Name
Title | ||
|---|---|---|
Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer. |
Abstract | ||
|---|---|---|
Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classes free text pathology reports for prostate cancer into T,N and M stage groups. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/SHTI190515 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Prostate Cancer,Neoplasm Staging,Natural Language Processing | Artificial intelligence,Prostate cancer,Medicine,Machine learning | Conference |
Volume | ISSN | Citations |
264 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raphael Lenain | 1 | 0 | 0.34 |
| Martin G. Seneviratne | 2 | 0 | 0.68 |
| Selen Bozkurt | 3 | 17 | 7.22 |
| Douglas W. Blayney | 4 | 0 | 1.35 |
| James D. Brooks | 5 | 4 | 6.23 |
| T Hernandez-Boussard | 6 | 376 | 64.26 |