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At least 1,477 records · Page 82Linked to original sources
PubMed1999

Intelligent split menus for data entry: a simulation study in general practice medicine.

A compelling notion in menu design is that a few of the most frequently selected items should be placed as a hot list at the top of the menu. A few researchers have explored this type of interface control, known as a split menu, and have investigated the identification of the hot-list items by statistical analysis of past data. We extend the technique to automated development of dynamic hot-lists for entry of medication data in a General Practice setting. Using clinical data from 113,000 visits, a statistical model is developed and evaluated by simulated data entry of cases held back from training. Simulated SOAP note entry shows 12-item hot lists to hold over 70% of desired drug and diagnosis selections. Intelligent split menus should improve user efficiency if current selection methods require 3 seconds or more per item. A demonstration prototype can be downloaded over the Web.

Artificial Intelligence↗
PubMed1995

Principles of development and design of microsystems.

The state of the art in microsystem design and fabrication is reviewed. The focus is on silicon micromachining, which uses the equipment and processes of IC-technology to produce highly miniaturized, three-dimensional structures, sensors and actuators for various biomedical applications. The micro components can be integrated into surgical instruments, catheters and implants. Thus new functions and a higher level of intelligence are created. Portable microsystems are being developed for chemical analysis and biomedical monitoring. The cultivation and investigation of live cells on chip substrates offers encouraging perspectives for the future.

Artificial Intelligence↗
PubMed2004

Human-centric intelligent systems for exploration and knowledge discovery.

This speculative article discusses research and development relating to computational intelligence (CI) technologies comprising powerful machine-based search and exploration techniques that can generate, extract, process and present high-quality information from complex, poorly understood biotechnology domains. The integration and capture of user experiential knowledge within such CI systems in order to support and stimulate knowledge discovery and increase scientific and technological understanding is of particular interest. The manner in which appropriate user interaction can overcome problems relating to poor problem representation within systems utilising evolutionary computation (EC), machine-learning and software agent technologies is investigated. The objective is the development of user-centric intelligent systems that support an improving knowledge-base founded upon gradual problem re-definition and reformulation. Such an approach can overcome initial lack of understanding and associated uncertainty.

Artificial Intelligence↗
PubMed2006

Evaluation of PROforma as a language for implementing medical guidelines in a practical context.

BACKGROUND: PROforma is one of several languages that allow clinical guidelines to be expressed in a computer-interpretable manner. How these languages should be compared, and what requirements they should meet, are questions that are being actively addressed by a community of interested researchers. METHODS: We have developed a system to allow hypertensive patients to be monitored and assessed without visiting their GPs (except in the most urgent cases). Blood pressure measurements are performed at the patients' pharmacies and a web-based system, created using PROforma, makes recommendations for continued monitoring, and/or changes in medication. The recommendations and measurements are transmitted electronically to a practitioner with authority to issue and change prescriptions. We evaluated the use of PROforma during the knowledge acquisition, analysis, design and implementation of this system. The analysis focuses on the logical adequacy, heuristic power, notational convenience, and explanation support provided by the PROforma language. RESULTS: PROforma proved adequate as a language for the implementation of the clinical reasoning required by this project. However a lack of notational convenience led us to use UML activity diagrams, rather than PROforma process descriptions, to create the models that were used during the knowledge acquisition and analysis phases of the project. These UML diagrams were translated into PROforma during the implementation of the project. CONCLUSION: The experience accumulated during this study highlighted the importance of structure preserving design, that is to say that the models used in the design and implementation of a knowledge-based system should be structurally similar to those created during knowledge acquisition and analysis. Ideally the same language should be used for all of these models. This means that great importance has to be attached to the notational convenience of these languages, by which we mean the ease with which they can be read, written, and understood by human beings. The importance of notational convenience arises from the fact that a language used during knowledge acquisition and analysis must be intelligible to the potential users of a system, and to the domain experts who provide the knowledge that will be used in its construction.

Antihypertensive Agents↗
PubMed2006

Automated identification of cancerous smears using various competitive intelligent techniques.

