Using text mining techniques to extract prostate cancer predictive information (Gleason score) from semi-structured narrative laboratory reports in the Gauteng province, South Africa

BMC Medical Informatics and Decision Making

Jaya Anna George¹  Naseem Cassim²  Deborah Kim Glencross²  Victor Olago³  Turgay Celik⁴  Michael Mapundu⁵ 

[1] Department of Chemical Pathology, Faculty of Health Sciences, University of Witwatersrand and National Health Laboratory Service (NHLS), 7 York Road, Parktown, Johannesburg, South Africa;Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of Witwatersrand and National Health Laboratory Service (NHLS), 7 York Road, Parktown, Johannesburg, South Africa;National Health Laboratory Service (NHLS), National Cancer Registry (NCR), 1 Modderfontein Road, Sandringham, Johannesburg, South Africa;School of Electrical & Information Engineering and Wits Institute of Data Science, University of Witwatersrand, 1 Jan Smuts Avenue, Braamfontein, Johannesburg, South Africa;School of Public Health, Faculty of Health Sciences, University of Witwatersrand, 7 York Road, Parktown, Johannesburg, South Africa;

关键词: Prostate cancer;    Gleason score;    Late presentation;    Text mining;    Algorithm;    Public health;   

DOI  :  10.1186/s12911-021-01697-2

来源: Springer

【 摘 要 】

BackgroundProstate cancer (PCa) is the leading male neoplasm in South Africa with an age-standardised incidence rate of 68.0 per 100,000 population in 2018. The Gleason score (GS) is the strongest predictive factor for PCa treatment and is embedded within semi-structured prostate biopsy narrative reports. The manual extraction of the GS is labour-intensive. The objective of our study was to explore the use of text mining techniques to automate the extraction of the GS from irregularly reported text-intensive patient reports.MethodsWe used the associated Systematized Nomenclature of Medicine clinical terms morphology and topography codes to identify prostate biopsies with a PCa diagnosis for men aged > 30 years between 2006 and 2016 in the Gauteng Province, South Africa. We developed a text mining algorithm to extract the GS from 1000 biopsy reports with a PCa diagnosis from the National Health Laboratory Service database and validated the algorithm using 1000 biopsies from the private sector. The logical steps for the algorithm were data acquisition, pre-processing, feature extraction, feature value representation, feature selection, information extraction, classification, and discovered knowledge. We evaluated the algorithm using precision, recall and F-score. The GS was manually coded by two experts for both datasets. The top five GS were reported, with the remaining scores categorised as “Other” for both datasets. The percentage of biopsies with a high-risk GS (≥ 8) was also reported.ResultsThe first output reported an F-score of 0.99 that improved to 1.00 after the algorithm was amended (the GS reported in clinical history was ignored). For the validation dataset, an F-score of 0.99 was reported. The most commonly reported GS were 5 + 4 = 9 (17.6%), 3 + 3 = 6 (17.5%), 4 + 3 = 7 (16.4%), 3 + 4 = 7 (14.7%) and 4 + 4 = 8 (14.2%). For the validation dataset, the most commonly reported GS were: (i) 3 + 3 = 6 (37.7%), (ii) 3 + 4 = 7 (19.4%), (iii) 4 + 3 = 7 (14.9%), (iv) 4 + 4 = 8 (10.0%) and (v) 4 + 5 = 9 (7.4%). A high-risk GS was reported for 31.8% compared to 17.4% for the validation dataset.ConclusionsWe demonstrated reliable extraction of information about GS from narrative text-based patient reports using an in-house developed text mining algorithm. A secondary outcome was that late presentation could be assessed.

【 授权许可】

CC BY