Abstract
This paper presents a novel architectural model approach for the extraction of strictly necessary information from Patient Summary documents. These clinical documents, synthesizing the medical history of a patient, contain heterogeneous information, characterized by different confidentiality levels. For this reason, patients should be able to define privacy rules acting on the specific information they intend to share. However, the main system approaches currently used permit a patient to specify privacy policies only at document level. Thus, a patient is obliged to deny the access to the whole document to protect his/her privacy if only one kind of information is assumed particularly sensitive. To face this problem, the proposed architectural model is provided with semantic-enhanced features with the aim of allowing patients to define access rights based on document sections or information nuggets, trying moreover to fill the semantic gap between the queries and the information present in the documents.
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References
McClellan, M., Rivlin, A.: Improving health while reducing cost growth, what is possible? Health Policy Issue Briefs. Number 1 of 7 (2014)
Pluye P., Grad R.M., Dunikowski L.G., Stephenson R.: Impact of clinical informationretrieval technology on physicians: A literature review of quantitative, qualitative and mixed methods studies. International Journal of Medical Informatics, Vol. 74(9) (2005) 745–768
epSOS project, online at: http://www.epsos.eu/ (Access date: 25 July 2016)
Dolin R.H., Alschuler L., Boyer S., Beebe C., Behlen F.M., Biron P.V., Shabo A.: HL7 Clinical Document Architecture, Release 2. Journal of the American Medical Informatics Association, Volume 13, Issue 1 (2006), 30-39
Ciampi M., De Pietro G., Esposito C., Sicuranza M., Mori P., Gebrehiwot A., Donzelli P.: On securing communications among federated health information systems, Vol. 7613 (2012) 235-246
General Data Protection Regulation, European Commission.: Regulation of the European Parliament and of the Council on the protection of individuals with regard to the processing of personal data and on the free movement of such data, online at: http://ec.europa.eu/justice/data-protection/document/review2012/com_2012_11_en.pdf (Access date: 25 July 2016)
Khan M.F.F., Sakamura K.: Context-aware access control for clinical information systems. International Conference on Innovations in Information Technology (2002) 123-128
Xu H., Stenner S.P., Doan S., Johnson K.B., Waitman L.R., Denny J.C.: MedEx: A medication information extraction system for clinical narratives, Journal of the American Medical Informatics Association (2013) 19–24
Lin Y.K., Chen H., Brown R.A.: MedTime: A temporal information extraction system for clinical narratives. Journal of Biomedical Informatics, ISSN 1532-0464, Vol. 46, Suppl. (2013) S20-S28
Liu X., Chen H.: Identifying adverse drug events from patient social media: A case study for diabetes. IEEE Intelligent Systems, Vol. 30, Issue 3 (2015) 44-51
Amato F., Greco L., Persia F., Poccia S.R., De Santo A.: Content-based multimedia retrieval. Data Management in Pervasive Systems (2015) 291-310
Al-Qahtani M., Amira A., Ramzan N.: “An efficient information retrieval technique for ehealth systems”, International Conference on Systems, Signals and Image Processing (2015) 257-260
Patrick J., Wang Y., Budd P., Recter A., Brandt S., Rogers J., Herkes R., Ryan A., Vazirnezhed B.: “Developing SNOMED CT subsets from clinical notes for intensive care service”, Health Care and Informatics Review Online, Vol. 12(3) (2008) 25-30
Zeng Q.T., Goryachev S., Weiss S., Sordo M., Murphy S.N., Lazarus E.: “Extracting principal diagnosis, co-morbidity and smoking status for asthma research: Evaluation of a natural language processing system”, Medical Informatics and Decision Making (2006)
Demner-Fushman D., Chapman W.W., McDonald C.J.: “What natural language processing do for clinical decision support?”, Journal of Biomedical Informatics (2009) 760-772
Feilmayr C.: Text mining-supported information extraction: An extended methodology for developing information extraction systems, 22nd International Workshop on Database and Expert Systems Applications, IEEE CS, (2011) 217-221
Limsopatham N., Macdonald C., Ounis I.: A task-specific query and document representation for medical records search. Advances in Information Retrieval, Lecture Notes in Computer Science, Vol. 7814 (2013) 747-751
Abril D., Navarro-Arribas G., Torra V.: Towards privacy preserving information retrieval through semantic microaggregation. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (2010) 296 – 299
Sicuranza M., Esposito A., Ciampi M.: An access control model to minimize the data exchange in the information retrieval. Journal of Ambient Intelligence and Humanized Computing (2015) 741-752
Gigante G., Gargiulo F., Ficco M.: A semantic driven approach for requirements verification. Intelligent Distributed Computing VIII Studies in Computational Intelligence, Vol. 570 (2015) 427-436
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Sicuranza, M., Esposito, A., Ciampi, M. (2017). Semantic Information Retrieval from Patient Summaries. In: Xhafa, F., Barolli, L., Amato, F. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_33
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DOI: https://doi.org/10.1007/978-3-319-49109-7_33
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