In this study the performance of various intelligent methodologies is compared in the task of pap-smear diagnosis. The selected intelligent methodologies are briefly described and explained, and then, the acquired results are presented and discussed for their comprehensibility and usefulness to medical staff, either for fault diagnosis tasks, or for the construction of automated computer-assisted classification of smears. The intelligent methodologies used for the construction of pap-smear classifiers, are different clustering approaches, feature selection, neuro-fuzzy systems, inductive machine learning, genetic programming, and second order neural networks. Acquired results reveal the power of most intelligent techniques to obtain high quality solutions in this difficult problem of medical diagnosis. Some of the methods obtain almost perfect diagnostic accuracy in test data, but the outcome lacks comprehensibility. On the other hand, results scoring high in terms of comprehensibility are acquired from some methods, but with the drawback of achieving lower diagnostic accuracy. The experimental data used in this study were collected at a previous stage, for the purpose of combining intelligent diagnostic methodologies with other existing computer imaging technologies towards the construction of an automated smear cell classification device.

Artificial Intelligence↗
PubMed1997

Effective data validation of high-frequency data: time-point-, time-interval-, and trend-based methods.

Real-time systems for monitoring and therapy planning, which receive their data from on-line monitoring equipment and computer-based patient records, require reliable data. Data validation has to utilize and combine a set of fast methods to detect, eliminate, and repair faulty data, which may lead to life-threatening conclusions. The strength of data validation results from the combination of numerical and knowledge-based methods applied to both continuously-assessed high-frequency data and discontinuously-assessed data. Dealing with high-frequency data, examining single measurements is not sufficient. It is essential to take into account the behavior of parameters over time. We present time-point-, time-interval-, and trend-based methods for validation and repair. These are complemented by time-independent methods for determining an overall reliability of measurements. The data validation benefits from the temporal data-abstraction process, which provides automatically derived qualitative values and patterns. The temporal abstraction is oriented on a context-sensitive and expectation-guided principle. Additional knowledge derived from domain experts forms an essential part for all of these methods. The methods are applied in the field of artificial ventilation of newborn infants. Examples from the real-time monitoring and therapy-planning system VIE-VENT illustrate the usefulness and effectiveness of the methods.

Artificial Intelligence↗
PubMed2004

Formalization of mouse embryo anatomy.

MOTIVATION: The Edinburgh Mouse Atlas and Gene Expression Database project has developed a digital atlas of mouse development to provide a spatio-temporal framework for spatially mapped data such as in situ gene expression and cell lineage. As part of this database, a mouse embryo anatomy ontology has been created. A formalization of this anatomy is required to document its precise semantics and how it is used in the context of the Mouse Atlas. RESULTS: The paper describes the existing anatomy ontology and formalizes aspects of it using a predicate logic based approach. It therefore provides a guide for users of the current version of the ontology, as well as the basis for a description of the anatomy using an ontology language, such as OWL, thus enabling future work on reasoning about the Mouse Atlas in the context of an intelligent gene expression bioinformatics workflow system. The logic has been implemented in a Prolog prototype. AVAILABILITY: The Mouse Atlas is available on-line at http://genex.hgu.mrc.ac.uk

Algorithms↗
PubMed1994

The role of object representation in the design of the intelligent radiology workstation.

The paper describes the design of the Intelligent Radiology Workstation (IRW) that is intended to handle heterogeneous radiologic data (text, image, video) and radiologic knowledge in such a way that it is easy to store, access, use, and repurpose. An object-based structure is used to combine the relational database, hybrid knowledge base, and hypermedia within a common framework. Functions such as data entry and retrieval, browsing, and intelligent processing of data are available in the single environment. IRW open architecture allows radiologic digital resources to be used for clinical practice, diagnosis support, education, and research.

Artificial Intelligence↗
PubMed1999

A collision model for safety evaluation of autonomous intelligent cruise control.

This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.

Acceleration↗
PubMedDate not supplied

Using fuzzy sets to analyze putative correlates between age, blood type, gender and/or race with bacterial infection.

Previous studies have suggested that the demographic variables of age and blood type may serve as "risk factors" for infection by specific bacterial species. Since both demographic variables and bacterial species are defined using generally accepted parameters, they constitute highly suitable variables for the generation of a fuzzy logic program. A prospective study was therefore undertaken to examine the influence of age, blood type, gender and race on bacterial infection rates using a real database generated from 187 bacteremic patients admitted to Albert Einstein Medical Center. A fuzzy logic program was created using 155 randomly selected patients' data with four input (demographic variables) and four output classes (infections with "staphylococci", "streptococci", "Escherichia coli" or "non-E. coli gram negative rods (non-E.coli GNR)"). To see whether bacterial infection could be predicted based on demographic data alone, the program was tested using the remaining 32 patients' data. The program was able to correctly determine the bacterial output group of 27 of 32 randomly selected patients, giving an overall correlation of 84.38%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a powerful diagnostic tool for all physicians.

