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At least 757 records · Page 42Linked to original sources
PubMed1999

Comparing expert systems for identifying chest x-ray reports that support pneumonia.

We compare the performance of four computerized methods in identifying chest x-ray reports that support acute bacterial pneumonia. Two of the computerized techniques are constructed from expert knowledge, and two learn rules and structure from data. The two machine learning systems perform as well as the expert constructed systems. All of the computerized techniques perform better than a baseline keyword search and a lay person, and perform as well as a physician. We conclude that machine learning can be used to identify chest x-ray reports that support pneumonia.

Algorithms↗
PubMed2000

Event discovery in medical time-series data.

Vast amounts of clinical information are generated daily on patients in the health care setting. Increasingly, this information is collected and stored for its potential utility in advancing health care. Knowledge-based systems, for example, might be able to apply rules to the collected data to determine whether a patient has a certain condition. Often, however, the underlying knowledge needed to write such rules is not well understood. How could these clinical data be useful then? Use of machine learning is one answer. We present a pipeline for discovering the knowledge needed for event detection in medical time-series data. We demonstrate how this process can be applied in the development of intelligent patient monitoring for the intensive care unit (ICU). Specifically, we develop a system for detecting Otrue alarmO situations in the ICU, where currently as many as 86% of bedside monitor alarms are false.

Artificial Intelligence↗
PubMed2000

MedView-design and adoption of an interactive system for oral medicine.

MedView is a joint project with participants from oral medicine and computer science. The aim of the project is to build a large database from patient examinations and produce computerized tools to extend, view, and analyze the contents of the database. The contents of the data base is based on a formalization of health-care processes and clinical knowledge in oral medicine harmonized within the network SOMNET. We give an overview of the current status of the MedView project and discuss background and future directions.

Artificial Intelligence↗
PubMed2000

Nearest neighbour classification with heterogeneous proximity functions.

Heterogeneous proximity functions are similarity or distance functions which process data differently according to the scale of attributes. We compared two Minkowskian distance functions with three heterogeneous proximity functions to test whether these functions were better in data sets with attributes of mixed type. Significant differences in nearest neighbour classification accuracy were found in 11 of all the 21 data sets. City-block and Euclidean distance functions outperformed Gower's similarity function and Heterogeneous Euclidean-Overlap Metric, while Heterogeneous Value Difference Metric (HVDM) was better than the other functions. HVDM classified mixed data best, because it treats nominal attributes more carefully than the other functions.

Artificial Intelligence↗
PubMed1996

First use of cognitive algorithms in investigations under compensated gravity.

In the present paper the use of cognitive algorithms for solving a wide spectrum of problems which often arise in investigations under compensated gravity is suggested. Applying such algorithms in the preparation and performance of experiments provides a substantial assistance to the experimentator as the behaviour of complex processes can be described and predicted correctly even when unexpected perturbations occur. Furthermore, an essential advantage of cognitive computing consists in the fact that the description and optimisation of the processes considered are possible also in such cases in which the corresponding basic equations are not known or not treatable practically. For convenience, the basic ideas of cognitive algorithms are discussed here. Due to their special relevance for investigations under compensated gravity algorithms based on fuzzy logic (FL) and artificial neuronal networks (ANN) are elucidated more in detail. In order to illustrate some advantages of cognitive computing exemplary results for the flow field induced by coaxial rotating disks are given. This represents the first attempt to use the benefits provided by cognitive algorithms in investigations under compensated gravity. The flow field between rotating disks plays an important role not only in experiments under compensated gravity but also in a wide range of terrestrial applications. A comparison of the results found by solving the Navier-Stokes equations and those from the prediction performed by ANN adequately trained shows an excellent agreement. However, the calculation times needed by the ANN are significantly smaller than that of the direct numerical simulation. Therefore, the real time prediction of the results from a running experiment seems to be possible.

Algorithms↗
PubMed2001

Intelligent systems for nursing education.

Health care is one of the fastest growing areas in terms of care, treatment and the exploitation of new technology in Slovenia. There is a great need for new approaches ensuring that education and work of health care professionals will be built upon the state of the art in nursing. As a consequence the educational, governmental and "industrial" institutions from Slovenia, UK, Italy and Greece have determined to work on above problem. EU agreed to support the project under the Phare Tempus Framework and the aim of this paper is to present an educational approach based on intelligent systems and its application in nursing education.

Artificial Intelligence↗
PubMed2001

Induction of hypotheses concerning hip arthroplasty: a modified methodology for medical research.

OBJECTIVES: The objective of this study is to advocate a methodology for medical research that, in contrast to traditional medical methodology, exploits the flexibility of machine learning and retains the kind of statistical tests that are generally accepted in the medical field for the confirmation of hypotheses. METHODS: First, the medical problem is defined and data for an observed population are collected; then a machine learning tool is used to generate hypotheses regarding the problem; finally, statistical methods are used to determine the validity of the generated hypotheses. RESULTS: To illustrate this approach, the problem of defining indications for hip arthroplasty after an acute medial femoral neck fracture is investigated as a case study. CONCLUSIONS: The methodology is similar to the usual style of applying machine learning, but insists on a link to the techniques of statistical tests that are normally used in medicine. It aims at a more flexible and economical use of experimental data than in the usual medical research, which is enabled by techniques of machine learning. At the same time, by reference to traditional statistical tests, it is hoped that this approach will lead to improved acceptance of machine learning in the medical field.

Aged↗
PubMed2002

Intelligent agent software for medicine.

