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PubMed1995

Utilization of laboratory resources: developments in knowledge-based ordering systems.

This paper describes two rule based decision support systems. The first system is used to screen incoming test requests for adequacy on the basis of signs and symptoms volunteered by the requesting GPs. The system was tested using a database of 794 requests for a TSH test. About 17% of the test requests were correctly identified as unnecessary. In total, 0.5% of the tests were incorrectly labelled as unnecessary. This concerned 4% of the patients that appeared to have hyperthyroidism and 23% of the patients that appeared to have hypothyroidism on the basis of TSH and FT4 results. The other system is a rule-based clinical decision support system for the requesting of laboratory investigations, originally designed for use at a hospital within the UK, that was implemented in a predominantly French-speaking hospital in Belgium. This involved the modification of the system to allow multilingual operation, and also the implementation of a completely new set of investigation protocols. The purpose of this study was to assess the transferability, both of the system itself, and of its benefits. The system was introduced gradually and has only recently been in full operation. However, the findings from the first months of routine use of the system indicate that the transfer of the system to a different clinical environment has been successful. Although it is too early to assess fully the impact on laboratory utilization, the clinicians believe that it is improving the appropriateness of investigations.

Artificial Intelligence↗
PubMed2000

Model selection for a medical diagnostic decision support system: a breast cancer detection case.

There are a number of different quantitative models that can be used in a medical diagnostic decision support system (MDSS) including parametric methods (linear discriminant analysis or logistic regression), non-parametric models (K nearest neighbor, or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. Practitioners are left to either choose a favorite model or to test a small subset using cross validation methods. This paper illustrates the use of a self-organizing map (SOM) to guide model selection for a breast cancer MDSS. The topological ordering properties of the SOM are used to define targets for an ideal accuracy level similar to a Bayes optimal level. These targets can then be used in model selection, variable reduction, parameter determination, and to assess the adequacy of the clinical measurement system. These ideas are applied to a successful model selection for a real-world breast cancer database. Diagnostic accuracy results are reported for individual models, for ensembles of neural networks, and for stacked predictors.

Algorithms↗
PubMed2005

Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.

MOTIVATION: Although the outbreak of the severe acute respiratory syndrome (SARS) is currently over, it is expected that it will return to attack human beings. A critical challenge to scientists from various disciplines worldwide is to study the specificity of cleavage activity of SARS-related coronavirus (SARS-CoV) and use the knowledge obtained from the study for effective inhibitor design to fight the disease. The most commonly used inductive programming methods for knowledge discovery from data assume that the elements of input patterns are orthogonal to each other. Suppose a sub-sequence is denoted as P2-P1-P1'-P2', the conventional inductive programming method may result in a rule like 'if P1 = Q, then the sub-sequence is cleaved, otherwise non-cleaved'. If the site P1 is not orthogonal to the others (for instance, P2, P1' and P2'), the prediction power of these kind of rules may be limited. Therefore this study is aimed at developing a novel method for constructing non-orthogonal decision trees for mining protease data. RESULT: Eighteen sequences of coronavirus polyprotein were downloaded from NCBI (http://www.ncbi.nlm.nih.gov). Among these sequences, 252 cleavage sites were experimentally determined. These sequences were scanned using a sliding window with size k to generate about 50,000 k-mer sub-sequences (for short, k-mers). The value of k varies from 4 to 12 with a gap of two. The bio-basis function proposed by Thomson et al. is used to transform the k-mers to a high-dimensional numerical space on which an inductive programming method is applied for the purpose of deriving a decision tree for decision-making. The process of this transform is referred to as a bio-mapping. The constructed decision trees select about 10 out of 50,000 k-mers. This small set of selected k-mers is regarded as a set of decisive templates. By doing so, non-orthogonal decision trees are constructed using the selected templates and the prediction accuracy is significantly improved.

Algorithms↗
PubMed2001

Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models.

BACKGROUND: In busy clinical settings, physicians often do not have enough time to identify patients for specific therapeutic guidelines. As a solution, decision support systems could automatically identify eligible patients and trigger computerized guidelines for specific diseases. Applying this idea to community-acquired pneumonia (CAP), we developed a Bayesian network (BN) and an artificial neural network (ANN) for identifying patients who have CAP and are eligible for a pneumonia guideline. OBJECTIVE: The aim of this study was to determine whether the diagnostic accuracy of these two decision support models differs in terms of identifying CAP patients. METHODS: We trained and tested the networks with a data set of 32,662 adult patients. For each network, we (1) calculated the specificity, the positive predictive value (PPV), and the negative predictive value (NPV) at a sensitivity of 95%, and (2) determined the area under the receiver operating characteristic curve (AUC) as a measure of overall accuracy. We tested for statistical difference between the AUCs using the correlated area z statistic. RESULTS: At a sensitivity of 95%, the respective values for specificity, PPV, and NPV were: 92.3%, 15.1%, and 99.9% for the BN, and 94.0%, 18.6%, and 99.9% for the ANN. The BN had an AUC of 0.9795 (95% CI: 0.9736, 0.9843), and the ANN had an AUC of 0.9855 (95% CI: 0.9805, 0.9894). The difference between the AUCs was statistically significant (p=0.0044). CONCLUSIONS: The networks achieved high overall accuracies on the testing data set. Because the difference in accuracies is statistically significant but not clinically significant, both networks are equally suited to drive a guideline.

