Name
Title | ||
|---|---|---|
Using Big Data Techniques to Improve Prostate Cancer Reporting in the Gauteng Province, South Africa. |
Abstract | ||
|---|---|---|
Prostate cancer (PCa) data is of public health importance in South Africa. Biopsy data is recorded as semi-structured narrative text that is not easily analysed. Our study reports a pilot study that applied predictive analytics and text mining techniques to extract prognostic information that guides patient management. In particular, the Gleason score (GS) reported in a number of formats were extracted successfully. Our study reports that predominantly older men were diagnosed with PCa reporting a high-risk GS (8-10). Where cell differentiation was reported, 64% of biopsies reported poor differentiation. The approaches demonstrated in our study should be extended to a larger dataset to assess whether it has the potential to scale up to the national level. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/SHTI190472 | Studies in Health Technology and Informatics |
Keywords | DocType | Volume |
Prostate cancer,Gleason score,Risk,Cell differentiation | Conference | 264 |
ISSN | Citations | PageRank |
0926-9630 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naseem Cassim | 1 | 1 | 1.36 |
| M Mapundu | 2 | 0 | 0.34 |
| V Olago | 3 | 0 | 0.34 |
| J A George | 4 | 0 | 0.34 |
| Deborah K Glencross | 5 | 1 | 1.50 |