Annotation of clinical datasets using composite information models
type: CreativeWork
description: Annotation of clinical datasets is a process of discovery of clinical concepts and their association with machine readable constructs. The purpose of the process is to expose clinical information held in the datasets to automated agents in a structured format that facilitates and accelerates processes like document comparison, search or decision support. Numerous annotation techniques currently exist and techniques vary from simple matching to words from a reference dictionary to complex supervised machine learning techniques. The results vary, there is no gold standard and the community constantly produces new techniques. We developed an annotation technique that utilises ontological features of SNOMED-CT in discovering complex concepts contained in clinical documents and transforming them using term expansion into composite information models that reveal semantics of the messages conveyed in the document. Our algorithm converts clinical documents into segments of ontology of reality and weights the concepts found based on their frequency and location in the expanded structure. The algorithm utilises term expansion methods and pre-defined weights and compares the similarities of the resulting structures by measuring distances between their x highest weight-bearing concepts based on the position of those concepts in the ontology of reality’s graph structure represented by SNOMED CT. We test the effectiveness of our algorithm by comparing the outputs of the headers and bodies of clinical discharge summaries. We use a machine agent to measure similarity between the composite information models discovered in the body and in the diagnosis section of the matched entity. The assumption that the model derived from the body of the discharge summary will be similar to the model derived from the diagnosis section has been tested and the results show that our algorithm is a valid solution for comparing and finding similarities between clinical documents. The results show lower…
abstract: Annotation of clinical datasets is a process of discovery of clinical concepts and their association with machine readable constructs. The purpose of the process is to expose clinical information held in the datasets to automated agents in a structured format that facilitates and accelerates processes like document comparison, search or decision support. Numerous annotation techniques currently exist and techniques vary from simple matching to words from a reference dictionary to complex supervised machine learning techniques. The results vary, there is no gold standard and the community constantly produces new techniques. We developed an annotation technique that utilises ontological features of SNOMED-CT in discovering complex concepts contained in clinical documents and transforming them using term expansion into composite information models that reveal semantics of the messages conveyed in the document. Our algorithm converts clinical documents into segments of ontology of reality and weights the concepts found based on their frequency and location in the expanded structure. The algorithm utilises term expansion methods and pre-defined weights and compares the similarities of the resulting structures by measuring distances between their x highest weight-bearing concepts based on the position of those concepts in the ontology of reality’s graph structure represented by SNOMED CT. We test the effectiveness of our algorithm by comparing the outputs of the headers and bodies of clinical discharge summaries. We use a machine agent to measure similarity between the composite information models discovered in the body and in the diagnosis section of the matched entity. The assumption that the model derived from the body of the discharge summary will be similar to the model derived from the diagnosis section has been tested and the results show that our algorithm is a valid solution for comparing and finding similarities between clinical documents. The results show lower…
author: Zivaljevic, Aleksandar
license: https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
date published: 2021
date modified: 2026-07-24T01:04:52Z
provider: University of Auckland
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: ResearchSpace@Auckland

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2292/56837