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.
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:
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:
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.
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.)