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At least 217 records · Page 12Linked to original sources
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↗
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↗
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↗
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↗
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↗
PubMed1989

Artificial intelligence techniques for the control of cancer cells.

NEWCHEM, an artificial intelligence system for the control of cancer cell growth, is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to develop optimal strategies for the selective control of cancer cell through qualitative reasoning from first principles at cellular level.

Computer Simulation↗
PubMed1989

Structure characteristics of QMSOC and the relevant operators.

This article presents a further description on the background, significance, and structure characteristics of Quantitative Medicine Simulation and Operation by Computer (QMSOC). Also some basic operators were recommended for calculations of biomedical events such as estimation of substance concentrations, exploration of etiology, evaluation of biomedical effects, etc. At last some differences of QMSOC from other artificial intelligent systems in the medical field were discussed.

Computer Simulation↗
PubMed2000

[Blindness caused by an airbag in a minor accident].

PURPOSE: Installation of airbags has reduced the rate of fatal injuries in severe automobile accidents. We report, however, severe ocular injuries in a minor accident as the result of an airbag. CASE REPORT: A front passenger suffered a blunt ocular trauma of her right eye during a collision. The approaching speed was about 31 km/h. The maximum change of velocity in direction of the impact was 19 km/h. Color traces were found on the upper rim of the airbag, apparently from the patient's eye shadow. RESULTS: In the emergency room, visual acuity was reduced to light perception. There was endothelial contusion, traumatic mydriasis, and lens subluxation. A sclopetarian retinopathy developed with a chorioretinal scar. Eight months after the accident visual acuity remained at light projection only. CONCLUSIONS: The eye injuries had very probably been caused by the deploying airbag. Improvements are a better geometry of deployment (e.g., tethered airbags), release at higher impacts only, and "intelligent systems" with additional sensors to avoid potentially hazardous airbag inflation in minor accidents.

Accidents, Traffic↗
PubMed1993

Carotenoid content of fruits and vegetables: an evaluation of analytic data.

The test of the association between dietary intake of specific carotenoids and disease incidence requires the availability of accurate and current food composition data for individual carotenoids. To generate a carotenoid database, an artificial intelligence system was developed to evaluate data for carotenoid content of food in five general categories, namely, number of samples, analytic method, sample handling, sampling plan, and analytic quality control. Within these categories, criteria have been created to rate analytic data for beta-carotene, alpha-carotene, lutein, lycopene, and beta-cryptoxanthin in fruits and vegetables. These carotenoids are also found in human blood. Following the evaluation of data, acceptable values for each carotenoid in the foods were combined to generate a database of 120 foods. The database includes the food description; median, minimum, and maximum values for the specific carotenoids in each food; the number of acceptable values and their references; and a confidence code, which is an indicator of the reliability of a specific carotenoid value for a food. The carotenoid database can be used to estimate the intake of specific carotenoids in order to examine the association between dietary carotenoids and disease incidence.

Carotenoids↗
PubMedDate not supplied

Artificial intelligence and Bayesian decision theory in the prediction of chemical carcinogens.

Two procedures for predicting the carcinogenicity of chemicals are described. One of these (CASE) is a self-learning artificial intelligence system that automatically recognizes activating and/or deactivating structural subunits of candidate chemicals and uses this to determine the probability that the test chemical is or is not a carcinogen. If the chemical is predicted to be carcinogen, CASE also projects its probable potency. The second procedure (CPBS) uses Bayesian decision theory to predict the potential carcinogenicity of chemicals based upon the results of batteries of short-term assays. CPBS is useful even if the test results are mixed (i.e. both positive and negative responses are obtained in different genotoxic assays). CPBS can also be used to identify highly predictive as well as cost-effective batteries of assays. For illustrative purposes the ability of CASE and CPBS to predict the carcinogenicity of a carcinogenic and a non-carcinogenic polycyclic aromatic hydrocarbon is shown. The potential for using the two methods in tandem to increase reliability and decrease cost is presented.

Animals↗
PubMed1988

CASE, the computer-automated structure evaluation method, correctly predicts the low mutagenicity for Salmonella of nitrated cyclopenta-fused polycyclic aromatic hydrocarbons.

Recently Goldring et al. [Mutation Res., 187 (1987) 67-77] reported the synthesis and purification of a series of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons. On the basis of expected charge distributions, these chemicals were predicted to be potent mutagens and, yet, contrary to expectation, they were found to be only weakly mutagenic for Salmonella. In their discussion, the authors suggest that application of CASE, an artificial intelligence system recently developed in these laboratories, would also not predict the low mutagenicity of this group of chemicals. In the present report, it is shown that CASE, in fact, correctly predicts the low mutagenicity of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons.

Mutagenicity Tests↗
PubMed2004

Metabolite profiling as an aid to metabolic engineering in plants.

The past decade has seen some impressive successes in the metabolic engineering of biotechnologically important plant pathways. However, plant metabolic engineering currently proceeds more by trial and error than by intelligent system design. A change in philosophy away from studying pathways in isolation and towards studying metabolism as a network is necessary. To support this development, improvements in technologies for metabolic analysis, a wider adoption of metabolite-profiling approaches and significant innovations in data analysis methodologies are required.

Biotechnology↗
PubMed1986

In vivo probes: problems and perspectives.

Devices constructed for potential use as invasive bioprobes incorporate a selective receiving site for molecular or ionic recognition, and a transducer which is capable of translating a perturbation of physical chemistry of the determinant-site reaction (interaction) into a usable signal. Four types are envisioned--implants for general hospital use, transient-use probes to replace classical blood tests, short-term implantable probes and the long-term variety. Performance criteria are selectivity, sensitivity, fast response, site-reversible, small, rugged, inexpensive, biocompatible, calibratible, facile use by non-expert personnel and ease of telemetry. These demands, not surprisingly, create enormous challenges to the sensor specialist. With respect to biocompatibility the sensor must not be involved in infection, clot formation or antigenic response, and, furthermore, protein adsorption, etc., which can affect the sensor response should be avoided. Calibration remains a problem of monumental proportions. Many devices drift from calibrated levels even in in vitro experiments, let alone in the implanted milieu. One solution has been to carry out on-line switching between patient blood and standard solutions. However, this type of approach leaves a lot to be desired with respect to portability. Another method which is attracting increasing attention is the chemometric or artificial intelligence system involving compensation by multi-sensor array configurations. Sensitivity and limit-of-detection have attracted little research due to the overwhelming nature of other difficulties. In the present paper we evaluate a number of these technical problems and discuss the architecture of devices that are currently available. Finally, some thoughts as to priorities for re-directing sensor research in the bioprobe area are presented.

Biocompatible Materials↗