Adult↗
PubMed2005

Generalization of a prototype intelligent hybrid system for hard gelatin capsule formulation development.

The aim of this project was to expand a previously developed prototype expert network for use in the analysis of multiple biopharmaceutics classification system (BCS) class II drugs. The model drugs used were carbamazepine, chlorpropamide, diazepam, ibuprofen, ketoprofen, naproxen, and piroxicam. Recommended formulations were manufactured and tested for dissolution performance. A comprehensive training data set for the model drugs was developed and used to retrain the artificial neural network. The training and the system were validated based on the comparison of predicted and observed performance of the recommended formulations. The initial test of the system resulted in high error values, indicating poor prediction capabilities for drugs other than piroxicam. A new data set, containing 182 batches, was used for retraining. Ten percent of the test batches were used for cross-validation, resulting in models with R2 > or = 70%. The comparison of observed performance to the predicted performance found that the system predicted successfully. The hybrid network was generally able to predict the amount of drug dissolved within 5% for the model drugs. Through validation, the system was proven to be capable of designing formulations that met specific drug performance criteria. By including parameters to address wettability and the intrinsic dissolution characteristics of the drugs, the hybrid system was shown to be suitable for analysis of multiple BCS class II drugs.

Artificial Intelligence↗
PubMedDate not supplied

Scenario recognition for temporal reasoning in medical domains.

The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Déjà Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information.

Airway Obstruction↗
PubMed2001

The graphical presentation of decision support information in an intelligent anaesthesia monitor.

This contribution examines the graphical presentation of decision support information generated by an intelligent monitor, named SENTINEL, developed for use during anaesthesia. Clinicians make diagnoses in real-time during operations by examining clinically significant trends in multiple signals. SENTINEL attempts to mimic this decision process by using a system of fuzzy trend templates. SENTINEL's implementation of fuzzy trend templates is capable of providing the dual fuzzy measures of belief and plausibility, which are derived from the theory of evidence. It is thus capable of generating fairly rich diagnostic decision support information. However, for SENTINEL to be effective, the visual presentation of this information must be intuitive to the anaesthetist, who may not be familiar with the theory of evidence. This paper discusses techniques that are being evaluated to meet the requirements of the SENTINEL anaesthesia monitor. Specifically, the paper presents methods for highlighting clinically significant trends in physiological (or derived) signals by superimposing a coloured band on the signal that reflects fuzzy output from the intelligent monitor. This paper also discusses the intuitive graphical presentation of binary diagnostic fuzzy measures, including their further interpretation and presentation as crisp "alarm" and "warning" conditions.

Anesthesia, General↗
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↗
PubMed1992

[Intelligent instrumentation in medicine].

The introduction of intelligent robots, expert systems and other forms of intelligent automatization in the current practice of medicine seems to be inevitable. It appears interesting to look back to the efforts that have been done, since the former steps, about three decades ago and consider the prospects in this field for both short and long term. Simultaneously it is interesting to reckon the new aspects which are raised with the evolution of these methodologies such as the responsibility of decisions taken by intelligent systems, the probable advantages, at the present stage, of the interactive systems and the risk of self-learning systems. Some efforts carried out in our department in this field are described.

Artificial Intelligence↗
PubMed2001

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed1999

An intelligent system for pacemaker reprogramming.

The process of reprogramming a cardiac pacemaker can be described in terms similar to those used for describing diagnostic problem solving. In this paper, the process of reprogramming a pacemaker is formalized as a special form of abductive diagnostic reasoning, where observable findings are interpreted with respect to results obtained from diagnostic tests. The dynamics of this process is cast as a diagnostic strategy, where information is gathered in a structured fashion. This abductive theory of pacemaker reprogramming has been used as the basis for an actual system that in its present form is capable of assisting cardiologists in dealing with problems in atrial sensing and pacing. The performance of the system has been evaluated using data from actual patients.

Algorithms↗
PubMed2005

Rehabilitation device with variable resistance and intelligent control.

Resistance exercise has been widely reported to have positive rehabilitation effects for patients with neuromuscular and orthopaedic conditions. This paper presents an optimal design of magneto-rheological fluid dampers for variable resistance exercise device in the form of a knee brace. An intelligent supervisory control for regulating the resistive force or torque of the knee brace has also been studied. The device provides both isometric and isokinetic strength training for the knee.

Artificial Intelligence↗