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At least 1,369 records · Page 76Linked to original sources
PubMed2004

Computer control of mechanical ventilation.

Computer control of mechanical ventilators includes the operator-ventilator interface and the ventilator-patient interface. New ventilation modes represent the evolution of engineering control schemes. The various ventilation control strategies behind the modes have an underlying organization, and understanding that organization improves the clinician's appreciation of the capabilities of various ventilation modes and gives an idea of what we can and should expect for the future. The operator-ventilator interface has received little attention in the literature, despite the fact that there is a whole science of human-computer interaction. This report suggests a methodology for the study of ventilator interfaces.

Artificial Intelligence↗
PubMed2002

Defining the future of health technology: biomechatronics.

Future progress in healthcare and medicine depends on today's investment in research, development, and education. We cannot leave such urgent issues to determine themselves, but rather must actively collaborate to ensure a stable healthcare system. This chapter describes efforts made by leading experts in industry, government, and academia to better ascertain future healthcare management. Such collaboration has occurred during a series of Future Healthcare Technology Summits helping in planning investments in health technology. Deliberating and reviewing plans before taking action will accelerate progress as it will (1) save costs, (2) encourage compliance, (3) improve clinical outcomes, and (4) ensure greater patient satisfaction. What we must resolve is: How can we invest a couple billion dollars to save hundreds of billions and, most importantly, increase human health in the future. A new branch of science, Biomechatronics, with millions of Intelligent Caring Creatures- is the answer.

Artificial Intelligence↗
PubMed1994

Recognition of cardiogenic artifact in pediatric capnograms.

The capnograph is in regular use as a respiratory monitor. From measurements of its trace, calculations of breathing rate and end-tidal carbon dioxide can be made. Unfortunately, the reliability of these calculations depends on the quality of the signal. In the case of cardiogenic oscillations, the averaged results may be grossly inaccurate and, therefore, misleading. This paper describes a method for detecting such cardiogenic oscillations and removing their effect from the final results of capnogram analysis. The algorithm used resulted in a sensitivity of 99.6% and a specificity of 99.2%, when compared with manual scoring. The criteria could easily be incorporated into equipment software and are a step in the direction of "intelligent" monitoring.

Algorithms↗
PubMed2003

A general architecture for intelligent tutoring of diagnostic classification problem solving.

We report on a general architecture for creating knowledge-based medical training systems to teach diagnostic classification problem solving. The approach is informed by our previous work describing the development of expertise in classification problem solving in Pathology. The architecture envelops the traditional Intelligent Tutoring System design within the Unified Problem-solving Method description Language (UPML) architecture, supporting component modularity and reuse. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. We outline the advantages and limitations of this general approach, and describe it's implementation in SlideTutor - a developing Intelligent Tutoring System in Dermatopathology.

Artificial Intelligence↗
PubMed1992

The multi-trellis software architecture and the intelligent cardiovascular monitor.

A real-time, intelligent cardiovascular monitor is complex. It must process multiple waveforms, recognize artifacts, extract pertinent parameters, recognize a patient's clinical state, analyze the problem and formulate a response. This paper presents the multi-trellis (a collection of process trellises), a software architecture for building such a monitor. A process trellis is a uniform hierarchical framework for heterogeneous program modules. The multi-trellis extension allows one to compile several process trellis programs with widely varying run-time requirements into a single executable program that it is efficient, predictable and usable. Our prototype consists of two process trellises. The lower trellis contains processes to analyze three different analog signals: the blood pressure from a non-invasive monitor and an arterial catheter, and the ECG. The upper trellis contains processes to help detect evolving hemodynamic trends, identify abnormalities, and present a succinct summary to the clinician. Our prototype shows that the multi-trellis is a demonstrably useful software architecture for building these real-time, intelligent monitors.

Artificial Intelligence↗
PubMed2006

Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms.

The fertility of lactating dairy cows is economically important, but the mean reproductive performance of Holstein cows has declined during the past 3 decades. Traits such as first-service conception rate and pregnancy status at 150 d in milk (DIM) are influenced by numerous explanatory factors common to specific farms or individual cows on these farms. Machine learning algorithms offer great flexibility with regard to problems of multicollinearity, missing values, or complex interactions among variables. The objective of this study was to use machine learning algorithms to identify factors affecting the reproductive performance of lactating Holstein cows on large dairy farms. This study used data from farms in the Alta Genetics Advantage progeny-testing program. Production and reproductive records from 153 farms were obtained from on-farm DHI-Plus, Dairy Comp 305, or PCDART herd management software. A survey regarding management, facilities, labor, nutrition, reproduction, genetic selection, climate, and milk production was completed by managers of 103 farms; body condition scores were measured by a single evaluator on 63 farms; and temperature data were obtained from nearby weather stations. The edited data consisted of 31,076 lactation records, 14,804 cows, and 317 explanatory variables for first-service conception rate and 17,587 lactation records, 9,516 cows, and 341 explanatory variables for pregnancy status at 150 DIM. An alternating decision tree algorithm for first-service conception rate classified 75.6% of records correctly and identified the frequency of hoof trimming maintenance, type of bedding in the dry cow pen, type of cow restraint system, and duration of the voluntary waiting period as key explanatory variables. An alternating decision tree algorithm for pregnancy status at 150 DIM classified 71.4% of records correctly and identified bunk space per cow, temperature for thawing semen, percentage of cows with low body condition scores, number of cows in the maternity pen, strategy for using a clean-up bull, and milk yield at first service as key factors.

Algorithms↗
PubMed1996

Testing and validation of computerized decision support systems.

Systematic, through testing of decision support systems (DSSs) prior to release to general users is a critical aspect of high quality software design. Omission of this step may lead to the dangerous, and potentially fatal, condition of relying on a system with outputs of uncertain quality. Thorough testing requires a great deal of effort and is a difficult job because tools necessary to facilitate testing are not well developed. Testing is a job ill-suited to humans because it requires tireless attention to a large number of details. For these reasons, the majority of DSSs available are probably not well tested prior to release. We have successfully implemented a software design and testing plan which has helped us meet our goal of continuously improving the quality of our DSS software prior to release. While requiring large amounts of effort, we feel that the process of documenting and standardizing our testing methods are important steps toward meeting recognized national and international quality standards. Our testing methodology includes both functional and structural testing and requires input from all levels of development. Our system does not focus solely on meeting design requirements but also addresses the robustness of the system and the completeness of testing.

Artificial Intelligence↗
PubMed1997

NéoGanesh: a working system for the automated control of assisted ventilation in ICUs.

Automating the control of therapy administered to a patient requires systems which integrate the knowledge of experienced physicians. This paper describes NéoGanesh, a knowledge-based system which controls, in closed-loop, the mechanical assistance provided to patients hospitalized in intensive care units. We report on how new advances in knowledge representation techniques have been used to model medical expertise. The clinical evaluation shows that such a system relieves the medical staff of routine tasks, improves patient care, and efficiently supports medical decisions regarding weaning. To be able to work in closed-loop and to be tested in real medical situations, NéoGanesh deals with a voluntarily limited problem. However, embedded in a powerful distributed environment, it is intended to support future extensions and refinements and to support reuse of knowledge bases.

Artificial Intelligence↗