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PubMed2000

Managing medical and insurance information through a smart-card-based information system.

The continuously increased mobility of patients and doctors, in conjunction with the existence of medical groups consisting of private doctors, general practitioners, hospitals, medical centers, and insurance companies, pose significant difficulties on the management of patients' medical data. Inevitably this affects the quality of the health care services provided. The evolving smart card technology can be utilized for the implementation of a secure portable electronic medical record, carried by the patient herself/himself. In addition to the medical data, insurance information can be stored in the smart card thus facilitating the creation of an "intelligent system" supporting the efficient management of patient's data. In this paper we present the main architectural and functional characteristics of such a system. We also highlight how the security features offered by smart cards can be exploited in order to ensure confidentiality and integrity of the medical data stored in the patient cards.

Computer Security↗
PubMed2006

Fuzzy approach to the intelligent management of virtual spaces.

This paper presents research carried out toward the improvement of current virtual environments from an intelligent systems approach. A novel architecture to solve vague queries that allows users to find objects and scenes in virtual environments is described. As a base, a new virtual worlds representation model and an associated fuzzy querying approach are used. The new representation model adds a semantic level to the usual models, providing more suitable environments for the interaction with users. The query solver is able to work with queries expressing the vagueness inherent to human conceptualization of visual perception (for example, tall tree, a park with many tall trees, or a park bench near approximately five tall trees). The system has been developed and evaluated with user experiments, where comparison with navigation and keyword-based query approaches have been realized. The results of this study show that the proposed architecture is more powerful and intuitive for finding the targets.

Algorithms↗
PubMed2003

Virtual reality surgery: neurosurgery and the contemporary landscape.

OBJECTIVE: Virtual reality-simulated environments have been used for the training of personnel, most notably for military applications, for more than 35 years. The advantages conferred by being able to train novice personnel in a low- to no-risk simulated environment have long been appreciated by the medical community. The recent availability of affordable gigahertz-range microprocessors (once the exclusive domain of the Cray supercomputer) has made photorealistic graphical rendering and manipulation of virtual surgical substrates a reality. Concomitant advances in artificial intelligence systems and the portability of patient-specific magnetic resonance imaging, computed tomographic scanning, and angiographic image data presage the emergence of the surgical simulator as a modern surgical training adjunct. An overview of the status of surgical simulation with regard to its adaptability to current surgical training regimens is presented. METHODS: Extensive MEDLINE, Internet, and other database searches spanning the years 1960 to 2002 were conducted in an effort to delineate the status of simulated surgical environments. RESULTS: As would be expected, most articles addressing surgical simulation as their primary focus have been published in the past decade. A review of this literature demonstrates the broadest application in the field of endoscopic (and laparoscopic) procedures, most likely as a result of the reduced engineering burden with respect to incorporation of a haptic interface. CONCLUSION: The realization of ergonomically acceptable haptic interfaces remains elusive. Improvements in graphical rendering and the incorporation of artificial intelligence functions signal the certain emergence of surgical simulators as a viable supplement to the Halstedian method of surgical training.

Humans↗
PubMed1997

Linking large administrative databases: a method for conducting emergency medical services cohort studies using existing data.

OBJECTIVE: To evaluate probabilistic matching for linking a cohort of cardiac arrest (CA) patients identified in the Metro Toronto Ambulance (MTA) database in Toronto, Ontario, Canada, to their appropriate record in either the Vital Statistics Information System (VSIS) or the Canadian Institute of Health Information (CIHI) databases and thus establish their clinical outcomes. METHODS: A linkage of a large administrative database was performed. A cohort of patients who suffered an out-of-hospital CA during the calendar years 1988-1993 was identified. To determine the patients' outcomes, the cohort was probabilistically linked to patient records in the VSIS and CIHI databases. Identifying variables used during the process of linking records included: names (first and last); New York State Identification and Intelligence System (NYSIIS) code; date of event; date of death; city; admitting hospital number; mode of admission to hospital; age; and sex. RESULTS: A cohort of 7,079 CA patients was identified from the MTA database; 6,448 (91%) patients were accurately linked to records in 1 of the 2 outcome databases (CIHI, VSIS). Missing data for > or = 1 of the linking variables were responsible for unlinked records. Using these longitudinal data, it was possible to determine the number of patients surviving their out-of-hospital CAs to be admitted to hospital (n = 833) (16%). No differences in survival rates (p = 0.06) or median lengths of hospital stay among the survivors (p = 0.15) were observed between admitting hospitals. CONCLUSIONS: Probabilistic matching is an effective method by which researchers can use existing administrative data to determine outcomes of population cohorts. This is especially valuable in situations where controlled intervention studies are not feasible or may be inappropriate. In this analysis, in-hospital management of admitted CA patients, as determined by hospital-specific survival rates and length of stay, suggests no measurable differences in the care provided to these patients by hospitals in Toronto.

