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E H Shortliffe

Publications and source records attributed to E H Shortliffe.

87 records · Page 5Linked to original sources
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The Unified Medical Language System: toward a collaborative approach for solving terminologic problems.

The approach taken by the Unified Medical Language System (UMLS), in which disparate terminology systems are integrated, has allowed construction of an electronic thesaurus (the Metathesaurus) that avoids imposing any restrictions upon the content, structure, or semantics of the source terminologies. As such, the UMLS has served as a unifying paradigm by providing appropriate links among equivalent entities that are used in different contexts or for different purposes. It accordingly provides a vehicle through which possibly orthogonal semantic models can co-exist within a single framework. This framework provides a model for the collaborative evolution of biomedical terminology and allows a synergistic relationship between the UMLS and its source terminology systems.

Systems Integration↗
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The guideline interchange format: a model for representing guidelines.

OBJECTIVE: To allow exchange of clinical practice guidelines among institutions and computer-based applications. DESIGN: The GuideLine Interchange Format (GLIF) specification consists of GLIF model and the GLIF syntax. The GLIF model is an object-oriented representation that consists of a set of classes for guideline entities, attributes for those classes, and data types for the attribute values. The GLIF syntax specifies the format of the test file that contains the encoding. METHODS: Researchers from the InterMed Collaboratory at Columbia University, Harvard University (Brigham and Women's Hospital and Massachusetts General Hospital), and Stanford University analyzed four existing guideline systems to derive a set of requirements for guideline representation. The GLIF specification is a consensus representation developed through a brainstorming process. Four clinical guidelines were encoded in GLIF to assess its expressivity and to study the variability that occurs when two people from different sites encode the same guideline. RESULTS: The encoders reported that GLIF was adequately expressive. A comparison of the encodings revealed substantial variability. CONCLUSION: GLIF was sufficient to model the guidelines for the four conditions that were examined. GLIF needs improvement in standard representation of medical concepts, criterion logic, temporal information, and uncertainty.

Decision Making, Computer-Assisted↗
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Representing thoughts, words, and things in the UMLS.

The authors describe a framework, based on the Ogden-Richards semiotic triangle, for understanding the relationship between the Unified Medical Language System (UMLS) and the source terminologies from which the UMLS derives its content. They pay particular attention to UMLS's Concept Unique Identifier (CUI) and the sense of "meaning" it represents as contrasted with the sense of "meaning" represented by the source terminologies. The CUI takes on emergent meaning through linkage to terms in different terminology systems. In some cases, a CUI's emergent meaning can differ significantly from the original sources' intended meanings of terms linked by that CUI. Identification of these different senses of meaning within the UMLS is consistent with historical themes of semantic interpretation of language. Examination of the UMLS within such a historical framework makes it possible to better understand the strengths and limitations of the UMLS approach for integrating disparate terminologic systems and to provide a model, or theoretic foundation, for evaluating the UMLS as a Possible World--that is, as a mathematical formalism that represents propositions about some perspective or interpretation of the physical world.

Linguistics↗
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Representing clinical guidelines in GLIF: individual and collaborative expertise.

OBJECTIVE: An evaluation of the cognitive processes used in the translation of a clinical guideline from text into an encoded form so that it can be shared among medical institutions. DESIGN: A comparative study at three sites regarding the generation of individual and collaborative representations of a guideline for the management of encephalopathy using the GuideLine Interchange Format (GLIF) developed by members of the InterMed Collaboratory. MEASUREMENTS: Using theories and methods of cognitive science, the study involves a detailed analysis of the cognitive processes used in generating representations in GLIF. The resulting process-outcome measures are used to compare subjects with various types of computer science or clinical expertise and from different institutions. RESULTS: Consistent with prior studies of text comprehension and expertise, the variability in strategies was found to be dependent on the degree of prior experience and knowledge of the domain. Differing both in content and structure, the representations developed by physicians were found to have additional information and organization not explicitly stated in the guidelines, reflecting the physicians' understanding of the underlying pathophysiology. The computer scientists developed more literal representations of the guidelines; addition were mostly limited to specifications mandated by the logic of GLIF itself. Collaboration between physicians and computer scientists resulted in consistent representations that were more than the sum of the separate parts, in that both domain-specific knowledge of medicine and generic knowledge of guideline structure were seamlessly integrated. CONCLUSION: Because of the variable construction of guideline representations, understanding the processes and limitations involved in their generation is important in developing strategies to construct shared representations that are both accurate and efficient. The encoded guidelines developed by teams that include both clinicians and experts in computer-based representations are preferable to those developed by individuals of either type working alone.

