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PubMed1990

Structural classification of multi-input nonlinear systems.

We present new structural classification and parameter estimation results that are applicable to multi-input nonlinear systems. The mathematical relationships between the self- and cross-(Volterra and Wiener) kernels are derived for a basic two-input nonlinear structure. These results are then used to develop classification methods for more complicated two-input structures. Algorithms for estimating the parameters (linear and nonlinear subsystems) of these structures are also presented.

Cybernetics↗

PubMedDate not supplied

Theoretical surgery: a new specialty in operative medicine.

Theoretical surgery is defined as a nonoperative decision analysis and clinical and basic research supporting system for surgery. It developed to meet the needs of academic surgeons to coordinate communication with basic science disciplines. This article summarizes the development of this idea at the University of Marburg where theoretical surgery has reached departmental and institutional proportions. Its objectives and methods are described. Central to its operation are permanent working teams of 2 clinical surgeons, 1 basic scientist (theoretical surgeon), 1-2 technicians, and 1-2 students focusing on one problem in a joint interdisciplinary manner. Decision analysis with classification methods and the creation of decision trees and algorithms are central to the operation of this experiment. Lessons learned from this academic experiment and the accomplishments during the past 20 years are summarized on 3 levels of efficacy: performance, changing strategies, and outcome.

Decision Support Techniques↗

PubMed1991

Vector dissimilarity and clustering.

Based on the description of objects by m attributes, an m-element vector dissimilarity function is defined that, unlike scalar functions, retains the distinction among attributes. This function, which satisfies the conditions for a metric, allows the definition of betweenness, which can then be used for clustering. Applications to the subset-generation phase of conditional clustering and to nearest-neighbor-type algorithms are described.

Classification↗

PubMed1990

Non-linear statistical technique applied to data from baboon articular cartilage.

Pattern recognition software was developed and applied together with statistical techniques to articular cartilage data from the knee joint of the baboon. The standard statistical method used for comparison was ANOVA which indicates linear discrimination. In addition a Karhunen-Loève expansion was performed to reduce the dimensionality of the data and provide independent uncorrelated variables. Nearest neighbour analysis, a non-linear method, when combined with bionomial probabilities gave discrimination that was not obtained by ANOVA. Use of pattern recognition and related techniques can improve and extend the analysis of biological data to include non-linear discrimination and classification.

Algorithms↗

PubMed1992

A nationwide survey of migraine in France: prevalence and clinical features in adults. GRIM.

In November 1990 a nationwide survey of migraine was conducted in France on a representative sample of residents aged 15 years and older. The diagnosis of migraine was based on the International Headache Society (IHS) classification. In a previous study, we validated a diagnostic algorithm which classifies headache sufferers as IHS migraine, "borderline" migraine, possible migraine and non-migrainous headache. The overall prevalence of migraine patients with the IHS criteria in the present study was 8.1%; another 4% were classified as "borderline" migraine, which we in fact considered as definite migraine. Age, gender and occupation were found to be risk factors for migraine. Neither frequency and duration of attacks nor length of time of disease differed with gender. Expressed intensity of attacks, however, was greater in females.

Adolescent↗

PubMed1990

Temporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studies.

A technique for automatically clustering linear envelopes of the EMG during gait has been developed which uses a temporal feature representation and a maximum peak matching scheme. This new technique provides a viable way to define compact and meaningful EMG waveform features. The envelope matching is performed by dynamic programming, providing qualitatively the largest numbers of matched peaks and quantitatively a minimum distance measurement. The resulting averaged EMG profiles have low statistical variation and can serve as templates for EMG comparison and further classification.

Algorithms↗

PubMed1997

Digital tomosynthesis in breast imaging.

