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PubMed2026

Invisible Threats, Relentless Hunters: Biosurveillance of Airborne Plant Pathogens.

Airborne dispersal enables plant pathogens to travel across fields, regions, and continents, fueling rapid epidemics and emerging disease threats. Biosurveillance, the systematic monitoring of airborne inoculum, offers the opportunity to detect pathogens before symptoms appear and informs timely, risk-based management. Recent advances in air sampling, molecular diagnostics, metagenomics, and imaging technologies have expanded the scale and resolution of pathogen monitoring, from single-species qPCR assays to community-level aerobiome surveys. Integration of biosurveillance data with decision-support systems, remote sensing, and artificial intelligence is transforming early-warning capabilities and providing novel insights into pathogen ecology, evolution, and fungicide resistance. Yet major challenges remain, including assay standardization, data interpretation, and translation into actionable tools for growers. This review synthesizes current approaches, highlights case studies in which biosurveillance has advanced disease management, and outlines future directions toward coordinated surveillance networks and precision agriculture applications.

Air Microbiology↗
PubMedDate not supplied

Postdoctoral training in medical informatics: a survey of National Library of Medicine-supported fellows.

The National Library of Medicine (NLM) funds training programs in medical informatics and plans to significantly increase the number of program sites in the future. The authors surveyed all NLM-funded trainees at the nine sites supported in the spring of 1988 to determine their backgrounds, current research interests, and career plans. Forty-three fellows were identified, of whom 39 returned a mailed questionnaire. All but four were physicians (89.7%), 82.1% had at least one year of postdoctoral clinical training, and 61.5% had completed a residency. Seventy-one percent of those completing residency had done so in internal medicine. The most common areas of current research were decision support/decision analysis, knowledge representation, and artificial intelligence. The overwhelming majority of the fellows planned to seek positions in a medical school on completion of their fellowships, and most preferred affiliation with a department of medical informatics or medicine.

Education, Continuing↗
PubMed2003

Homecare: a telemedical application.

The advent of telecommunication and information technologies and the miniaturisation of technologies have enabled the evolution of telemonitoring systems. Early systems for hospitals and clinics, which usually required the patients to be wired to desktop devices, have evolved into homecare that requires devices to be lighter and simpler to use. In addition to miniaturisation and extended autonomy, further requirements for the telemonitoring system include local intelligence (that is, the system can take decisions without referring to external advice) and no moving parts to allow the patient to move freely during measurement periods. Body and local communication networks can prolong the connection of the homecare patient with a monitoring centre through the public networks. Additional medical functions and processing have been added to homecare equipment for telemedicine and patient discomfort is decreasing because of miniaturisation, autonomy and increased versatility of new systems. The applications have evolved rapidly from manually triggered alarms and single physiological parameter monitors to autonomous telemedical monitoring tailored to complete needs. This will eventually make telemedicine beneficial to patients, doctors and society.

Home Care Services↗
PubMed1996

The impact of clinical information systems research on the future of advanced practice nursing.

Expert systems are "intelligent" computer programs designed to mimic the decision making of a human expert. This article describes two formal evaluations of one of the first nursing expert systems. Initial results were positive, however, subsequent analysis identified significant limitations in the ability of the expert system to mimic the consultation process of advanced practice nurses (APNs). Because few newly developed systems are being subjected to a clinical trial or to the scrutiny reported here, developing systems may prove to be cost inefficient over time. APNs must be actively involved in the design, development, and evaluation of all nursing expert systems.

Expert Systems↗
PubMed2000

Dimension reduction for highdimensional online-monitoring data in intensive care.

Nowadays high dimensional data in intensive care medicine can be captured, stored, and retrieved with the help of clinical information systems. Intelligent alarm systems are needed for an adequate bedside decision support, in the course of which the detection of qualitative patterns in physiologic monitoring data such as outliers, level changes, or trends aims at a proper classification of the patients state. Statistical time series techniques have already been applied successfully to the analysis of single physiological variables. The simultaneous online analysis of the multivariate patient curve yields further challenges. We describe methods for reducing the dimension and for keeping the computational efforts necessary for monitoring low. We present preliminary results of an ongoing study on monitoring critically ill patients.

Adult↗
PubMed2001

Comparison of three databases with a decision tree approach in the medical field of acute appendicitis.

Decision trees have been successfully used for years in many medical decision making applications. Transparent representation of acquired knowledge and fast algorithms made decision trees one of the most often used symbolic machine learning approaches. This paper concentrates on the problem of separating acute appendicitis, which is a special problem of acute abdominal pain from other diseases that cause acute abdominal pain by use of an decision tree approach. Early and accurate diagnosing of acute appendicitis is still a difficult and challenging problem in everyday clinical routine. An important factor in the error rate is poor discrimination between acute appendicitis and other diseases that cause acute abdominal pain. This error rate is still high, despite considerable improvements in history-taking and clinical examination, computer-aided decision-support and special investigation, such as ultrasound. We investigated three different large databases with cases of acute abdominal pain to complete this task as successful as possible. The results show that the size of the database does not necessary directly influence the success of the decision tree built on it. Surprisingly we got the best results from the decision trees built on the smallest and the biggest database, where the database with medium size (relative to the other two) was not so successful. Despite that we were able to produce decision tree classifiers that were capable of producing correct decisions on test data sets with accuracy up to 84%, sensitivity to acute appendicitis up to 90%, and specificity up to 80% on the same test set.