Area Under Curve↗
PubMed2006

Machine learning in sedimentation modelling.

The paper presents machine learning (ML) models that predict sedimentation in the harbour basin of the Port of Rotterdam. The important factors affecting the sedimentation process such as waves, wind, tides, surge, river discharge, etc. are studied, the corresponding time series data is analysed, missing values are estimated and the most important variables behind the process are chosen as the inputs. Two ML methods are used: MLP ANN and M5 model tree. The latter is a collection of piece-wise linear regression models, each being an expert for a particular region of the input space. The models are trained on the data collected during 1992-1998 and tested by the data of 1999-2000. The predictive accuracy of the models is found to be adequate for the potential use in the operational decision making.

Algorithms↗
PubMed2006

A cognitive blueprint of collaboration in context: distributed cognition in the psychiatric emergency department.

OBJECTIVE: The complex cognitive processes that underlie human performance in 'messy' contexts such as critical care medicine suggest a need for a cognitive model with broad scope to support the understanding of error in such domains. The objective of this research is to characterize the cognition that underlies patient care in the domain of emergency psychiatry in order to enhance the understanding of error in this context. METHODS AND MATERIALS: The theoretical framework of distributed cognition has been used to study collaborative decision-making in a number of similarly complex environments such as airline cockpits and air traffic control towers. These environments share certain characteristics with the critical care domain: the work is collaborative in nature, it is supported by artifacts that can be studied directly, and the consequences of error are dire. However, the nature of the work in this domain and the artifacts used to support it are unique. The application of the theoretical constructs of distributed cognition to this context is necessary in order to characterize the collective thinking that underlies critical care. Our research uses a combination of ethnographic and interview data to derive a distributed cognitive model of the psychiatric emergency department (PED), a high volume clinical unit dealing exclusively with the acute phases of psychiatric crises. The dynamics of workflow within the department are complex: several types of clinician collaborate by forming temporary multidisciplinary teams that attach to and manage particular patients. The component members of these teams change over time. RESULTS: Using the theoretical framework of distributed cognition, we interpreted the collected data to derive a cognitive model of the distribution of work and information flow in the PED. This modeling process has revealed several latent flaws in the system related to the underlying distribution of cognition across teams, time, space and artifacts. CONCLUSIONS: The characterization of this distribution has enhanced our understanding of the cognitive dynamics underlying error in this environment, and will serve to guide future research on error management in the ED and inform the development of context-appropriate error-management systems.

Artificial Intelligence↗
PubMed1993

Artificial neural networks for predicting failure to survive following in-hospital cardiopulmonary resuscitation.

BACKGROUND: Neural networks are an artificial intelligence technique that uses a set of nonlinear equations to mimic the neuronal connections of biological systems. They have been shown to be useful for pattern recognition and outcome prediction applications, and have the potential to bring artificial intelligence techniques to the personal computers of practicing physicians, assisting them with a variety of medical decisions. It is proposed that such an artificial neural network can be trained, using information available at the time of admission to the hospital, to predict failure to survive following in-hospital cardiopulmonary resuscitation (CPR). METHODS: The age, sex, heart rate, and 21 other clinical variables were collected on a consecutive series of 218 adult patients undergoing CPR at a 295-bed public acute-care hospital. The data set was divided into two groups. A neural network was trained to predict failure to survive to discharge following CPR, using one group as the training set and the other as the testing set. The procedure was then reversed, and the results of the two networks were combined to form an aggregate network. RESULTS: The trained aggregate neural network had a sensitivity of 52.1% and a positive predictive value of 97% for the prediction of failure to survive following CPR. The relative risk of actually failing to survive to discharge following CPR for a patient predicted not to survive was 11.3 (95% CI 3.3 to 38.2). CONCLUSIONS: Predicting failure to survive following CPR is but one possible application of neural network technology. It demonstrates how this technique can assist physicians in medical decision making. Future work should attempt to improve the positive predictive value of the neural network, to consider combining it with an expert system, and to compare it with other predictive tools. Once validated, the network can be distributed as a separate application for use by practicing physicians.

Adolescent↗
PubMed2001

There are stable individual differences in performance variability, both from moment to moment and from day to day.