PURPOSE: To describe and evaluate a method of tomosynthesis breast imaging with a full-field digital mammographic system. MATERIALS AND METHODS: In this tomosynthesis method, low-radiation-dose images were acquired as the x-ray source was moved in an arc above the stationary breast and digital detector. A step-and-expose method of imaging was used. Breast tomosynthesis and conventional images of two imaging phantoms and four mastectomy specimens were obtained. Three experienced readers scored the relative lesion visibility, lesion margin visibility, and confidence in the classification of six lesions. RESULTS: Tomosynthesis image-reconstruction algorithms allow tomographic imaging of the entire breast from a single arc of the x-ray source and at a radiation dose comparable with that in single-view mammography. Except for images of a large mass in a fatty breast, the tomosynthesis images were superior to the conventional images. CONCLUSION: Digital mammographic systems make breast tomosynthesis possible. Tomosynthesis may improve the specificity of mammography with improved lesion margin visibility and may improve early breast cancer detection, especially in women with radiographically dense breasts.

Female↗

PubMed1996

Juvenile rheumatoid arthritis.

Musculoskeletal problems account for the majority of initial complaints attended to by primary care physicians. It is likely that a child who eventually has juvenile rheumatoid arthritis diagnosed will initially be evaluated by a family physician or a pediatrician. Primary care physicians will play an increasingly important role in management of juvenile rheumatoid arthritis, as the availability of specialists in many communities is limited, and access to them may be further limited by managed care initiatives. This article offers a brief review of the definition and classification of juvenile rheumatoid arthritis and introduces a diagnostic algorithm to provide a simplified approach toward evaluating children with arthritis. Treatment and outcomes are summarized in text and graphic formats.

Age of Onset↗

PubMed1983

The study of depressive disorders using the PSE-ID-CATEGO system.

The PSE-CATEGO-ID system and its approach to the sub-classification of depression is described. The principle of sub-classification partly corresponds to the empirical relationships between symptoms on affective disorder. Comparison of CATEGO and DSM III demonstrates that major differences of classification can arise because of differences of the classificatory algorithm even though similar lists of symptoms are used. Empirical studies of the PSE-CATEGORO-ID system are described and their importance in providing evidence for a rational choice between classificatory system is emphasised.

Anxiety Disorders↗

PubMed1995

Gunshot wounds to the elbow.

Gunshot wound injuries to the elbow are rare. This article presents the experience of the King/Drew Medical Center. Classification and management of these injuries are emphasized. An algorithm is presented.

Adult↗

PubMed2001

The use of 1H magnetic resonance spectroscopy in inflammatory bowel diseases: distinguishing ulcerative colitis from Crohn's disease.

OBJECTIVES: The distinction between the two major forms of inflammatory bowel diseases (IBD), i.e., ulcerative colitis (UC) and Crohn's disease is sometimes difficult and may lead to a diagnosis of indeterminate colitis. We have used 1H magnetic resonance spectroscopy (MRS) combined with multivariate methods of spectral data analysis to differentiate UC from Crohn's disease and to evaluate normal-appearing mucosa in IBD. METHODS: Colon mucosal biopsies (45 UC and 31 Crohn's disease) were submitted to 1H MRS, and multivariate analysis was applied to distinguish the two diseases. A second study was performed to test endoscopically and histologically normal biopsies from IBD patients. A classifier was developed by training on 101 spectra (76 inflamed IBD tissues and 25 normal control tissues). The spectra of 38 biopsies obtained from endoscopically and histologically normal areas of the colons of patients with IBD were put into the validation test set. RESULTS: The classification accuracy between UC and Crohn's disease was 98.6%, with only one case of Crohn's disease and no cases of UC misclassified. The diagnostic spectral regions identified by our algorithm included those for taurine, lysine, and lipid. In the second study, the classification accuracy between normal controls and IBD was 97.9%. Only 47.4% of the endoscopically and histologically normal IBD tissue spectra were classified as true normals; 34.2% showed "abnormal" magnetic resonance spectral profiles, and the remaining 18.4% could not be classified unambiguously. CONCLUSIONS: There is a strong potential for MRS to be used in the accurate diagnosis of indeterminate colitis; it may also be sensitive in detecting preclinical inflammatory changes in the colon.

