Applying Regular Expressions in Text Mining to Extract Information from Medical Expert Reports

With today’s technological advancements, the medical field generates a massive number of electronic medical records (EMRs) daily. These EMRs contain critical data that is primarily accessible and interpretable by healthcare experts. However, their unstructured nature presents significant challenges for analysis and interpretation. Transforming these unstructured documents into a structured format is crucial for scientific research and poses a complex challenge for data scientists. This paper presents an approach for converting unstructured textual documents into structured databases, demonstrated through a case study involving the processing of Word medical expert reports into structured data within Excel spreadsheets. These expert assessments, conducted on behalf of the National Health Insurance Fund (known as CNAM), covered various patient requests, ranging from prescribed rest to prolonged absences from work due to long-term illness or permanent incapacity. This method has the potential to significantly enhance healthcare decision-making efficiency and pave the way for further developments in medical informatics.