Privacy, information technology, and health care
Communications of the ACM
Matching records in a national medical patient index
Communications of the ACM
Modern Information Retrieval
IBHIS: Integration Broker for Heterogeneous Information Sources
COMPSAC '03 Proceedings of the 27th Annual International Conference on Computer Software and Applications
Dynamic Data Integration Using Web Services
ICWS '04 Proceedings of the IEEE International Conference on Web Services
Knowledge-based query expansion to support scenario-specific retrieval of medical free text
Proceedings of the 2005 ACM symposium on Applied computing
Telemedical information systems
IEEE Transactions on Information Technology in Biomedicine
Meeting medical terminology needs-the ontology-enhanced Medical Concept Mapper
IEEE Transactions on Information Technology in Biomedicine
Aggregating evidence from hospital departments to improve medical records search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Learning to handle negated language in medical records search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Semantic concept-enriched dependence model for medical information retrieval
Journal of Biomedical Informatics
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There are currently many active movements towards computerizing patient healthcare information. As Electronic Medical Record (EMR) systems are being increasingly adopted in healthcare facilities, however, there is a big challenge in effectively utilizing this massive information source. It is very time-consuming for healthcare providers to dig into the voluminous medical records of a patient to find the few that are indeed relevant to the patient's current problem. Due to the complex semantic relationships among medical concepts and use of many synonyms, antonyms, and hypernym/hyponym, simple word-based information retrieval does not produce satisfactory results. In this paper, we propose an EMR retrieval system that leverages semantic query expansion to retrieve medical records that are relevant to the patient's current symptom/problem. The proposed framework integrates various technologies, including information retrieval, domain ontologies, automatic semantic relationship learning, as well as a body of domain knowledge elicited from healthcare experts. Knowledge of semantic relationships among medical concepts, such as symptoms, exams and tests, diagnoses, and treatments, as well as knowledge of synonyms and hypernym/hyponyms, is used to expand and enhance initial queries posed by a user. We have implemented a preliminary prototype and conducted a pilot testing using sample nursing notes drawn from the EMR system of a community health center.