Linguistic rule-based methods for the extraction of medical summaries to benefit patients progression tracking

Clinical narratives contain useful information that can complement the patient progress records which are obtained throughout the patient’s medical and treatment duration. In order to understand the clinical narratives content, medical concepts that include events and temporal information should be performed. This study addresses this issue based on a linguistic rule-based approach which combines domain knowledge, extraction modules and temporal linker component. This is in contrast to the fundamentals adopted by the major works based on machine learning. The proposed work’s performance is therefore evaluated against a machine learning based approach and a knowledge intensive approach. Results have shown its strength regardless of its different nature

Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning