Analysing Clinical Notes for Translation Research: Back to the Future

Analysing Clinical Notes for Translation Research: Back to the Future

Jon Patrick (The University of Sydney, Australia) and Pooyan Asgari (The University of Sydney, Australia)
DOI: 10.4018/978-1-60566-274-9.ch020
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There have been few studies of large corpora of narrative notes collected from the health clinicians working at the point of care. This chapter describes the principle issues in analysing a corpus of 44 million words of clinical notes drawn from the Intensive Care Service of a Sydney hospital. The study identifies many of the processing difficulties in dealing with written materials that have a high degree of informality, written in circumstances where the authors are under significant time pressures, and containing a large technical lexicon, in contrast to formally published material. Recommendations on the processing tasks needed to turn such materials into a more usable form are provided. The chapter argues that these problems require a return to issues of 30 years ago that have been mostly solved for computational linguists but need to be revisited for this entirely new genre of materials. In returning to the past and studying the contents of these materials in retrospective studies we can plan to go forward to a future that provides technologies that better support clinicians. They need to produce both lexically and grammatically higher quality texts that can then be leveraged successfully for advanced translational research thereby bolstering its momentum.
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The task of performing natural language processing over clinical notes goes back to 1983 Chi, E. C., Sager, N., Tick, L. J., & Lyman, M. S.; Gabrieli, E. R., Speth, D. J., 1986) with the work Chi, Sager, Tick and Lyman, and it is only gradually increased in activity to this date. However, in 2008 with have had the first conference specifically targeted at the “Text and Data Mining of Clinical Documents” with a conference organized by the Turku Centre for Computer Science, in Finland (Karsten, H., Back, B., Salakoski, T., Salanterä, S., & Suominen, H., 2008). Much of the work prior to the 1990s has been superceded by later shifts in processing power and new ideas on the software development for this task. The review of the literature in this paper is restricted to later topics that are particularly relevant to automated processing of the language of clinical notes.

In 2001, Taira and Soderland published their approach to information extraction from clinical notes in the radiology domain. They proposed a general structure for such applications which was later used by many other research groups (Huang, Y., Lowe, H., Klein, D., & Cucina, R., 2005; Arnold, C. W., Bui, A. A. T., Morioka, C., El-Saden, S., & Kangarloo, H., 2007; Thomas, B. J., Ouellette, H., Halpern, E. F., & Rosenthal, D. I., 2005; Sinha, U., & Kangarloo, H., 2002). Their proposal had five steps of processing for a complete data retrieval system: Structural analyzer, Lexical analyzer, Parser, Semantic interpreter and Frame constructor. They performed an evaluation on this structuring and reported obstacles in: deep understanding of the domain, ability to deal with ungrammatical writing styles, shorthand and telegraphic writings, finding solutions for assumed knowledge between the writer and reader, and handling a large vocabulary. Following their work, different studies have expanded their proposed system to other clinical domains or addressed the reported issues (Sun, J. Y., & Sun, Y., 2006) and in some cases reported new obstacles.

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Table of Contents
Violaine Prince, Mathieu Roche
Chapter 1
Sophia Ananiadou
Text mining provides the automated means to manage information overload and overlook. By adding meaning to text, text mining techniques produce a... Sample PDF
Text Mining for Biomedicine
Chapter 2
Dimitrios Kokkinakis
The identification and mapping of terminology from large repositories of life science data onto concept hierarchies constitute an important initial... Sample PDF
Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH®
Chapter 3
M. Teresa Martín-Valdivia, Arturo Montejo-Ráez, M. C. Díaz-Galiano, José M. Perea Ortega, L. Alfonso Ureña-López
This chapter argues for the integration of clinical knowledge extracted from medical ontologies in order to improve a Multi-Label Text... Sample PDF
Expanding Terms with Medical Ontologies to Improve a Multi-Label Text Categorization System
Chapter 4
Piotr Pezik, Antonio Jimeno Yepes, Dietrich Rebholz-Schuhmann
The present chapter discusses the use of terminological resources for Information Retrieval in the biomedical domain. The authors first introduce a... Sample PDF
Using Biomedical Terminological Resources for Information Retrieval
Chapter 5
Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet
The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a... Sample PDF
Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval
Chapter 6
Vincent Claveau
This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This... Sample PDF
Translation of Biomedical Terms by Inferring Rewriting Rules
Chapter 7
Nils Reiter, Paul Buitelaar
This chapter is concerned with lexical enrichment of ontologies, that is how to enrich a given ontology with lexical information derived from a... Sample PDF
Lexical Enrichment of Biomedical Ontologies
Chapter 8
Torsten Schiemann, Ulf Leser, Jörg Hakenberg
Ambiguity is a common phenomenon in text, especially in the biomedical domain. For instance, it is frequently the case that a gene, a protein... Sample PDF
Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach
Chapter 9
M. Narayanaswamy, K. E. Ravikumar, Z. Z. Hu, K. Vijay-Shanker, C. H. Wu
Protein posttranslational modification (PTM) is a fundamental biological process, and currently few text mining systems focus on PTM information... Sample PDF
Information Extraction of Protein Phosphorylation from Biomedical Literature
Chapter 10
Yves Kodratoff, Jérôme Azé, Lise Fontaine
This chapter argues that in order to extract significant knowledge from masses of technical texts, it is necessary to provide the field specialists... Sample PDF
CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence
Chapter 11
Yun Niu, Graeme Hirst
The task of question answering (QA) is to find an accurate and precise answer to a natural language question in some predefined text. Most existing... Sample PDF
Analyzing the Text of Clinical Literature for Question Answering
Chapter 12
Nadine Lucas
This chapter presents the challenge of integrating knowledge at higher levels of discourse than the sentence, to avoid “missing the forest for the... Sample PDF
Discourse Processing for Text Mining
Chapter 13
Dimosthenis Kyriazis, Anastasios Doulamis, Theodora Varvarigou
In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical... Sample PDF
A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems
Chapter 14
Yitao Zhang, Jon Patrick
The fast growing content of online articles of clinical case studies provides a useful source for extracting domain-specific knowledge for improving... Sample PDF
Extracting Patient Case Profiles with Domain-Specific Semantic Categories
Chapter 15
Laura I. Furlong, Ferran Sanz
SNPs constitute key elements in genetic epidemiology and pharmacogenomics. While data about genetic variation is found at sequence databases... Sample PDF
Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach
Chapter 16
Francisco M. Couto, Mário J. Silva, Vivian Lee, Emily Dimmer, Evelyn Camon, Rolf Apweiler
Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a... Sample PDF
Verification of Uncurated Protein Annotations
Chapter 17
Burr Settles
ABNER (A Biomedical Named Entity Recognizer) is an open-source software tool for text mining in the molecular biology literature. It processes... Sample PDF
A Software Tool for Biomedical Information Extraction (And Beyond)
Chapter 18
Asanee Kawtrakul, Chaveevarn Pechsiri, Sachit Rajbhandari, Frederic Andres
Valuable knowledge has been distributed in heterogeneous formats on many different Web sites and other sources over the Internet. However, finding... Sample PDF
Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering
Chapter 19
Christophe Jouis, Magali Roux-Rouquié, Jean-Gabriel Ganascia
Identical molecules could play different roles depending of the relations they may have with different partners embedded in different processes, at... Sample PDF
Seekbio: Retrieval of Spatial Relations for System Biology
Chapter 20
Jon Patrick, Pooyan Asgari
There have been few studies of large corpora of narrative notes collected from the health clinicians working at the point of care. This chapter... Sample PDF
Analysing Clinical Notes for Translation Research: Back to the Future
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