Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of the Medical Domain and Decision Support Systems <s> Computer-based models of medical decision making account for a large portion of clinical computing efforts. This article reviews representative examples from each of several major medical computing paradigms. These include 1) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that considerable basic research in medical computing remains to be done and that powerful new approaches may lie in the melding of two or more established techniques. <s> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of the Medical Domain and Decision Support Systems <s> Internist-I is an experimental computer program capable of making multiple and complete diagnoses in internal medicine. It differs from most other programs for computer-assisted diagnosis in the generality of its approach and the size and diversity of its knowledge base. To document the strengths and weaknesses of the program we performed a systematic evaluation of the capabilities of INTERNIST-I. Its performance on a series of 19 clinicopathological exercises (Case Records of the Massachusetts General Hospital) published in the Journal appeared qualitatively similar to that of the hospital clinicians but inferior to that of the case discussants. The evaluation demonstrated that the present form of the program is not sufficiently reliable for clinical applications. Specific deficiencies that must be overcome include the program's inability to reason anatomically or temporally, its inability to construct differential diagnoses spanning multiple areas, its occasional attribution of findings to improper causes, and its inability to explain its "thinking". <s> BIB002 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of the Medical Domain and Decision Support Systems <s> An automated surveillance system utilizing a computer-based medical record system (COSTAR) was designed to improve the follow-up of patients with newly identified elevated diastolic blood pressure. A population of patients was selected where, in the 6-month period following the initial measurement of an elevated diastolic blood pressure, there were fewer than two visits during which blood pressure was recorded. In a randomized controlled clinical trial, this poor follow-up population was divided into two groups, with computer-generated reminders being automatically generated for only patients in the experimental group. Follow-up was significantly improved in the group receiving the reminders, both in terms of rate of follow-up attempted or achieved by the responsible physician and in the repeated recording of blood pressure. We conclude that a computer-based reminder system improves follow-up of newly discovered elevation in diastolic blood pressure. <s> BIB003 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of the Medical Domain and Decision Support Systems <s> If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be structured or coded. Many online patient reports are not coded, but are recorded in natural-language text that cannot be reliably accessed. Natural language processing (NLP) can solve this problem by extracting and structuring text-based clinical information, making clinical data available for use. NLP systems are quite difficult to develop, as they require substantial amounts of knowledge, but progress has definitely been made. Some NLP systems have been developed and tested and have demonstrated promising performance in practical clinical applications; some of these systems have already been deployed. The authors provide background information about NLP, briefly describe some of the systems that have been recently developed, and discuss the future of NLP in medicine. <s> BIB004 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of the Medical Domain and Decision Support Systems <s> Abstract ::: The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library. <s> BIB005 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of the Medical Domain and Decision Support Systems <s> Automated question answering has been a topic of research and development since the earliest AI applications. Computing power has increased since the first such systems were developed, and the general methodology has changed from the use of hand-encoded knowledge bases about simple domains to the use of text collections as the main knowledge source over more complex domains. Still, many research issues remain. The focus of this article is on the use of restricted domains for automated question answering. The article contains a historical perspective on question answering over restricted domains and an overview of the current methods and applications used in restricted domains. A main characteristic of question answering in restricted domains is the integration of domain-specific information that is either developed for question answering or that has been developed for other purposes. We explore the main methods developed to leverage this domain-specific information. <s> BIB006 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of the Medical Domain and Decision Support Systems <s> Objectives: In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics. Materials and methods: We devised a conceptual framework within which to categorize current QA approaches. In particular, we used ''semantic knowledge-based QA'' as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches. Results: Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference. Conclusion: Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems. <s> BIB007
A number of factors make the medical domain a complex and interesting one for text processing. They include: large volume of data (e.g., about 24 million articles in MEDLINE alone); highly complex domain-specific terminologies (e.g., drug names and disease names); domain-specific linguistic and ontological resources (such as UMLS); and software tools and methods for identifying semantic concepts and relationships (such as MetaMap BIB005 . The integration of technology with medical practice was initiated through the use of Clinical Decision Support (CDS) systems. Such systems help practitioners "make clinical decisions, deal with medical data or with the knowledge of medicine necessary to interpret such data"  ]. Early CDS systems consisted mostly of applications that facilitated diagnosis, treatment BIB001 BIB002 and the management of patients (e.g., through computerised guidelines and alerts) BIB003 . However, the capabilities of such applications were quite limited, primarily because they did not have access to raw medical data BIB004 . Since a lot of medical data is textual, the need to integrate NLP with CDS systems became more noticeable as the volume of medical data increased. This requirement introduced new challenges as well, such as faster processing ], which the latest research in large-scale distributed processing has successfully addressed. A detailed discussion of CDS systems is outside the scope of this paper and in the following subsections, we focus instead on a subset of CDS systems that attempt to provide answers to clinical queries by summarising the information contained in medical texts. Such systems consist of information extraction, summarisation, and QA components, and we do not distinguish between these three types. Identifying and presenting evidence in a condensed manner is essentially a task of summarisation. Hence we refer to these systems/components as summarisation systems. For QA systems, we primarily discuss their summarisation components. For the interested reader, BIB004 provide a review of early NLP-based CDS approaches and a recent and detailed analysis of CDS systems and NLP for the medical domain is provided by [DemnerFushman et al. 2009 ]. Readers unfamiliar with QA may refer to BIB006 for an overview of QA techniques in restricted domains and BIB007 ] for a review of QA approaches in the biomedical domain.