 to 2012. The cases of breast cancer pe…
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…
Global Cancer Incidence and Screening (2 works) · Health care (2 works) · Internal Medicine (2 works) · Medicine (2 works) · Oncology (2 works) · Artificial Intelligence (1 work) · Breast cancer (1 work) · Cancer (1 work) · Cancer Risks and Factors (1 work) · Cohort (1 work)