PROGRAMMED HEURISTICS AND THE CONCEPT OF PAR IN BUSINESS GAMES.
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Artificial Intelligence↗
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A system of IAC neural networks as the basis for self-organization in a sociological dynamical system simulation.
This sociological simulation uses the ideas of semiotics and symbolic interactionism to demonstrate how an appropriately developed associative memory in the minds of individuals on the microlevel can self-organize into macrolevel dissipative structures of societies such as racial cultural/economic classes, status symbols and fads. The associative memory used is based on an extension of the IAC neural network (the Interactive Activation and Competition network). Several IAC networks act together to form a society by virtue of their human-like properties of intuition and creativity. These properties give them the ability to create and understand signs, which lead to the macrolevel structures of society. This system is implemented in hierarchical object oriented container classes which facilitate change in deep structure. Graphs of general trends and an historical account of a simulation run of this dynamical system are presented.
Black or African American↗
Cognitive systems engineering: new wine in new bottles.
This paper presents an approach to the description and analysis of complex Man-Machine Systems (MMSs) called Cognitive Systems Engineering (CSE). In contrast to traditional approaches to the study of man-machine systems which mainly operate on the physical and physiological level, CSE operates on the level of cognitive functions. Instead of viewing an MMS as decomposable by mechanistic principles, CSE introduces the concept of a cognitive system: an adaptive system which functions using knowledge about itself and the environment in the planning and modification of actions. Operators are generally acknowledged to use a model of the system (machine) with which they work. Similarly, the machine has an image of the operator. The designer of an MMS must recognize this, and strive to obtain a match between the machine's image and the user characteristics on a cognitive level, rather than just on the level of physical functions. This article gives a presentation of what cognitive systems are, and of how CSE can contribute to the design of an MMS, from cognitive task analysis to final evaluation.
Artificial Intelligence↗
Microcomputer data acquisition and control.
In medicine and biology there are many tasks that involve routine well defined procedures. These tasks are ideal candidates for computerized data acquisition and control. As the performance of microcomputers rapidly increases and cost continues to go down the temptation to automate the laboratory becomes great. To the novice computer user the choices of hardware and software are overwhelming and sadly most of the computer sales persons are not at all familiar with real-time applications. If you want to bill your patients you have hundreds of packaged systems to choose from; however, if you want to do real-time data acquisition the choices are very limited and confusing. The purpose of this chapter is to provide the novice computer user with the basics needed to set up a real-time data acquisition system with the common microcomputers. This chapter will cover the following issues necessary to establish a real time data acquisition and control system: Analysis of the research problem: Definition of the problem; Description of data and sampling requirements; Cost/benefit analysis. Choice of Microcomputer hardware and software: Choice of microprocessor and bus structure; Choice of operating system; Choice of layered software. Digital Data Acquisition: Parallel Data Transmission; Serial Data Transmission; Hardware and software available. Analog Data Acquisition: Description of amplitude and frequency characteristics of the input signals; Sampling theorem; Specification of the analog to digital converter; Hardware and software available; Interface to the microcomputer. Microcomputer Control: Analog output; Digital output; Closed-Loop Control. Microcomputer data acquisition and control in the 21st Century--What is in the future? High speed digital medical equipment networks; Medical decision making and artificial intelligence.
Artificial Intelligence↗
An expert systems facility for mumps.
This paper describes extensions to the Mumps language which facilitate writing expert systems in Mumps. These extensions are based upon the Prolog language and utilize the existing Mumps global array data base as well as other aspects of the Mumps environment. Details are given of an experimental implementation of the interpreter written in C for the Unix operating system.
Artificial Intelligence↗
Objects, contradictions and collaboration in medical cognition: an activity-theoretical perspective.
Activity theory suggests three principles for contextual analysis of clinical cognition: orientation to objects in cognition, role of contradictions in cognition, and the importance of collaboration in cognition. Focusing on the objects of cognition calls attention to differences across medical work settings. There is an interconnection between the type of the object encountered, the physician's generalized conception of the object, and the physician's choice of linearization or lateralization as cognitive strategy. In a consultation the object of medical cognition is locally constructed through a series of mediated actions. Identification of contradictions at the level of the institutional activity system is crucial for the understanding of failures and innovations in actions of medical cognition. A conceptual model for analyzing such contradictions is presented. It is demonstrated that medical cognition is a collaborative achievement between the physician and the patient. Patients use a variety of strategies to turn their experienced health problems into manageable problems. The patient's strategy can and often does influence the physician's strategy and the outcome of the consultation.