Algorithms↗
PubMed2005

NewYork-Presbyterian Hospital: translating innovation into practice.

BACKGROUND: NewYork-Presbyterian (NYP) Hospital, a 2,242-bed not-for-profit academic medical center, was formed by a merger of The New York Hospital and The Presbyterian Hospital in the City of New York. It is also the flagship for the NewYork-Presbyterian Healthcare System, with 37 acute care facilities and 18 others. OVERALL APPROACH TO QUALITY AND SAFETY: The hospital embeds safety in the culture through strategic initiatives and enhances service and efficiency using Six Sigma and other techniques to drive adoption of improvements. Goals are selected in alignment with the annual strategic initiatives, which are chosen on the basis of satisfaction surveys, patient and family complaints, community advisory groups, and performance measures, among other sources. USE OF INFORMATION TO SET AND EVALUATE QUALITY GOALS AND PRIORITIZE INITIATIVES: A new business intelligence system enables online, dynamic analysis of performance results, replacing static paper reports. Advanced features in the clinical information systems include computerized physician order entry; interactive clinical alerts for decision support; a real-time infection control tracking system; and a clinical data warehouse supporting data mining and analysis for quality improvement, decision making, and education. APPROACH TO ADDRESSING THE SIX IOM QUALITY AIMS: To achieve clinical, service, and operational excellence, NYP focuses on all Institute of Medicine quality aims.

Hospital Bed Capacity, 500 and over↗
PubMed2005

Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS).

The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.

Algorithms↗
PubMed1990

Relationship between carcinogenicity in rodents and the induction of sister chromatid exchanges and chromosomal aberrations in Chinese hamster ovary cells.

Two independent analyses were carried out to compare the induction of sister chromatid exchanges and of chromosomal aberrations as predictors of carcinogenicity. Using both a classical and a Bayesian approach, as well as by analysis of the structural fragments generated by CASE, an artificial intelligence system, it is included that individually neither of these tests is a satisfactory predictor of carcinogenicity. However, because the analysis revealed that each of the cytogenetic assays responds to a different set of structural features associated with carcinogenicity, it can be concluded that the assays can be included in a battery of tests to improve predictivity.

Animals↗
PubMed1997

Standardized problem list generation, utilizing the Mayo canonical vocabulary embedded within the Unified Medical Language System.

UNLABELLED: VOCABULARY: The Mayo problem list vocabulary is a clinically derived lexicon created from the entries made to the Mayo Clinic's Master Sheet Index and the problem list entries made to the Impression/ Report/Plan section of the Clinical Notes System over the last three years. The vocabulary was reduced by eliminating repetition including lexical variants, spelling errors, and qualifiers (Administrative or Operational terms). Qualifiers are re-coordinated with other terms, at run-time, which greatly increased the number of input strings which our system is capable of recognizing. IMPLEMENTATION: The Problem Manager is implemented using standard windows tools in a Windows NT environment. The interface is designed using Object Pascal. HTTP calls are passed over the World Wide Web to a UNIX based vocabulary server. The server returns a document, which is read into Object Pascal structures, parsed, filtered and displayed. STUDY: This paper reports the results of a recent Usability Trial focused on assessing the viability of this mechanism for standardized problem entry. Eight clinicians engaged in eleven scenarios and responded as to their satisfaction with the systems performance. These responses were observed, videotaped and tabulated. Clinicians in this study were able to find acceptable diagnoses in 91.1% of the scenarios. The response time was acceptable in 92.5% of the scenarios. The presentation of related terms was stated to be useful in at least one scenario by seven of the eight participants. All clinicians wanted to make use of shortcuts which would minimize the amount of typing necessary to encode the concept they were searching for (e.g. Abbreviations, Word Completion). CONCLUSIONS: Clinicians are willing to choose a canonical term from a suggested list (as opposed to their own wording). Clinicians want an "intelligent" system, which would suggest terms within a category (e.g. Types of "Migraine"). They are able to make functional use of our system, in its current state of development. Finally, all clinicians appreciate the value of encoding their problems in a standardized vocabulary, toward improved research, education and practice.

Computer Communication Networks↗
PubMed2005

The fuzzy clustering analysis based on AFS theory.

