| Autor: |
N, Cassim, M, Mapundu, V, Olago, J A, George, D K, Glencross |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Studies in health technology and informatics. 264 |
| ISSN: |
1879-8365 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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