Show simple item record

dc.contributor.advisorAziz Boxwala.en_US
dc.contributor.authorStephen, Reejis, 1977-en_US
dc.contributor.otherHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.date.accessioned2005-09-27T18:11:59Z
dc.date.available2005-09-27T18:11:59Z
dc.date.copyright2004en_US
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/28760
dc.descriptionThesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2004.en_US
dc.descriptionIncludes bibliographical references (leaves 66-67).en_US
dc.description.abstractIn order to automate data extraction from electronic medical documents, it is important to identify the correct context of the extracted information. Context in medical documents is provided by the layout of documents, which are partitioned into sections by virtue of a medical culture instilled through common practice and the training of physicians. Unfortunately, formatting and labeling is inconsistently adhered to in practice and human experts are usually required to identify sections in medical documents. A series of experiments tested the hypothesis that section identification independent of the label on sections could be achieved by using a neural network to elucidate relationships between features of sections (like size, position from start of the document) and the content characteristic of certain sections (subject-specific strings). Results showed that certain sections can be reliably identified using two different methods, and described the costs involved. The stratification of documents by document type (such as History and Physical Examination Documents or Discharge Summaries), patient diagnoses and department influenced the accuracy of identification. Future improvements suggested by the results in order to fully outline the approach were described.en_US
dc.description.statementofresponsibilityby Reejis Stephen.en_US
dc.format.extent144 leavesen_US
dc.format.extent4712642 bytes
dc.format.extent4731797 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.titleContext identification in electronic medical recordsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.identifier.oclc59823293en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record