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PubMed2001

A neural network approach in diabetes management by insulin administration.

Diabetes management by insulin administration is based on medical experts' experience, intuition, and expertise. As there is very little information in medical literature concerning practical aspects of this issue, medical experts adopt their own rules for insulin regimen specification and dose adjustment. This paper investigates the application of a neural network approach for the development of a prototype system for knowledge classification in this domain. The system will further facilitate decision making for diabetic patient management by insulin administration. In particular, a generating algorithm for learning arbitrary classification is employed. The factors participating in the decision making were among other diabetes type, patient age, current treatment, glucose profile, physical activity, food intake, and desirable blood glucose control. The resulting system was trained with 100 cases and tested on 100 patient cases. The system proved to be applicable to this particular problem, classifying correctly 92% of the testing cases.

Adult↗

PubMed2005

Periprosthetic fractures around cementless hydroxyapatite-coated femoral stems.

We studied 14 periprosthetic femoral fractures out of a series of 619 hydroxyapatite coated hip implants and compared the outcome to published treatment algorithms using the Vancouver classification. There were five type A fractures, six B1, two B2, and one type B3 fracture. All but one type A fractures were treated conservatively. Compared with the Vancouver classification, we observed a different fracture type in the type B fractures. No fractures at the tip of the stem were seen, as in cemented implants. Three B1 fractures were treated operatively due to fracture displacement, and three were treated conservatively. The B2 and B3 fractures were managed with long, uncemented, revision stems because of a disrupted bone-prosthesis interface. All fractures healed well. This study confirms that the modified algorithm of management of periprosthetic fractures, using the Vancouver classification, is a simple, reproducible, classification system for uncemented prostheses. Conservative treatment is a valid option if the implant is stable whilst surgical intervention is mandatory if the implant is loose.

Aged↗

PubMed2026

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the data set is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently used as pseudolabels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methods, such as hierarchical clustering and density-based spatial clustering of applications with noise (DBSCAN), are used, with results visualized through principal component analysis-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underlines the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable method for analyzing complex genomic data sets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.

Humans↗

PubMed2003

Directional correlation characterization and classification of white matter tracts.

To study the architectural characteristics of white matter (WM) tracts, the directional correlation (DC), defined as the inner product of the major eigenvector of adjacent pixels, was used as a quantitative index to investigate directional similarity in WM tracts. A region-growing algorithm was employed to propagate an area from a seed point as a function of the DC threshold (DCt) to critically evaluate the directional properties of WM tracts. As the DCt was increased, more pixels were excluded from the propagated region as their DC fell below the DCt, and neighboring WM tracts could be distinguished as the area decreased. Taking the DC into account, a systematic classification routine for WM tracts was devised and tested on a mouse brain in vivo. The results show that individual WM tracts possess a high degree of directional similarity, and, by careful choice of the DCt value, the proposed classification algorithm can recognize all possible WM tracts in a given data set.

Animals↗

PubMed1997

Wavelet image extension for analysis and classification of infarcted myocardial tissue.

Some computer applications for tissue characterization in medicine and biology, such as analysis of the myocardium or cancer recognition, operate with tissue samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture operators can be employed: the operators should be insensitive to noise and image distortion and yet be reliable in order to estimate texture quality from the small number of image points available. In order to describe the quality of infarcted myocardial tissue, we propose a new wavelet-based approach for analysis and classification of texture samples with small dimensions. The main idea of this method is to decompose the given image with a filter bank derived from an orthonormal wavelet basis and to form an image approximation with higher resolution. Texture energy measures calculated at each output of the filter bank as well as energies of synthesized images are used as texture features in a classification procedure. We propose an unsupervised classification technique based on a modified statistical t-test. The method is tested with clinical data, and the classification results obtained are very promising. The performance of the new method is compared with the performance of several other transform-based methods. The new algorithm has advantages in classification of small and noisy input samples, and it represents a step toward structural analysis of weak textures.

Algorithms↗

PubMed2000

A simple method for classifying genes and a bootstrap test for classifications.

A new simple method for classifying genes is proposed based on Klastorin's method. This method classifies genes into monophyletic groups which are made distinct from each other by evolutionary changes. The method is applicable as long as the phylogenetic tree of genes is obtained. There is a fast algorithm for obtaining the classification. A bootstrap test of a classification is also presented. As an example, we classified opsin genes. The classification obtained by this method is the same as the previous classification based on the function of opsins.

Algorithms↗

PubMed1999

Classification of periprosthetic fractures complicating total knee arthroplasty.