Adult↗
The future of forensic and crime scene science. Part I. A UK forensic science user and provider perspective.
This paper presents an overview of the views expressed by UK forensic science users and providers during the Centre for Forensic Investigation's 1 day conference 'The Future of Forensic and Crime Scene Science' and is set in the context of the changing national agenda and likely advances in current and future technology. It begins by examining the success of the Home Office DNA Expansion Programme and future demands of the Criminal Justice System, highlighting the changing use of forensic science both at the crime scene and within the forensic process itself. In particular, the use of forensic science at the early stages of an investigation to provide intelligence and support the decision making process is discussed together with the need to adopt a partnership approach to tackling crime and its causes. Key system and technological drivers for performance improvement and change are identified and the likely timescales and implications of their introduction are discussed. Finally, the Home Office plans to build on the success of the DNA Expansion Programme, through the introduction of the proposed Home Office Forensic Integration Strategy, are explored and the paper concludes by highlighting the benefits, implications and issues arising from the changing and developing use of forensic science.
Congresses as Topic↗
Getting to the point: developing IT for the sharp end of healthcare.
Healthcare demonstrates the same properties of risk, complexity, uncertainty, dynamic change, and time-pressure as other high hazard sectors including aviation, nuclear power generation, the military, and transportation. Unlike those sectors, healthcare has particular traits that make it unique such as wide variability, ad hoc configuration, evanescence, resource constraints, and governmental and professional regulation. While healthcare's blunt (management) end is more easily understood, the sharp (operator) end is more difficult to research the closer one gets to the sharp end's point. Understanding sharp end practice and cognitive work can improve computer-based systems resilience, which is the ability to perform despite change and challenges. Research into actual practice at the sharp end of healthcare will provide the basis to understand how IT can support clinical practice. That understanding can be used to develop computer-based systems that will act as team players, able to support both individual and distributed cognitive work at healthcare's sharp end.
Artificial Intelligence↗
Intelligent ship traffic monitoring for oil spill prevention: risk based decision support building on AIS.
The paper describes a model, which estimates the risk levels of individual crude oil tankers. The intended use of the model, which is ready for trial implementation at The Norwegian Coastal Administrations new Vardø VTS (Vessel Traffic Service) centre, is to facilitate the comparison of ships and to support a risk based decision on which ships to focus attention on. For a VTS operator, tasked with monitoring hundreds of ships, this is a valuable decision support tool. The model answers the question, "Which ships are likely to produce an oil spill accident, and how much is it likely to spill?".
Decision Support Techniques↗
The effects of communication modes on performance and discourse organization with an adaptive interface.
This paper reports a study of different communication patterns on performance with a simulated adaptive interface that created the impression of a talking and listening computer which would help participants solve problems with a computer. There were four levels of communication modes which differed in the restrictions placed on human-computer communication. Dependent measures included tasks completed per minute as well as participants' utterances, which were assessed for verbosity, disfluencies, and indices of common ground. The largest performance differences were found between the groups that could communicate freely and those where communication was restricted or denied. As restriction increased, performance decreased. Further, as restriction increased, the computer assumed greater control and verbosity decreased. Performance on the simple tasks declined as communication restriction increased, but no differences were observed for complex tasks. The results are discussed with respect to differences between human-human and human-computer communication as well as research on adaptive environments.
Analysis of Variance↗
Building a case-based diet recommendation system without a knowledge engineer.
We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.
Artificial Intelligence↗
Formalizing an electronic institution for the distribution of human tissues.
