Jack W. Smith

Task-structure analysis for knowledge modeling

n recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article , we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description , based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches. A knowledge-based system (KBS) has explicit representations of knowledge as well as inference processes that operate on these representations to achieve a goal. An inference process consists of a number of inference steps, each step creating additional knowledge. The process of applying inference steps is repeated until the information needed to fulfill the requirements of the problem-solving goal or task is generated. Typically, both domain knowledge and possible inference steps have to be modeled and represented in some form. In one sense, knowledge is of general utility-the same piece can be utilized in different contexts and problems; so, unlike traditional procedural approaches, knowledge should not be tied to one task or goal. On the other hand, it is difficult to know what knowledge to put in a system without having an idea of the tasks the KBS will confront. In spite of claims of generality, all KBSs are designed with some task or class of tasks in mind. Similarly, they are designed to be operational across some range of domains. Thus, a clear understanding of the relationship between tasks, knowledge and inferences required to perform the task is needed before knowledge in …

Designing human-centered distributed information systems

Many computer systems are designed according to engineering and technology principles and are typically difficult to learn and use. The fields of human-computer interaction, interface design, and human factors have made significant contributions to ease of use and are primarily concerned with the interfaces between systems and users, not with the structures that are often more fundamental for designing truly human-centered systems. The emerging paradigm of human-centered computing (HCC)-which has taken many forms-offers a new look at system design. HCC requires more than merely designing an artificial agent to supplement a human agent. The dynamic interactions in a distributed system composed of human and artificial agents-and the context in which the system is situated-are indispensable factors. While we have successfully applied our methodology in designing a prototype of a human-centered intelligent flight-surgeon console at NASA Johnson Space Center, this article presents a methodology for designing human-centered computing systems using electronic medical records (EMR) systems.

What is biomedical informatics

Biomedical informatics lacks a clear and theoretically-grounded definition. Many proposed definitions focus on data, information, and knowledge, but do not provide an adequate definition of these terms. Leveraging insights from the philosophy of information, we define informatics as the science of information, where information is data plus meaning. Biomedical informatics is the science of information as applied to or studied in the context of biomedicine. Defining the object of study of informatics as data plus meaning clearly distinguishes the field from related fields, such as computer science, statistics and biomedicine, which have different objects of study. The emphasis on data plus meaning also suggests that biomedical informatics problems tend to be difficult when they deal with concepts that are hard to capture using formal, computational definitions. In other words, problems where meaning must be considered are more difficult than problems where manipulating data without regard for meaning is sufficient. Furthermore, the definition implies that informatics research, teaching, and service should focus on biomedical information as data plus meaning rather than only computer applications in biomedicine.

Human-centered design of a distributed knowledge management system

Many healthcare technology projects fail due to the lack of consideration of human issues, such as workflow, organizational change, and usability, during the design and implementation stages of a projects development process. Even when human issues are considered, the consideration is typically on designing better user interfaces. We argue that human-centered computing goes beyond a better user interface: it should include considerations of users, functions and tasks that are fundamental to human-centered computing. From this perspective, we integrated a previously developed human-centered methodology with a Project Design Lifecycle, and we applied this integration in the design of a complex distributed knowledge management system for the Biomedical Engineer (BME) domain in the Mission Control Center at NASA Johnson Space Center. We analyzed this complex system, identified its problems, generated systems requirements, and provided specifications of a replacement prototype for effective organizational memory and knowledge management. We demonstrated the value provided by our human-centered approach and described the unique properties, structures, and processes discovered using this methodology and how they contributed in the design of the prototype.

A catalog of errors

Abstract This paper reviews various errors that have been described by comparing human behavior to the norms of probability, causal connection and logical deduction. For each error we review evidence on whether the error has been demonstrated to occur. For many errors, the occurrence of a bias has not been demonstrated; for other, a bias does occur, but arguments can be made that the bias is not always an error. Based on the conclusions of this review, we caution researchers and practitioners in referring to well known biases and errors.

‘Deep’ models and their relation to diagnosis

Abstract In this paper we distinguish between deep models in the sense of scientific first principles and deep cognitive models where the problem solver has a qualitative symbolic representation of the system or device that accounts for how the system ‘works’. We analyze diagnostic reasoning as an information processing task, identifying the generic types of knowledge (and reasoning) needed for the task to be performed adequately. If these are available, an integrated collection of generic problem solvers can produce a diagnostic conclusion. The need for deep or causal models arises when some or all of these types of knowledge are missing in the problem solver. We provide a typology of different knowledge structures and reasoning processes that play a role in qualitative or functional reasoning and elaborate on functional representations as deep cognitive models for some aspects of causal reasoning in medicine.

Synergies and Distinctions between Computational Disciplines in Biomedical Research: Perspective from the Clinical and Translational Science Award Programs

Clinical and translational research increasingly requires computation. Projects may involve multiple computationally oriented groups including information technology (IT) professionals, computer scientists, and biomedical informaticians. However, many biomedical researchers are not aware of the distinctions among these complementary groups, leading to confusion, delays, and suboptimal results. Although written from the perspective of Clinical and Translational Science Award (CTSA) programs within academic medical centers, this article addresses issues that extend beyond clinical and translational research. The authors describe the complementary but distinct roles of operational IT, research IT, computer science, and biomedical informatics using a clinical data warehouse as a running example. In general, IT professionals focus on technology. The authors distinguish between two types of IT groups within academic medical centers: central or administrative IT (supporting the administrative computing needs of large organizations) and research IT (supporting the computing needs of researchers). Computer scientists focus on general issues of computation such as designing faster computers or more efficient algorithms, rather than specific applications. In contrast, informaticians are concerned with data, information, and knowledge. Biomedical informaticians draw on a variety of tools, including but not limited to computers, to solve information problems in health care and biomedicine. The paper concludes with recommendations regarding administrative structures that can help to maximize the benefit of computation to biomedical research within academic health centers.

Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described.

Coping with the complexities of multiple-solution problems: a case study

A model is proposed to account for the expertise of a skilled immunohematologist in solving multiple-solution problems. These problems, which he must deal with daily, are concerned with ensuring the safe transfusion of blood into patients. This model suggests that he copes with this difficult class of problems by: (1) Using patterns in the data to simplify the problem, hypothesizing the number of primitive solutions necessary to account for the test results and, when possibles, decomposing the problem into a set of less complex, single-solution problems. Such decompositions then enable more powerful reasoning processes; (2) making use of a mixture of data-driven and hypothesis-driven processes in order to reduce the chances that heuristic (and therefore error-prone) methods and cognitive biases will lead away from critical data; (3) Relying on a mixture of confirmatory and rule-out processes to provide converging evidence, thus reducing the changes of error; (4) uncovering his own errors through the use of “error models” that identify the conditions where one of his processes is likely to make an error (similar to the use of student models by expert tutors to diagnose mistakes made by students).

An empirical evaluation of the performance of antibody identification tasks.

Four empirical studies were conducted for better understanding of the nature of problem‐solving activities by medical technologists and medical technology students when performing antibody identification tasks. The results indicated the importance of strategies that ensure the collection of converging evidence, as these strategies protect against the fallibility of commonly used heuristics and against errors due to simple slips. The results also indicate that not only do students make significant numbers of errors, but so do practicing technologists. In one of the studies covering a 1‐year period, for instance, a group of 16 technologists made a total of 41 errors in 1057 cases. On the basis of these findings, several alternatives are proposed to reduce errors.