Ontology-driven Vaccination Information Extraction

Increasingly, medical institutions have access to clinical information through computers. The need to process and manage the large amount of data is motivating the recent interest in semantic approaches. Data regarding vaccination records is a common in such systems. Also, being vaccination is a major area of concern in health policies, numerous information is available in the form of clinical guidelines. However, the information in these guidelines may be difficult to access and apply to a specific patient during consultation. The creation of computer interpretable representations allows the development of clinical decision support systems, improving patient care with the reduction of medical errors, increased safety and satisfaction. This paper describes the method used to model and populate a vaccination ontology and the system which recognizes vaccination information on medical texts.The system identifies relevant entities on medical texts and populates an ontology with new instances of classes. An approach to automatically extract information regarding inter-class relationships using association rule mining is suggested.