In the framework of axiomatic fuzzy sets theory, we first study how to impersonally and automatically determine the membership functions for fuzzy sets according to original data and facts, and a new algorithmic framework of determining membership functions and their logic operations for fuzzy sets has been proposed. Then, we apply the proposed algorithmic framework to give a new clustering algorithm and show that the algorithm is feasible. A number of illustrative examples show that this approach offers a far more flexible and effective means for the intelligent systems in real-world applications. Compared with popular fuzzy clustering algorithms, such as c-means fuzzy algorithm and k-nearest-neighbor fuzzy algorithm, the new fuzzy clustering algorithm is more simple and understandable, the data types of the attributes can be various data types or subpreference relations, even descriptions of human intuition, and the distance function and the class number need not be given beforehand.

Algorithms↗
PubMed1995

Steering through the murky waters of a scientific conflict: situated and symbolic models of clinical cognition.

The situated action perspective, which embraces a diversity of views, challenges several of the fundamental assumptions of the symbolic information-processing framework underlying cognitive science and artificial intelligence. In this paper, we consider the following issues; symbolic representations, plans and actions, distributed cognition, and the transfer of learning. We evaluate each of these issues in terms of research and theories in clinical cognition and examine the implications for education and training, and for the integration of intelligent systems in medical practice. We argue for a reconceptualization of the symbolic framework in terms of the way the role of internal representations and cognitive activities are perceived. However, symbolic representations are integral to medical cognition and should continue to be central in any theoretical framework. A re-examination of cognitive science in medicine in terms of the relationship among physicians, technology, and the workplace could prove to be constructive in bridging the gap between theory and practice.

Artificial Intelligence↗
PubMed2005

Grammatical inference in bioinformatics.

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approaches to biological sequence analyses and related researches and focus on learning stochastic grammars from biological sequences and predicting their functions based on learned stochastic grammars.

Algorithms↗
PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗
PubMed2004

The intelligent data management system for toxicogenomics.

Toxicogenomics is now emerging as one of the most important genomic application because the toxicity test based on gene expression profiles is expected to be more precise and efficient than current histopathological approaches in a pre-clinical phase. One of the challenging issues in toxicogenomics is the construction of intelligent database management system which can deal with heterogeneous and complex data from many different experimental and information sources. TEST(Toxicogenomics for Efficient Safety Test) database is especially focused on the connectivity of heterogeneous data and the intelligent query system which enable users to obtain relevant useful information from the complex data sets. The database deals with four kinds of information; compound, histopathology, gene expression, and annotation information. Currently, TEST database maintains toxicogenomics information for 16 compounds, 45 microarrays, 190 animal experiments, and customized 4.8 K rat clone set. Our presented system is expected to be a good information source for studying of toxicology mechanism in the genome-wide level and can also be applied to the designing toxicity test chip.

Animals↗
PubMed1989

An analysis of pathology knowledge and decision making for the development of artificial intelligence-based consulting systems.

This paper partly addresses the question "What artificial intelligence (AI) tools are appropriate for which parts of pathology?" by analyzing the structure and components of knowledge in pathology (e.g., observations plus archival and reference data) and which aspects of that knowledge should be expressible in an AI consulting system. The different aspects of uncertainty (observational, prevalence and validity) play an important role in both human and computer-based decision-making processes, as do relationships between the components of knowledge. The design of an AI consultant system is discussed in terms of the way uncertainty is expressed and in how many parameters, the way uncertainty is propagated (Bayes, certainty factors, Dempster-Schafer, logic or Pathfinder heuristic methods), whether the system reasons from data to a conclusion or vice versa and what the aim of the system is. The suitability of an AI tool is determined by the knowable facts of the pathology subfield, by the match with its knowledge structure and by its requirements. While the success of an AI tool will partly depend on an appropriate definition of its scope, the appropriate combinatoric also depends on the expertise of the user.

Artificial Intelligence↗
PubMed1998

Animats: computer-simulated animals in behavioral research.

The term animat refers to a class of simulated animals. This article is intended as a nontechnical introduction to animat research. Animats can be robots interacting with the real world or computer simulations. In this article, the use of computer-generated animats is emphasized. The scientific use of animats has been pioneered by artificial intelligence and artificial life researchers. Behavior-based artificial intelligence uses animats capable of autonomous and adaptive activity as conceptual tools in the design of usefully intelligent systems. Artificial life proponents view some human artifacts, including informational structures that show adaptive behavior and self-replication, as animats may do, as analogous to biological organisms. Animat simulations may be used for rapid and inexpensive evaluation of new livestock environments or management techniques. The animat approach is a powerful heuristic for understanding the mechanisms that underlie behavior. The simple rules and capabilities of animat models generate emergent and sometimes unpredictable behavior. Adaptive variability in animat behavior may be exploited using artificial neural networks. These have computational properties similar to natural neurons and are capable of learning. Artificial neural networks can control behavior at all levels of an animat's functional organization. Improving the performance of animats often requires genetic programming. Genetic algorithms are computer programs that are capable of self-replication, simulating biological reproduction. Animats may thus evolve over generations. Selective forces may be provided by a human overseer or be part of the simulated environment. Animat techniques allow researchers to culture behavior outside the organism that usually produces it. This approach could contribute new insights in theoretical ethology on questions including the origins of social behavior and cooperation, adaptation, and the emergent nature of complex behavior. Animat studies applied to domestic animals have been few so far, and have involved simulations of space use by swine. I suggest other applications, including modeling animal movement during human handling and the effects of environmental enrichment on the satisfaction of behavioral needs. Appropriate use of animat models in a research program could result in savings of time and numbers of animals required. This approach may therefore come to be viewed as both ethically and economically advantageous.