An important trend for the future of health technology will be the increasing use of intelligent agent software for medical applications. As the complexity of situations faced by both patients and health care providers grows, conventional interfaces that rely on users to manually transfer data and manually perform each problem-solving step, won't be able to keep up. This article describes how software agents that incorporate learning, personalization, proactivity, context-sensitivity and collaboration will lead to a new generation of medical applications that will streamline user interfaces and enable more sophisticated communication and problem-solving.

Artificial Intelligence↗
PubMed2003

In search of a course design and teaching methods to improve critical thinking skills.

This article describes a unique course design to teach critical thinking skills to students in formal academic degree programs preparing to become healthcare administrators. The course and its experimental design were motivated by several factors. First, intelligent and competent problem solving and decision making, most would agree, are directly dependent on critical thinking proficiency. Therefore, elevating students' critical-thinking competence will inherently improve their problem-solving and decision-making ability. Second, problem solving and decision making consumes the majority of an administrator's time. And finally, a review of AUPHA's catalog descriptions of healthcare graduate curriculums listrelatively few critical-thinking courses compared to the time administrators devote to it.

Curriculum↗
PubMed2003

Evaluating the C-section rate of different physician practices: using machine learning to model standard practice.

The C-section rate of a population of 22,175 expectant mothers is 16.8%; yet the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 13% to 23%. Our goal is to determine retrospectively if the variations in the observed rates can be attributed to variations in the intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or differences in physician practice (i.e. some groups do more C-sections). We apply machine learning to this problem by training models to predict standard practice from retrospective data. We then use the models of standard practice to evaluate the C-section rate of each physician practice. Our results indicate that although there is variation in intrinsic risk among the groups, there also is much variation in physician practice.

Artificial Intelligence↗
PubMed2003

Interactive visualization and exploration of time-oriented clinical data using a distributed temporal-abstraction architecture.

KNAVE-II is a system for visualization and exploration of large amounts of time-oriented clinical data and of multiple levels of clinically meaningful abstractions derivable from these data. KNAVE-II uses a distributed temporal-abstraction architecture that integrates a set of knowledge services, each interacting with a domain-specific knowledge source, a set of data-access services, each interacting with a clinical data source, and a computational service for deriving knowledge-based abstractions of the data.

Artificial Intelligence↗
PubMed2004

The GLARE approach to clinical guidelines: main features.

In this paper, we present GLARE, a domain-independent prototypical system for acquiring, representing and executing clinical guidelines. GLARE has been built within a 7-year project with Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy) and has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure. GLARE is characterized by the adoption of advanced Artificial Intelligence (AI) techniques, to support medical decision making and to manage temporal knowledge.

Decision Making, Computer-Assisted↗
PubMed2004

MUDRLite - health record tailored to your particular needs.

Nowadays most hospitals use electronic health records as part of their hospital information systems. However, these systems are more suitable for hospital management than for physicians. The health record is not sufficiently structured; it includes a lot of free-text information, and the set of collected attributes is fixed and practically impossible to extend. Physicians, gathering information for the purpose of medical studies, often use varied proprietary solutions based on MS Access databases or MS Excel Sheets. The EuroMISE Centre - Cardio is developing an electronic health record (EHR) application called MUDRLite, which could easily fill the gap between existing EHRs. MUDRLite is the result of applied research in the field of EHR design, which is based on experience gathered during cooperation in the TripleC project. MUDRLite development is an extra branch in the MUDR (MUltimedia Distributed Record) development; it simplifies both the MUDR architecture and the MUDR data storage principles.MUDRLite itself is an empty body, which has to be filled in with an XML configuration file. This file completely describes the visual aspects and the behavior of the EHR application. It includes simple 4GL-like constructs written in the MUDRLite Language (MLL). This enables - using the event-oriented programming principles - to program various handling procedures for a range of actions, e.g. clicking a button fills a form with the result of an SQL statement. MUDRLite can be tailored to particular needs of any healthcare provider. This makes the MUDRLite application easy to use in specific environments. In the first instance, we are testing it at the Neurovascular Department of the Central Military Hospital in Prague.

Artificial Intelligence↗
PubMed1991

Knowledge-based support for a physician's workstation.

We describe knowledge-based support for a Physician's Workstation prototype. Our knowledge base uses a qualitative simulation model of patient physiology. We present the motivation behind our design, discuss the components of the knowledge base, and show how the knowledge base supports a physician's workstation in the patient management process. We describe a graphical knowledge base editor used by the domain expert for knowledge acquisition, and a graphical knowledge base presenter which monitors the qualitative simulation during patient event processing.

Artificial Intelligence↗
PubMed1991

Using comparative clinical information to understand practice patterns and affect organizational change.

The University Hospital Consortium is collecting clinical, administrative and financial data from its members to develop a Clinical Information Network. The value of this collective data lies in how comparative information about peer hospitals and physicians in the same specialty can be used to influence practice. The raw data from each hospital is analyzed, classified, normalized and stored in a data repository which is easily accessible. This data becomes information when it is presented in a variety of ways, and is supported by a knowledge-base of health care rules. The "drilling down" technique to progressive levels of detail serves the needs of all levels in the organization--executives, managers, and analysts. The system combines the power of a mainframe for the data repository with the ease of use of a PC-based workstation. With an open-ended approach, the users can ask a variety of questions of the data, as well as perform statistical analysis, create graphical presentations and generate explanations of the analysis techniques.

Artificial Intelligence↗