As the total number of knee arthroplasties increase, the frequency with which periprosthetic fractures will be encountered can be expected to increase as well. The classification of these fractures is an important aspect in the development of an understanding of these problems. Adequate classification systems allow accurate communication between researchers and comparisons to be made between different techniques. Classification systems also allow algorithms to be developed to guide clinicians in the diagnosis, investigation, and treatment of these fractures. The authors present an outline of the classification systems described for fractures occurring in relation to total knee arthroplasty.

Arthroplasty, Replacement, Hip↗

PubMed1992

The effect of noise and biases on the performance of machine learning algorithms.

This paper describes the results of experiments with a machine learning algorithm for the induction of classification trees. We mainly address the impact of noise on the resulting classification tree and on the classification results obtained with the derived tree. We use the domain of the biochemical assessment of thyroid diseases as an example. Some suggestions for quality assessment are outlined that should be available in tools that assist users in deriving classification trees in noisy domains.

Algorithms↗

PubMed1994

Improving prediction of preterm birth using a new classification scheme and rule induction.

Prediction of preterm birth is a poorly understood domain. The existing manual methods of assessment of preterm birth are 17%-38% accurate. The machine learning system LERS was used for three different datasets about pregnant women. Rules induced by LERS were used in conjunction with a classification scheme of LERS, based on "bucket brigade algorithm" of genetic algorithms and enhanced by partial matching. The resulting prediction of preterm birth in new, unseen cases is much more accurate (68%-90%).

Algorithms↗

PubMed2024

A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery.

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

Humans↗

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↗

PubMed1983

A texture analysis model for the classification of video images of B and T cells and the formation of subcategories.

B and T lymphocytes of known origin were used to test a pretopologic (mathematical) classification model that uses chromatin texture as a parameter. One data base was obtained by digitizing the cells with a standard TV camera coupled with a microscope, with the images transmitted to an image-analysis system (ASTI) coupled with a multi-20 computer. A second data base was obtained from the same slides using the TAS coupled with a PDP 11/34. The data bases were analyzed by the pretopologic program, which paramatizes the dispersion and aggregation of 15 possible optical densities (O.D.) and their relation to each other for each cell. Sub-categories were formed according to an algorithm that forms categories of images having the same pretopologic predominating sequences of O.D. values. In the ASTI system, 92% of the B-cell and 94% of the T-cell training samples were classified unambiguously; the test sample was classified as 60% B cells and 40% T cells, which is in close agreement with the spleen distribution as noted in the immunologic literature. The classification algorithm found 13 B-cell subcategories and 12 T-cell subcategories in the respective training sample; classification of the spleen test sample added no new subcategories. The TAS results were in agreement with the ASTI results within the limits of a restricted sample size. The overall B-cell and T-cell classification results were in agreement with those obtained by other authors . The subcategories found are unlikely to be fortuitous since only certain combinations of predominating O.D. values occurred and a large number of digitized nuclei had identical parameters. Nevertheless, the biologic significance of the subcategories cannot be assessed by mathematical methods alone, and these methods must be tested with appropriate biologic models.

Animals↗

PubMed2004

Improving the prediction of final infarct size in acute stroke with bolus delay-corrected perfusion MRI measures.

PURPOSE: To investigate whether bolus delay-corrected dynamic susceptibility contrast (DSC) perfusion MRI measures allowed a more accurate estimation of eventual infarct volume in 14 acute stroke patients using a predictive tissue classifier algorithm. MATERIALS AND METHODS: Tissue classification was performed using a expectation maximization and k-means clustering algorithm utilizing diffusion and T2 measures (diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and T2) combined with uncorrected perfusion measures cerebral blood flow ((CBF) and mean transit time [MTT]), bolus delay-corrected perfusion measures (cCBF and cMTT), and bolus delay-corrected perfusion indices (cCBF and cMTT with bolus delay). RESULTS: The mean similarity index (SI), a kappa-based correlation statistic reflecting the pixel-by-pixel classification agreement between predicted and 30-day T2 lesion volumes, were 0.55 +/- 0.19, 0.61 +/- 0.15 (P < 0.02) and 0.60 +/- 0.17 (P <0.03), respectively. Spearman's correlation coefficients, comparing predicted and final lesion volumes were 0.56 (P < 0.05), 0.70 (P < 0.01), and 0.84 (P < 0.001), respectively. We found a more significant correlation between predicted infarct volumes derived from bolus delay-corrected perfusion measures than from conventional perfusion measures when combined with diffusion measures and compared with final lesion volumes measured on 30-day T2 MRI scans. CONCLUSION: Bolus delay-corrected perfusion measures enable an improved prediction of infarct evolution and evaluation of the hemodynamic status of neuronal tissue in acute stroke.

Acute Disease↗

PubMed1999

Classification of gastritis--yesterday, today and tomorrow.