Adult↗

PubMed1980

Further experience with computer-assisted diagnosis of diseases of the liver and biliary tree.

Computer-assisted classification of disease has largely relied upon testing the diagnostic algorithm in the same population from which it was originally derived, as a means of validation. To evaluate the accuracy of a diagnostic program in which discriminant function analysis is used, we applied it to a separate population, selected by different criteria from those used to define the original case material on which the diagnostic program was based. We selected a group of 315 patients having abnormal values for alkaline phosphatase, bilirubin, or aspartate aminotransferase for further biochemical and immunological investigations. We used a computer program involving discriminant function analysis and classification procedures primed with the results of 10 tests obtained on each of 535 patients in a previous series to allocate those 173 new patients who had diseases of the liver or biliary tree into one of 13 disease groups. The classification was less accurate than was the case in previous cross-validation studies. We developed new discriminants with the new case material, using the same group of tests, and when cross-validation was performed, overall accuracy was greatly improved. These experiences point to the powerful influence of group selection upon computer-assisted diagnostic procedures, and the hazards of applying to one clinical population discriminant functions derived from a different population.

Autoanalysis↗

PubMed1999

[The concept and use of artificial neural networks in medicine].

The use of neural networks in medicine is concentrated mainly on classification purposes. In particular, neural networks applications in spectroscopy are discussed, where this approach offers powerful algorithmic tools for interpretation of spectral data and elucidation of chemical structure of compounds. Neural networks are effective also for the classification and prediction of chemical reactivity and structure of proteins and also for QSAR and QSRR studies. At present the most successful use of neural networks in clinical medicine is image analysis and analysis of wave forms--ECG or EEG pattern recognition and classification and partly also clinical diagnosis and prognosis. (Tab. 7, Fig. 2, Ref. 170.)

Neural Networks, Computer↗

PubMed2000

Relationship between ultrasound texture classification images and histology of atherosclerotic plaque.

Structure and content of atherosclerotic plaque varies between patients and may be indicative of their risk for embolisation. This study aimed to construct parametric images of B-scan texture and assess their potential for predicting plaque morphology. Sequential transverse in vitro scans of 10 carotid plaques, excised during endarterectomy, were compared with macrohistology maps of plaque content. Multidiscriminant analysis combined the output of 157 statistical and textural algorithms into five separate texture classes, displayed as ultrasound (US) texture classification images (UTCI). Visual comparison between corresponding UTCI and histology maps found the five texture classes matched with the location of fibrin, elastin, calcium, haemorrhage or lipid. However, histology preparation removes calcium and lipid and, so, can affect the structural integrity of atherosclerotic plaques. Soft tissue regions smaller than the UTCI kernel, (0.87 mm x 0.85 mm x 3.9 mm), such as blood clots, are also difficult to detect by UTCI. These factors demonstrate limitations in the use of histology as a "gold standard" for US tissue characterisation.

Arteriosclerosis↗

PubMed2000

EEG-based discrimination between imagination of left and right hand movements using Adaptive Gaussian Representation.

This article uses the Adaptive Gaussian Representation (AGR) for human electroencephalogram (EEG) feature extraction aiming the discrimination among mental tasks to be used in a brain computer interface (BCI). It does not focus on the AGR time-frequency representation, but rather on their projection coefficients. Ten volunteers were asked to imagine either right or left hand movement, according to a proper visual stimulus. The features of the resulting EEG signals were characterised by extracting AGR coefficients. Classification was carried out using a Multilayer perceptron (MLP) trained with the classical backpropagation algorithm. Overall results show that AGR coefficients representation is able to reveal a significant EEG discrimination between imagination of right and left hand movement with a mean classification performance of 91%+/-5.8% achieved for female subjects and 87%+/-5.0% achieved for male subjects.