The use of multi-agent systems (MAS) in health-care domains is increasing. Such agent-mediated medical systems can manage complex tasks and have the potential to adapt gracefully to unexpected events. However, in these kinds of systems the issues of privacy, security and trust are particularly sensitive in relation to matters such as agents' access to patient records, what is acceptable behaviour for an agent in a particular role and the development of trust both between (heterogeneous) agents and between users and agents. To address these issues we propose a formal normative framework, deriving from and developing the notion of an electronic institution. Such institutions provide a framework to define and police norms that guide, control and regulate the behaviour of the heterogeneous agents that participate in the institution. These norms define the acceptable actions that each agent may perform depending on the role or roles it is playing, and clearly specifies the data it may access and/or modify in playing those roles. In this paper, we present the formalization of Carrel, a virtual organization for the procurement of organs and tissues for transplantation purposes, as an electronic institution using the ISLANDER institution specification language as formalizing languages. We demonstrate aspects of the formalization of such an institution, example fragments in the language used for the textual specification, and how such formalization can be used as a blueprint in the implementation of the final agent architecture, through techniques such as skeleton generation.
Artificial Intelligence↗
The internal challenges of medical informatics.
Haux's [7] basic assumption that the object of medical informatics is: "... to assure and to improve the quality of healthcare as well as the quality of research and education in medicine and in the health sciences ..." is taken as a starting point to discuss the three main topics: What is the meaning of medical informatics (i.e. what should be the main activities of medical informatics to bring maximum benefit to medicine)? What are the achievements and failures of medical informatics today (again considering the impact on the quality of healthcare)? What are the main challenges? Concerning the definition of medical informatics it is argued that one should not hide the link to basic informatics and, for that matter to computers, completely behind abstract definitions. After an analysis of the purposes of the definition of a discipline, a differentiated definition of the scope of medical informatics, rather general when concerning the field of scientific interest, more focused when concerning the practical (constructive) applications, is proposed. Contrasting Haux's chapter on achievements of medical informatics we concentrate on and analyse non fulfilled promises of medical informatics to derive lessons for the future and to propose 'generic' (or core) tasks of medical informatics to meet the challenges of the future. A set of 'internal challenges' of medical informatics to change priorities and attitudes within the discipline is put forward to enable medical informatics to meet the 'external challenges' listed by Haux.
Artificial Intelligence↗
A critical evaluation of the semantic-episodic distinction.
Tulving (1983, 1984) has recently claimed that a wide range of evidence supports the distinction between episodic and semantic memory systems. He has provided a list of features to describe the differences between the two systems and a set of experimental results to demonstrate the distinction. In this article, we present opposing evidence that invalidates many of the distinguishing features and contradicts interpretations of the supporting experiments. In addition, we argue that the question of whether there are two separate memory systems cannot be answered without a specific theory about the differences between the systems.
Amnesia↗
Intelligent computer reporting 'lack of experience': a confidence measure for decision support systems.
The purpose of this study was to explore the feasibility of developing artificial neural networks that are able to provide confidence measures for their diagnostic advice. Computer-aided decision making can improve physician performance, but many physicians hesitate to use these 'black boxes'. If we are to rely upon decision support systems for such tasks as medical diagnosis it is essential that the computers indicate when the advice given is based on experience, i.e. give a confidence measure. An artificial neural network was trained to diagnose healed anterior myocardial infarction and to indicate 'lack of experience' when test electrocardiograms were different from the electrocardiograms of the training set. A database of 1249 electrocardiograms from patients who had undergone cardiac catheterization was used to train and test the neural network. Thereafter, the ability of the network to indicate 'lack of experience' was assessed using 100 left bundle branch block electrocardiograms, an electrocardiographic pattern that was excluded from the training set. The network indicated that 83% of the left bundle branch block electrocardiograms and 1% of the test electrocardiograms from catheterized patients were different from the electrocardiograms of the training set. All but one of the left bundle branch block electrocardiograms would otherwise be falsely classified as anterior myocardial infarction by the network. Artificial neural networks can be trained to indicate 'lack of experience', and this ability increases the possibility for neural networks to be accepted as reliable decision support systems in clinical practice.
Bundle-Branch Block↗
Computer oriented hospital systems. All four types have limitations which can be offset by an integrative approach.
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Artificial Intelligence↗
Predicting high-risk preterm birth using artificial neural networks.
A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.
Artificial Intelligence↗
Role play: mentoring, membership in professional organizations, and the pursuit of excellence in nursing.
It is our professional responsibility to encourage membership in nursing organizations. Membership helps nurses make intelligent, well-informed decisions about health care and our profession. It also enhances our credibility and helps validate nursing as a profession. Plato said, "Those having torches will pass them on to others." As nurses, educators, preceptors, advocates and mentors, we carry the fire.
Humans↗