Analyzing the Text of Clinical Literature for Question Answering

Analyzing the Text of Clinical Literature for Question Answering

Yun Niu (Ontario Cancer Institute, Canada) and Graeme Hirst (University of Toronto, Canada)
DOI: 10.4018/978-1-60566-274-9.ch011
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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 QA systems handle fact-based questions that usually take named entities as the answers. In this chapter, the authors take clinical QA as an example to deal with more complex information needs. They propose an approach using Semantic class analysis as the organizing principle to answer clinical questions. They investigate three Semantic classes that correspond to roles in the commonly accepted PICO format of describing clinical scenarios. The three Semantic classes are: the description of the patient (or the problem), the intervention used to treat the problem, and the clinical outcome. The authors focus on automatic analysis of two important properties of the Semantic classes.
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The vast increase in online information brings new challenges to the area of information retrieval (IR) in both query processing and answer processing. To free the user from constructing a complicated boolean keywords query, a system should be able to process queries represented in natural language. Instead of responding with some documents relevant to the query, the system should actually answer the questions accurately and concisely. Systems with such characteristics are question-answering (QA) systems, which take advantage of high-quality natural language processing and mature technologies in IR. The task of a QA system is to find the answer to a particular natural language question in some predefined text. In this paper, we propose an approach that aims to automatically find answers to clinical questions.

Clinicians often need to consult literature on the latest information in patient care, such as side effects of a medication, symptoms of a disease, or time constraints in the use of a medication. The published medical literature is an important source to help clinicians make decisions in patient treatment (Sackett & Straus, 1998; Straus & Sackett, 1999). For example:

  • Q: In a patient with a suspected MI does thrombolysis decrease the risk of death if it is administered 10 hours after the onset of chest pain?

An answer to the question can be found in Clinical Evidence (CE) (Barton, 2002), a regularly updated publication that reviews and consolidates experimental results for clinical problems:

  • A: Systematic reviews of RCTs have found that prompt thrombolytic treatment (within 6 hours and perhaps up to 12 hours and longer after the onset of symptoms) reduces mortality in people with AMI and ST elevation or bundle branch block on their presenting ECG.

Studies have shown that searching the literature can help clinicians answer questions regarding patient treatment (Cimino, 1996; Gorman, Ash, & Wykoff, 1994; Mendonça, Cimino, Johnson, & Seol, 2001). It has also been found that if high-quality evidence is available in this way at the point of care—e.g., the patient’s bedside —clinicians will use it in their decision making, and it frequently results in additional or changed decisions (Sackett & Straus, 1998; Straus & Sackett, 1999). The practice of using the current best evidence to help clinicians in making decisions on the treatment of individual patients is called evidence-based medicine (EBM).

Clinical questions usually represent complex information needs and cannot be answered using a single word or phrase. For a clinical question, it is often the case that more than one clinical trial with different experimental settings will have been performed. Results of each trial provide some evidence on the problem. To answer such a question, all this evidence needs to be taken into account, as there may be duplicate evidence, partially agreed-on evidence, or even contradictions. A complete answer can be obtained only by synthesizing these multiple pieces of evidence, as shown in Figure 1. In our work, we take EBM as an example to investigate clinical QA. Our targets are questions posed by physicians in patient treatment.

Figure 1.

Example of a clinical question, with corresponding evidence from Clinical Evidence

Our task is to find answers to clinical questions automatically. Our work is part of the EPoCare project (“Evidence at Point of Care”) at the University of Toronto. The goal of EPoCare is to develop methods for answering clinical questions automatically with CE as the source text. (We do not look at primary medical research text.)

Complete Chapter List

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Table of Contents
Violaine Prince, Mathieu Roche
Chapter 1
Sophia Ananiadou
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Text Mining for Biomedicine
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Dimitrios Kokkinakis
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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
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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
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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
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Information Extraction of Protein Phosphorylation from Biomedical Literature
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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
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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
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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
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Laura I. Furlong, Ferran Sanz
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Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach
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Burr Settles
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A Software Tool for Biomedical Information Extraction (And Beyond)
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