Abdomen, Acute↗
PubMed2002

Decision support system for classification of epilepsies in childhood.

Diagnosis of epilepsy in childhood is often difficult as the symptoms are often atypical and the epilepsy syndromes are multiform. Methods from the domain of artificial intelligence give the opportunity to formalize medical knowledge and standardize various diagnostic procedures in specific domains of medicine. We developed a decision support system using artificial intelligence techniques for the classification and ultimately the diagnosis of epilepsies and epilepsy syndromes in children. The system incorporates knowledge from the International Classification of Epilepsies and Epileptic Syndromes. It was assessed using clinical data and the system's conclusions were compared with the diagnoses proposed by an experienced doctor. The system and the physician reached identical diagnoses in 85.2% of the cases. In an additional 8.2% of the cases, the system's diagnosis was similar to that of the physician, thus raising its overall success rate to 93.4%. The system can be helpful, especially for trainees, since it only needs to import the clinical and laboratory data. Decision making and differential diagnosis are then performed automatically.

Child↗
PubMed2005

Modeling human behaviors and reactions under dangerous environment.

This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools Dev. Programming within Virtools Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed.

Artificial Intelligence↗
PubMed1996

A design for a World Wide Web decision-support system using a controlled medical terminology.

We have developed a medical decision-support system based on Arden Syntax for Medical Logic Modules and hypertext using the World Wide Web. The two representations are integrated to provide a better platform for decision support. To manage the integration a controlled medical terminology has been used as a well-defined interface between the representations. The terminology is also used to facilitate communication between specialists and non-specialists.

Artificial Intelligence↗
PubMed1999

The use of physician domain knowledge to improve the learning of rule-based models for decision-support.

This paper describes a study testing the hypothesis that the learning of a decision-support model by a computer learning algorithm from clinical data can be improved by the addition of domain knowledge from practicing physicians. The domain of the experiment is community-acquired pneumonia. The overall design of the study compares a computer learning algorithm given clinical data to one given clinical data plus domain knowledge added by physician subjects. This study showed that the performance of the computer-generated models augmented with knowledge added by physician subjects were significantly better than the computer-generated models generated without added knowledge using a two-stage rule induction algorithm in the domain of community-acquired pneumonia. This result was highly significant and shows that the addition of domain knowledge may be beneficial to the learning of clinical decision-support models, especially in domains where data is limited.

Algorithms↗
PubMed1998

The limitations of decision trees and automatic learning in real world medical decision making.

The decision tree approach is one of the most common approaches in automatic learning and decision making. It is popular for its simplicity in constructing, efficient use in decision making and for simple representation, which is easily understood by humans. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. The training sets are usually large enough for learning algorithm to construct a hypothesis consistent with the underlying concept. But in real life it is often impossible to find the desired number of training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, unavailability of all attributes at the same time are the typical representatives. There are different ways to deal with some of these problems, but in a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions. We have measured the values of 24 attributes before and after the 82 operations of children in age between 2 and 10 years. The aim was to find the dependencies between attribute values and a child's predisposition to acidemia--the decrease of blood's pH. Our main interest was in discovering predisposition to two forms of acidosis, the metabolic acidosis and the respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects, which was complete (there were no missing attribute values), but on the other hand not large enough to avoid the effect of overfitting. A common approach to evaluation of a decision tree is the use of a test set. In our case we decided that instead of using a test set, we ask medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments on the generated trees can be found in this paper. The comments show, that trees generated from available training set mainly have surprisingly good branches, but on the other hand some are very "stupid" and no medical explanation could be found. Thereafter we can conclude, that the decision tree concept and automatic learning can be successfully used in real world situations, constrained with the real world limitations, but they should be used only with the guidelines of appropriate medical experts.

Acidosis↗
PubMed1997

Algorithm analysis of lectin glycohistochemistry and Feulgen cytometry for a new classification of nasal polyposis.

The aim of this study is to present a new classification of nasal polyps. This classification is based both on morphologic criteria relating to morphonuclear features from isolated Feulgen-stained nuclei and on glycohistochemical characteristics from histologic slides submitted to three lectins (peanut, wheat germ, and gorse seed agglutinins) and one neoglycoconjugate glycohistochemical stain. While the morphonuclear features (including 30 variables) relate essentially to chromatin pattern, the glycohistochemical stains (including 16 variables) are linked to the presence of specific carbohydrate moieties in cell membranes and cytoplasm. Forty-nine nasal polyps, including single polyps, diffuse polyposis, cystic fibrosis-related polyposis, and aspirin idiosyncracy-related polyposis associated with asthma, were thus characterized. All the variables were obtained quantitatively by means of computer-assisted microscopy. Two complementary methods of data classification were used to determine the actual diagnostic value contributed by each quantitative variable, namely, discriminant analysis, which forms part of multifactorial statistical analysis, and the decision tree technique, which is an artificial intelligence-related algorithm. The data so obtained show that our morphologic classification of nasal polyps fits in with the classification of nasal polyps defined on the basis of clinical criteria.