Adult↗

PubMed1999

Classification of Arabidopsis thaliana gene sequences: clustering of coding sequences into two groups according to codon usage improves gene prediction.

While genomic sequences are accumulating, finding the location of the genes remains a major issue that can be solved only for about a half of them by homology searches. Prediction methods are thus required, but unfortunately are not fully satisfying. Most prediction methods implicitly assume a unique model for genes. This is an oversimplification as demonstrated by the possibility to group coding sequences into several classes in Escherichia coli and other genomes. As no classification existed for Arabidopsis thaliana, we classified genes according to the statistical features of their coding sequences. A clustering algorithm using a codon usage model was developed and applied to coding sequences from A. thaliana, E. coli, and a mixture of both. By using it, Arabidopsis sequences were clustered into two classes. The CU1 and CU2 classes differed essentially by the choice of pyrimidine bases at the codon silent sites: CU2 genes often use C whereas CU1 genes prefer T. This classification discriminated the Arabidopsis genes according to their expressiveness, highly expressed genes being clustered in CU2 and genes expected to have a lower expression, such as the regulatory genes, in CU1. The algorithm separated the sequences of the Escherichia-Arabidopsis mixed data set into five classes according to the species, except for one class. This mixed class contained 89 % Arabidopsis genes from CU1 and 11 % E. coli genes, mostly horizontally transferred. Interestingly, most genes encoding organelle-targeted proteins, except the photosynthetic and photoassimilatory ones, were clustered in CU1. By tailoring the GeneMark CDS prediction algorithm to the observed coding sequence classes, its quality of prediction was greatly improved. Similar improvement can be expected with other prediction systems.

Algorithms↗

PubMed1998

The rational clinical examination. Is this patient having a myocardial infarction?

When faced with a patient with acute chest pain, clinicians must distinguish myocardial infarction (MI) from all other causes of acute chest pain. If MI is suspected, current therapeutic practice includes deciding whether to administer thrombolysis or primary percutaneous transluminal coronary angioplasty and whether to admit patients to a coronary care unit. The former decision is based on electrocardiographic (ECG) changes, including ST-segment elevation or left bundle-branch block, the latter on the likelihood of the patient's having unstable high-risk ischemia or MI without ECG changes. Despite advances in investigative modalities, a focused history and physical examination followed by an ECG remain the key tools for the diagnosis of MI. The most powerful features that increase the probability of MI, and their associated likelihood ratios (LRs), are new ST-segment elevation (LR range, 5.7-53.9); new Q wave (LR range, 5.3-24.8); chest pain radiating to both the left and right arm simultaneously (LR, 7.1); presence of a third heart sound (LR, 3.2); and hypotension (LR, 3.1). The most powerful features that decrease the probability of MI are a normal ECG result (LR range, 0.1-0.3), pleuritic chest pain (LR, 0.2), chest pain reproduced by palpation (LR range, 0.2-0.4), sharp or stabbing chest pain (LR, 0.3), and positional chest pain (LR, 0.3). Computer-derived algorithms that depend on clinical examination and ECG findings might improve the classification of patients according to the probability that an MI is causing their chest pain.

Acute Disease↗

PubMed1994

An algorithmic approach to aspergillus sinusitis.

The effective management of paranasal sinus aspergillosis requires early diagnosis, histological classification, surgery and where appropriate, chemotherapy. Fungal sinusitis may be easily missed unless a high index of suspicion is maintained and specific culture and histology requested. The disease is classified into invasive and noninvasive types, each being divided into two subgroups: invasive aspergillosis may be either fulminant or indolent and noninvasive disease localized or allergic. The literature is reviewed and an algorithmic approach to aspergillus sinusitis proposed. The importance of histologically differentiating invasive from noninvasive aspergillosis prior to selecting the appropriate treatment options is stressed. CT scan should precede definitive surgery, and be used in follow-up. Close and prolonged follow-up is essential.

Algorithms↗