This article describes information retrieval, natural language processing and text mining of electronic patient record text, also called clinical text. Clinical text is written by physicians and nurses to document the health care process of the patient. First we describe some characteristics of clinical text, followed by the automatic preprocessing of the text that is necessary for making it usable for some applications. We also describe some applications for clinicians including spelling and grammar checking, ICD-10 diagnosis code assignment, as well as other applications for hospital management such as ICD-10 diagnosis code validation and detection of adverse events such as hospital acquired infections. Part of the preprocessing makes the clinical text useful for faceted search, although clinical text already has some keys for performing faceted search such as gender, age, ICD-10 diagnosis codes, ATC drug codes, etc. Preprocessing makes use of ICD-10 codes and the SNOMED-CT textual descriptions. ICD-10 codes and SNOMED-CT are available in several languages and can be considered the modern Greek or Latin of medical language. The basic research presented here has its roots in the challenges described by the health care sector. These challenges have been partially solved in academia, and we believe the solutions will be adapted to the health care sector in real world applications.
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- Clinical Text Retrieval - An Overview of Basic Building Blocks and Applications
- Springer International Publishing