Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> Semantic processing provides the potential for producing high quality results in natural language processing (NLP) applications in the biomedical domain. In this paper, we address a specific semantic phenomenon, the hypernymic proposition, and concentrate on integrating the interpretation of such predications into a more general semantic processor in order to improve overall accuracy. A preliminary evaluation assesses the contribution of hypernymic propositions in providing more specific semantic predications and thus improving effectiveness in retrieving treatment propositions in MEDLINE abstracts. Finally, we discuss the generalization of this methodology to additional semantic propositions as well as other types of biomedical texts. <s> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> We explore a semantic abstraction approach to automatic summarization in the biomedical domain. The approach relies on a semantic processor that functions as the source interpreter and produces a list of predications. A transformation stage then generalizes and condenses this list, ultimately generating a conceptual condensate for a disorder input topic. The final condensate is displayed in graphical form. We provide a set of principles for the transformation stage and describe the application of this approach to multidocument input. Finally, we examine the characteristics and quality of the condensates produced. <s> BIB002 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> OBJECTIVE ::: To identify the most frequent obstacles preventing physicians from answering their patient-care questions and the most requested improvements to clinical information resources. ::: ::: ::: DESIGN ::: Qualitative analysis of questions asked by 48 randomly selected generalist physicians during ambulatory care. ::: ::: ::: MEASUREMENTS ::: Frequency of reported obstacles to answering patient-care questions and recommendations from physicians for improving clinical information resources. ::: ::: ::: RESULTS ::: The physicians asked 1,062 questions but pursued answers to only 585 (55%). The most commonly reported obstacle to the pursuit of an answer was the physician's doubt that an answer existed (52 questions, 11%). Among pursued questions, the most common obstacle was the failure of the selected resource to provide an answer (153 questions, 26%). During audiotaped interviews, physicians made 80 recommendations for improving clinical information resources. For example, they requested comprehensive resources that answer questions likely to occur in practice with emphasis on treatment and bottom-line advice. They asked for help in locating information quickly by using lists, tables, bolded subheadings, and algorithms and by avoiding lengthy, uninterrupted prose. ::: ::: ::: CONCLUSION ::: Physicians do not seek answers to many of their questions, often suspecting a lack of usable information. When they do seek answers, they often cannot find the information they need. Clinical resource developers could use the recommendations made by practicing physicians to provide resources that are more useful for answering clinical questions. <s> BIB003 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline. <s> BIB004 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> BackgroundThe use of PubMed to answer daily medical care questions is limited because it is challenging to retrieve a small set of relevant articles and time is restricted. Knowing what aspects of queries are likely to retrieve relevant articles can increase the effectiveness of PubMed searches. The objectives of our study were to identify queries that are likely to retrieve relevant articles by relating PubMed search techniques and tools to the number of articles retrieved and the selection of articles for further reading.MethodsThis was a prospective observational study of queries regarding patient-related problems sent to PubMed by residents and internists in internal medicine working in an Academic Medical Centre. We analyzed queries, search results, query tools (Mesh, Limits, wildcards, operators), selection of abstract and full-text for further reading, using a portal that mimics PubMed.ResultsPubMed was used to solve 1121 patient-related problems, resulting in 3205 distinct queries. Abstracts were viewed in 999 (31%) of these queries, and in 126 (39%) of 321 queries using query tools. The average term count per query was 2.5. Abstracts were selected in more than 40% of queries using four or five terms, increasing to 63% if the use of four or five terms yielded 2–161 articles.ConclusionQueries sent to PubMed by physicians at our hospital during daily medical care contain fewer than three terms. Queries using four to five terms, retrieving less than 161 article titles, are most likely to result in abstract viewing. PubMed search tools are used infrequently by our population and are less effective than the use of four or five terms. Methods to facilitate the formulation of precise queries, using more relevant terms, should be the focus of education and research. <s> BIB005 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> Clinical systematic reviews are based on expert, laborious search of well-annotated literature. Boolean search on bibliographic databases, such as MEDLINE, continues to be the preferred discovery method, but the size of these databases, now approaching 20 million records, makes it impossible to fully trust these searching methods. We are investigating the trade-offs between Boolean and ranked retrieval. Our findings show that although Boolean search has limitations, it is not obvious that ranking is superior, and illustrate that a single query cannot be used to resolve an