Individual differences in decision speed have been regarded as direct reflections of a "primitive" functional neurophysiological characteristic, which affects performance on all cognitive tasks and so may be regarded as the "biological basis of intelligence", or of age-related changes in mental abilities. More detailed analyses show that variability within an experimental session (WSV) is a stable individual difference characteristic and that mean choice reaction times (CRTs) are gross summary statistics that reflect variability, rather than maximum speed of performance. A total of 98 people aged from 60 to 80 years completed 36 weekly sessions on six different letter categorization tasks. After effects of practice and of circadian variability had been eliminated, individuals with lower scores on the Cattell Culture Fair intelligence test had slower CRTs and greater WSV on all tasks. A simulation study showed that the greater WSVs of low Cattell scorers led directly to the significantly greater variability of their mean CRTs from session to session. However because CRTs on tasks co-varied from session to session it was apparent that, besides being affected by WSV, individuals' between-session variabilities (BSVs) also vary because of state changes that affect their performance from day to day. It seems that both variability in performance from trial to trial during a session and variability in average performance from day to day are correlated, stable, individual difference characteristics that vary inversely with intelligence test performance. Methodological consequences of these results for interpretations of age-related cognitive changes, for variability between as well as within individuals, for individual differences in decision speed, and for circadian variability in performance are discussed.

Aged↗
PubMed2002

Computer-aided diagnosis of breast tumors with different US systems.

RATIONALE AND OBJECTIVES: The authors performed this study to determine whether a computer-aided diagnostic (CAD) system was suitable from one ultrasound (US) unit to another after parameters were adjusted by using intelligent selection algorithms. MATERIALS AND METHODS: The authors used texture analysis and data mining with a decision tree model to classify breast tumors with different US systems. The databases of training cases from one unit and testing cases from another were collected from different countries. Regions of interest on US scans and co-variance texture parameters were used in the diagnosis system. Proposed adjustment schemes for different US systems were used to transform the information needed for a differential diagnosis. RESULTS: Comparison of the diagnostic system with and without adjustment, respectively, yielded the following results: accuracy, 89.9% and 82.2%; sensitivity, 94.6% and 92.2%; specificity, 85.4% and 72.3%; positive predictive value, 86.5% and 76.8%; and negative predictive value, 94.1% and 90.4%. The improvement in accuracy, specificity, and positive predictive value was statistically significant. Diagnostic performance was improved after the adjustment. CONCLUSION: After parameters were adjusted by using intelligent selection algorithms, the performance of the proposed CAD system was better both with the same and with different systems. Different resolutions, different setting conditions, and different scanner ages are no longer obstacles to the application of such a CAD system.

Algorithms↗
PubMed2006

Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees.

Toe-walking is one of the most prevalent gait deviations and has been linked to many diseases. Three major ankle kinematic patterns have been identified in toe-walkers, but the relationships between the causes of toe-walking and these patterns remain unknown. This study aims to identify these relationships. Clearly, such knowledge would increase our understanding of this gait deviation, and could help clinicians plan treatment. The large quantity of data provided by gait analysis often makes interpretation a difficult task. Artificial intelligence techniques were used in this study to facilitate interpretation as well as to decrease subjective interpretation. Of the 716 limbs evaluated, 240 showed signs of toe-walking and met inclusion criteria. The ankle kinematic pattern of the evaluated limbs during gait was assigned to one of three toe-walking pattern groups to build the training data set. Toe-walker clinical measurements (range of movement, muscle spasticity and muscle strength) were coded in fuzzy modalities, and fuzzy decision trees were induced to create intelligible rules allowing toe-walkers to be assigned to one of the three groups. A stratified 10-fold cross validation situated the classification accuracy at 81%. Twelve rules depicting the causes of toe-walking were selected, discussed and characterized using kinematic, kinetic and EMG charts. This study proposes an original approach to linking the possible causes of toe-walking with gait patterns.

Biomechanical Phenomena↗
PubMed1990

Intellectual and adaptive functioning: some tables for interpreting the Vineland in combination with intelligence tests.

A set of tables was provided with the differences necessary for statistical significance between the Vineland Adaptive Behavior Scales and the Bayley Scales of Infant Development. McCarthy Scales of Children's Abilities, Stanford-Binet Intelligence Scale (4th ed.), and all current Wechsler intelligence scales. This information may be beneficial in determining whether discrepancies in the Vineland and measures of intelligence can be attributed to chance alone or whether they represent real differences. In light of normative sampling diversity, regression effects, and procedural factors, which result in varying and indeterminate error in the tables, the tables can only augment clinical decision, not replace it.

Adaptation, Psychological↗
PubMed2003

An intelligent controller for automated operation of sequencing batch reactors.