Algorithms↗
PubMed1994

Neural networks and psychiatry: candidate applications in clinical decision making.

Neural networks comprise a fundamentally new type of computer system inspired by the functioning of neurons in the brain. Such networks are good at solving problems that involve pattern recognition and categorisation. An important difference between a neural network and a traditional computer system is that in developing an application, a neural network is not programmed; instead, it is trained to solve a particular type of problem. This ability to learn to solve a problem makes neural networks adaptable to solving a wide variety of problems, some of which have proved intractable using a traditional computing approach. Neural networks are particularly suited to tasks involving the categorisation of patterns of information, such as is required in diagnosis and clinical decision making. In the last three years reports of applications involving neural networks have begun to appear in the medical literature, and these are described in this paper. However, a comprehensive search of the literature has shown that there have not as yet been reports of any applications in psychiatry. This paper discusses the nature of clinical decision making, outlines the sorts of problems in psychiatry which neural networks applications might be developed to address, and gives examples of candidate applications in clinical decision making.

Adult↗
PubMed1997

Real time validation of paediatric biochemical reports using the Valab-Biochem system.

Validation of biochemical reports must be fast and clinically accurate to be of assistance to clinicians. Considerable skill is required to analyse the consistency of different data in the report and to consider influences on the data. When performed throughout the day, such analysis is time-consuming and uncertain. We therefore decided to use a computer-assisted validation system, Valab-Biochem. Its decisions result from a decision tree based primarily on the intrinsic consistency of the data, validation ranges and patients' sex, age and hospital ward. Three hundred randomly chosen reports were simultaneously submitted to Valab-Biochem and to five biologists in order to analyse the computer's findings. The sensitivity of Valab-Biochem was 80% compared to biologists' consensus decision, which was taken as the gold standard. The specificity was 78%. This system provided autonomous assessment of the reports and could be used as an initial screen to assist biologists and focus attention on potentially inconsistent reports.

Artificial Intelligence↗
PubMed2006

Three-class ROC analysis--a decision theoretic approach under the ideal observer framework.

Receiver operating characteristic (ROC) analysis is well established in the evaluation of systems involving binary classification tasks. However, medical tests often require distinguishing among more than two diagnostic alternatives. The goal of this work was to develop an ROC analysis method for three-class classification tasks. Based on decision theory, we developed a method for three-class ROC analysis. In this method, the objects were classified by making the decision that provided the maximal utility relative to the other two. By making assumptions about the magnitudes of the relative utilities of incorrect decisions, we found a decision model that maximized the expected utility of the decisions when using log-likelihood ratios as decision variables. This decision model consists of a two-dimensional decision plane with log likelihood ratios as the axes and a decision structure that separates the plane into three regions. Moving the decision structure over the decision plane, which corresponds to moving the decision threshold in two-class ROC analysis, and computing the true class 1, 2, and 3 fractions defined a three-class ROC surface. We have shown that the resulting three-class ROC surface shares many features with the two-class ROC curve; i.e., using the log likelihood ratios as the decision variables results in maximal expected utility of the decisions, and the optimal operating point for a given diagnostic setting (set of relative utilities and disease prevalences) lies on the surface. The volume under the three-class surface (VUS) serves as a figure-of-merit to evaluate different data acquisition systems or image processing and reconstruction methods when the assumed utility constraints are relevant.

Algorithms↗
PubMedDate not supplied

Choice and explanation in medical management: a multiattribute model of artificial intelligence approaches.

This paper explores a model of choice and explanation in medical management and makes clear its advantages and limitations. The model is based on multiattribute decision making (MADM) and consists of four distinct strategies for choice and explanation, plus combinations of these four. Each strategy is a restricted form of the general MADM approach, and each makes restrictive assumptions about the nature of the domain. The advantage of tailoring a restricted form of a general technique to a particular domain is that such efforts may better capture the character of the domain and allow choice and explanation to be more naturally modelled. The uses of the strategies for both choice and explanation are illustrated with analyses of several existing medical management artificial intelligence (AI) systems, and also with examples from the management of primary breast cancer. Using the model it is possible to identify common underlying features of these AI systems, since each employs portions of this model in different ways. Thus the model enables better understanding and characterization of the seemingly ad hoc decision making of previous systems.

Algorithms↗
PubMed2004

A computational TW3 classifier for skeletal maturity assessment. A Computing with Words approach.

This paper proposes a fuzzy methodology to translate the natural language descriptions of the TW3 method for bone age assessment into an automatic classifier. The classifier is built upon a modified version of a fuzzy ID3 decision tree. No large data records are needed to train the classifier, i.e., to find out the classification rules, since the classifier is built upon rules given by the TW3 method. Only small data records are needed to fine-tune the fuzzy sets used to implement the rulebase.

Adolescent↗