Animal Welfare↗
PubMed2002

Karyotyping of comparative genomic hybridization human metaphases by using support vector machines.

BACKGROUND: Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method for detecting chromosomal imbalance. Karyotyping of human metaphases is an important step to assign each chromosome to one of 23 or 24 classes (22 autosomes and two sex chromosomes). Automatic karyotyping in CGH analysis is needed. However, conventional karyotyping approaches based on DAPI images require complex image enhancement procedures. METHODS: This paper proposes a simple feature extraction method, one that generates density profiles from original true color CGH images and uses normalized profiles as feature vectors without quantization. A classifier is developed by using support vector machine (SVM). It has good generalization ability and needs only limited training samples. RESULTS: Experiment results show that the feature extraction method of using color information in CGH images can improve greatly the classification success rate. The SVM classifier is able to acquire knowledge about human chromosomes from relatively few samples and has good generalization ability. A success rate of moe than 90% has been achieved and the time for training and testing is very short. CONCLUSIONS: The feature extraction method proposed here and the SVM-based classifier offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes.

Chromosomes, Human↗
PubMed2002

Karyotyping of comparative genomic hybridization human metaphases using kernel nearest-neighbor algorithm.

BACKGROUND: Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method that detects chromosomal imbalances. Automatic karyotyping is an important step in CGH analysis because the precise position of the chromosome abnormality must be located and manual karyotyping is tedious and time-consuming. In the past, computer-aided karyotyping was done by using the 4',6-diamidino-2-phenylindole, dihydrochloride (DAPI)-inverse images, which required complex image enhancement procedures. METHODS: An innovative method, kernel nearest-neighbor (K-NN) algorithm, is proposed to accomplish automatic karyotyping. The algorithm is an application of the "kernel approach," which offers an alternative solution to linear learning machines by mapping data into a high dimensional feature space. By implicitly calculating Euclidean or Mahalanobis distance in a high dimensional image feature space, two kinds of K-NN algorithms are obtained. New feature extraction methods concerning multicolor information in CGH images are used for the first time. RESULTS: Experiment results show that the feature extraction method of using multicolor information in CGH images improves greatly the classification success rate. A high success rate of about 91.5% has been achieved, which shows that the K-NN classifier efficiently accomplishes automatic chromosome classification from relatively few samples. CONCLUSIONS: The feature extraction method proposed here and K-NN classifiers offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes.

Algorithms↗
PubMed1994

Suitability of artificial neural networks for feature extraction from cardiotocogram during labour.

Fetal condition during labour is inferred from a continuous display of fetal heart rate and uterine contractions called the cardiotocogram (CTG). The CTG requires a considerable expertise for correct interpretation, which is not always available. We are developing an intelligent system to support clinical decision-making during labour. The system's performance depends on its ability to classify features from the CTG similarly to experts. Artificial neural networks (NNs) can be taught by experts for such tasks, and so may be particularly suitable. We found NNs suitable for feature extraction when the problem was reduced to small well defined tasks, and numerical algorithms were used to pre-process the raw data before application to the NNs. A NN with optimised dimensions was used in this way to classify the magnitude of decelerations, a feature clinicians find particularly difficult. The NN was compared with the algorithm used in a commercial antenatal monitor and six reviewers which included two CTG experts. The experts were consistent (89.7% and 97.0%) and agreed well with each other (81.0%), whereas the non-experts were less consistent and agreed less well. The NN agreed well with the experts (75.0% and 81.9%) but the algorithm agreed poorly (56.5% and 68.9%). It was found that the algorithm's performance could be improved (72.1% and 76.7%) when modified to use additional information. Our earlier attempts to fully classify the raw CTG using a single NN were unsuccessful because of the large number of data patterns. A simplified approach to classify the magnitude and timing of decelerations was also unsuitable when contraction data was of poor quality or absent.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