In this paper the results are presented of original research into the automatic and "intelligent" detection of breakpoints in Dissolved Oxygen (DO) profiles. The research has been based on a large body of data collected from laboratory SBRs operating on synthetic wastewater. Two different approaches were followed to identify the endpoints. The paper analyses and evaluates the results of automatic detection on the basis of geometric features in the DO profiles. This was followed by classification of the detected breakpoints using different soft computing techniques based on Neural Network (NN), Fuzzy Neural Network (FuNN) and Evolving Fuzzy Neural Network (EfuNN) software systems for breakpoint classification. A high rate of successful detection and classification was obtained with up to 96% of the decisions made correctly. In order to overcome the limitations of this system to adapt to dynamically changing process conditions, an intelligent control model was developed by a combination between an Evolving Fuzzy Neural Net (EfuNN) combined with a logic decision unit. This system has the ability to "learn on-the-fly" and adjust its response pattern in order to maintain a high rate of successful breakpoint detection under varying changing process conditions. This software system has been sucessfully embedded on a small programmable controller for integration into larger process control systems for the operation of SBR plants.

Automation↗
PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗
PubMed2003

Fuzzy connectives as a combination tool in a hybrid multi-neural system.

The set of fuzzy connectives can be seen as an important combination tool, such as in combining the antecedent sets of the rules, in multi-criteria decision making and in combining the outputs of neural classifiers in a multi-neural system. This papers investigates the performance of some fuzzy combination schemes applied to a multi hybrid neural system which is composed of neural and fuzzy neural networks. An empirical evaluation in a handwritten numeral recognition task is used to investigate the performance of the presented fuzzy methods with some existing combination methods.

Artificial Intelligence↗
PubMed1995

Computerized consensus diagnosis: a classification strategy for the robust analysis of MR spectra. I. Application to 1H spectra of thyroid neoplasms.

We introduce and apply a new classification strategy we call computerized consensus diagnosis (CCD). Its purpose is to provide robust, reliable classification of biomedical data. The strategy involves the cross-validated training of several classifiers of diverse conceptual and methodological origin on the same data, and appropriately combining their outcomes. The strategy is tested on proton magnetic resonance spectra of human thyroid biopsies, which are successfully allocated to normal or carcinoma classes. We used Linear Discriminant Analysis, a Neural Net-based method, and Genetic Programming as independent classifiers on two spectral regions, and chose the median of the six classification outcomes as the consensus. This procedure yielded 100% specificity and 100% sensitivity on the training sets, and 100% specificity and 98% sensitivity on samples of known malignancy in the test sets. We discuss the necessary steps any classification approach must take to guarantee reliability, and stress the importance of fuzziness and undecidability in robust classification.

Adenocarcinoma, Follicular↗
PubMedDate not supplied

Three- and four-dimensional visualization of magnetic resonance imaging data sets in pediatric cardiology.

The purpose of medical imaging technology in pediatric cardiology is to provide clear representations of the underlying anatomy and physiology of the cardiovascular system--representations that are easily understood and that facilitate clinical decision making. However, standard projective and tomographic imaging methods often yield results that are intelligible only to imaging specialists. Three- and four-dimensional reconstructions from projective and tomographic data sets are an alternative form of image display. Often, these reconstructions are more readily comprehensible as representations of the reality apparent in the operating room or the pathology laboratory than are the original data sets. Furthermore, viewing of these reconstructions is much more time efficient than viewing hundreds of separate tomographic images. Magnetic resonance imaging inherently provides three-, four-, and even higher dimensional data, and magnetic resonance data sets are commonly used to generate volumetric reconstructions. This review will focus on the practical application of magnetic resonance imaging to yield three- and four-dimensional reconstructions of pediatric cardiovascular disorders.

Heart Defects, Congenital↗
PubMed2006

Differences in brain information transmission between gifted and normal children during scientific hypothesis generation.

The purpose of the present study was to investigate differences in neural information transmission between gifted and normal children involved in scientific hypothesis generation. To investigate changes in the amount of information transmission, the children's averaged-cross mutual information (A-CMI) of EEGs was estimated during their generation of scientific hypotheses. We recorded EEG from 25 gifted and 25 age-matched normal children using 16 electrodes on each subject's scalp. To generate hypotheses, the children were asked to observe 20 "quail eggs" that gave rise to questions. After observation, they were asked to generate a scientific hypothesis--a tentative causal explanation for the questions evoked. The results of this study revealed several distinguishing brain activities between gifted and normal children during hypothesis generation. In contrast to normal children, gifted children showed increased A-CMI values between the left temporal and central, between the left temporal and parietal, and between the left central and parietal locations while generating a hypothesis. These results suggested that gifted children more efficiently distribute the cognitive resources essential to cope with hypothesis generation.

Aptitude↗