Validation of semantic analyses of unstructured medical data for research purposes = Validierung von semantischen Analysen von unstrukturierten medizinischen Daten für Forschungszwecke - Pokora, Roman; Le Cornet, Lucian; Daumke, Philipp; Mildenberger, Peter; Zeeb, Hajo; Blettner, Maria (Das Gesundheitswesen, 2020)
Background In secondary data there are often unstructured free texts. The aim of this study was to validate a text mining system to extract unstructured medical data for research purposes. Methods From a radiological department, 1,000 out of 7,102 CT findings were randomly selected. These were manually divided into defined groups by 2 physicians. For automated tagging and reporting, the text analysis software Averbis Extraction Platform (AEP) was used. Special features of the system are a morphological analysis for the decomposition of compound words as well as the recognition of noun phrases, abbreviations and negated statements. Based on the extracted standardized keywords, findings reports were assigned to the given findings groups using machine learning methods. To assess the reliability and validity of the automated process, the automated and two independent manual mappings were compared for matches in multiple runs. Results Manual classification was too time-consuming. In the case of automated keywording, the classification according to ICD-10 turned out to be unsuitable for our data. It also showed that the keyword search does not deliver reliable results. Computer-aided text mining and machine learning resulted in reliable results. The inter-rater reliability of the two manual classifications, as well as the machine and manual classification was very high. Both manual classifications were consistent in 93% of all findings. The kappa coefficient is 0.89 [95% confidence interval (CI) 0.87-0.92]. The automatic classification agreed with the independent, second manual classification in 86% of all findings (Kappa coefficient 0.79 [95% CI 0.75-0.81]). Discussion The classification of the software AEP was very good. In our study, however, it followed a systematic pattern. Most misclassifications were found in findings that indicate an increased risk of cancer. The free-text structure of the findings raises concerns about the feasibility of a purely automated analysis. The combination of human intellect and intelligent, adaptive software appears most suitable for mining unstructured but important textual information for research.