Brain Diseases↗
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Informatics at NIH.

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Biomedical Engineering↗
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Are medical informatics and nursing informatics distinct disciplines? The 1999 ACMI debate.

The 1999 debate of the American College of Medical Informatics focused on the proposition that medical informatics and nursing informatics are distinctive disciplines that require their own core curricula, training programs, and professional identities. Proponents of this position emphasized that informatics training, technology applications, and professional identities are closely tied to the activities of the health professionals they serve and that, as nursing and medicine differ, so do the corresponding efforts in information science and technology. Opponents of the proposition asserted that informatics is built on a re-usable and widely applicable set of methods that are common to all health science disciplines, and that "medical informatics" continues to be a useful name for a composite core discipline that should be studied by all students, regardless of their health profession orientation.

Medical Informatics↗
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Choice and explanation in medical management: a multiattribute model of artificial intelligence approaches.

This paper explores a model of choice and explanation in medical management and makes clear its advantages and limitations. The model is based on multiattribute decision making (MADM) and consists of four distinct strategies for choice and explanation, plus combinations of these four. Each strategy is a restricted form of the general MADM approach, and each makes restrictive assumptions about the nature of the domain. The advantage of tailoring a restricted form of a general technique to a particular domain is that such efforts may better capture the character of the domain and allow choice and explanation to be more naturally modelled. The uses of the strategies for both choice and explanation are illustrated with analyses of several existing medical management artificial intelligence (AI) systems, and also with examples from the management of primary breast cancer. Using the model it is possible to identify common underlying features of these AI systems, since each employs portions of this model in different ways. Thus the model enables better understanding and characterization of the seemingly ad hoc decision making of previous systems.

Algorithms↗
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A methodology for generating computer-based explanations of decision-theoretic advice.

Decision analysis is an appealing methodology with which to provide decision support to the practicing physician. However, its use in the clinical setting is impeded because computer-based explanations of decision-theoretic advice are difficult to generate without resorting to mathematical arguments. Nevertheless, human decision analysts generate useful and intuitive explanations based on decision trees. To facilitate the use of decision theory in a computer-based decision support system, the authors developed a computer program that uses symbolic reasoning techniques to generate nonquantitative explanations of the results of decision analyses. A combined approach has been implemented to explain the differences in expected utility among branches of a decision tree. First, the mathematical relationships inherent in the structure of the tree are used to find any asymmetries in tree structure or inequalities among analogous decision variables that are responsible for a difference in expected utility. Next, an explanation technique is selected and applied to the most significant variables, creating a symbolic expression that justifies the decision. Finally, the symbolic expression is converted to English-language text, thereby generating an explanation that justifies the desirability of the choice with the greater expected utility. The explanation does not refer to mathematical formulas, nor does it include probability or utility values. The results suggest that explanations produced by a combination of decision analysis and symbolic processing techniques may be more persuasive and acceptable to clinicians than those produced by either technique alone.

Artificial Intelligence↗
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Networking health: learning from others, taking the lead.

The Internet provides one of the most compelling examples of the way in which government research investments can, in time, lead to innovations of broad social and economic impact. This paper reviews the history of the Internet's evolution, emphasizing in particular its relationship to biomedical computing and to the nation's health care system. Here I summarize current national research programs, emphasizing the need for greater involvement by the medical research community and leadership from federal health care agencies.

Delivery of Health Care↗
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Medical informatics and clinical decision making: the science and the pragmatics.

There are important scientific and pragmatic synergies between the medical decision making field and the emerging discipline of medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classic probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, the author describes recent work in his laboratory that has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. This point is illustrated with an example drawn from outcomes management research.

Artificial Intelligence↗