information need. Our experiments show that a combination of less complicated Boolean queries and ranked retrieval outperforms either of them individually, leading to possible time savings over the current process. <s> BIB006 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p<0.01) and the increase in the overall score of clinical usefulness was 0.39 (p<0.05). <s> BIB007 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> Formal ontologies are difficult to read and understand for domain experts who do not have a background in formal logic. This severely restricts the ability of this user group to determine whether an ontology conforms to the requirements of the application domain or not. We argue that a formal ontology—in our case a description logic knowledge base—should be created in a linguistically motivated way so that domain experts can understand and query the ontology. We first show that this can be partially achieved with the help of a naming convention which is based on those linguistic expressions that occur in the application domain. We then go a step further and suggest the use of a controlled natural language for creating and querying formal ontologies that follows the syntactic structure of a well-defined subset of English. <s> BIB008 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Possible Applications of IR and NLP in EBM <s> Searching for relevant documents is a laborious task involved in preparing systematic reviews of biomedical literature. Currently, complex Boolean queries are iteratively developed, and then each document of the final query result is assessed for relevance. However, the result set sizes of these queries are hard to control, and in practice it is difficult to balance the competing desires to keep result sets to a manageable volume, and yet not exclude relevant documents from consideration. ::: ::: Ranking overcomes these problems by allowing the user to choose the number of documents to be inspected. However, previous work did not show significant improvements over the Boolean approach when ranked keyword queries based on terms in the Boolean queries, review title, research question or inclusion criteria were used. ::: ::: The extended Boolean retrieval model also provides ranked output, but existing complex Boolean queries can be directly used as formal description of the complex information needs occurring in this domain. In this paper we show that extended Boolean retrieval is able to find a larger quantity of relevant documents than previous approaches when comparable (or greater) numbers of documents are inspected for relevance. <s> BIB009
Despite the presence of many barriers, EBM practice has gained popularity over recent years for a number of reasons, including its promise of improving patient healthcare in the long run. As for the barriers, advances in technology are gradually eliminating them and making the process more efficient. From the problems and barriers specified earlier in this section, it can be inferred that the next boost in EBM practice will come from research in NLP. NLP offers suitable solutions to the problems faced by EBM practitioners, particularly for problems associated with information overload. Ideally, practitioners require bottom-line answers to their queries, along with estimates about the qualities of the evidences. They require fast access to information, and evidence-backed reasoning for recommendations BIB003 . NLP has the potential of addressing all these requirements. For example, Query Analysis can be used to understand and expand practitioners' queries through the use of domain-specific semantic information. Queries posed by practitioners are often very short with an average of 2.5 words BIB005 , and therefore existing techniques for ontology-driven query reformulation BIB008 ] can be built upon to help practitioners compose and refine queries. Information Retrieval techniques tailored for the medical domain can be used to increase the recall and precision of literature searches. Strength of recommendation values can be used to classify documents to make searches more reliable. There has already been some research in this area with promising outcomes BIB006 BIB009 . Information Extraction techniques which incorporate domain knowledge (e.g., MeSH terms, UMLS) can be used to extract relevant information, based on practitioners' questions, from the retrieved documents. This is also an area that is being explored by medical informatics researchers, and knowledge-based and statistical techniques have produced promising results BIB004 . Furthermore, some research has attempted to extract semantic information and use that information to represent the main concepts presented in documents BIB001 BIB002 BIB007 . Research in the area of information extraction is closely related to that of Document Summarisation. The goal of summarisation in this context is to summarise the content extracted from multiple documents and present them to the users (i.e., specific bottom-line recommendations). Summarisation of multiple documents is a well explored area (e.g., in the news domain), however its application to the medical domain is still quite limited. Successful summarisation of medical documents and effective presentation of summarised information to the user are key to the successful development of end-to-end question answering systems that can be used for EBM practice. More specifically, query-focused multi-document summarisation that incorporates medical domain knowledge is an area of research that is worth exploring and success in this area will significantly advance the practice of EBM. For the rest of this survey, we cover automated summarisation techniques and their applications to the medical domain.