The major landmark in the recent history of gastritis was the discovery of Helicobacter pylori as the cause for approximately 90% of cases of chronic gastritis. This was followed by an attempt to rationalise classification from the many conflicting nomenclatures in existence to one, the Sydney System, that might gain more universal acceptance and allow studies from around the world to be compared. In the decade since its inception, including an appropriate update, the system has partially achieved this aim. It is based on documenting the topography of the gastritis, this reflecting the range of possible clinical outcomes and point of progress along the gastritis-metaplasia-dysplasia-carcinoma cascade. The rapidly expanding molecular knowledge about host mucosal immunology, determinants of bacterial virulence and dietary constituents makes it likely "tomorrow's" classification will be an algorithm to include several such factors. From this an exact "peptic" patient profile could be constructed. However one must also speculate that interest in gastritis and its classification could whither just as quickly as it blossomed with the advent of a successful vaccine.

Gastritis↗

PubMed1981

Selection of optimal features for classification of electrocardiograms.

The forward sequential selection and backward sequential rejection algorithms and the optimal branch and bound algorithm were evaluated in selection of features for classification of electrocardiograms of 237 patients with old myocardial infarction and 299 subjects without infarction. The branch and bound algorithm proved suitable for small sets of ECG features. However, the computational effort required was orders of magnitude greater than that for the other two methods and became prohibitive with large features sets. A satisfactory and consistent overall classification accuracy was achieved by using the sequential selection algorithms for selecting continuous features by maximizing the Mahalanobis distance at each step of the feature selection process. Maximization of the association index can produce better results but requires more computing effort. Feature selection based on maximizing sensitivity at each step for a fixed level of specificity is less satisfactory when a high level of specificity is required.

Computers↗

PubMed2000

Uterine EMG analysis: a dynamic approach for change detection and classification.

Toward the goal of detecting preterm birth by characterizing events in the uterine electromyogram (EMG), we propose a method of detection and classification of events in this signal. Uterine EMG is considered as a nonstationary signal and our approach consists of assuming piecewise stationarity and using a dynamic change detector with no a priori knowledge of the parameters of the hypotheses on the process state to be detected. The detection approach is based on the dynamic cumulative sum (DCS) of the local generalized likelihood ratios associated with a multiscale decomposition using wavelet transform. This combination of DCS and multiscale decomposition was shown to be very efficient for detection of both frequency and energy changes. An unsupervised classification based on the comparison between variance-covariance matrices computed from selected scales of the decomposition was implemented after detection. Finally a class labeling based on neural networks was developed. This algorithm of detection-classification-labeling gives satisfactory results on uterine EMG: in most cases more than 80% of the events are correctly detected and classified whatever the term of gestation.

Algorithms↗

PubMedDate not supplied

Segmentation and visualization of brain lesions in multispectral magnetic resonance images.

In this study we focus on the problem of segmentation and visualization of soft tissue structures in three-dimensional (3D) magnetic resonance (MR) imaging. We introduce a classification method which is a combination of a recently proposed contour detection algorithm and Haslett's contextual classification method extended to 3D. This classification method is used in the classification step of a rendering model suggested by Drebin et al. for visualizing normal and pathological tissue structures in the brain. We evaluate the combination of these two methodologies, and identify some problems which have to be solved in order to develop a clinical useful tool.

Algorithms↗

PubMed1997

On the use of neural network techniques to analyse sleep EEG data. First communication: application of evolutionary and genetic algorithms to reduce the feature space and to develop classification rules.

To automate sleep stage scoring, the system sleep analysis system to challenge innovative artificial networks (SASCIA) has been developed and implemented. The aims of our investigation were twofold: In addition to automatic sleep stage scoring the hypothesis was tested that the information of only 1 EEG channel (C4-A2) should be sufficient to automatically generate sleep profiles which are comparable with profiles made by sleep experts on the basis of at least 3-channel EEG (C4-A2), EOG and EMG, as EOG and EMG are seen as epiphenomena during sleep and the full information about the sleep stage should--according to our hypothesis--be available in the EEG. The main components of the SASCIA sleep analysis system are designed to meet the requirements of flexible adaptation to the interindividual differences of the sleep EEG. The core of the SASCIA sleep analysis system consists of neural networks. Supervised learning was implemented and the experts' scorings were included into the learning set and test set. The feature selections out of a large number (118) are performed by genetic algorithms and the topologies of the networks are optimized by evolutionary algorithms. Different mathematical procedures were used to evaluate and optimize the efficiency of the system. The profiles generated by SASCIA are in reasonable agreement with the sleep stages scored by experts according to RKR. The development of the system is communicated in three parts: the first communication deals with the application of the neural network techniques using evolutionary and genetic algorithms and with the selection of feature space. The second communication shows the training of these evolutionary optimized network techniques with multiple subjects and the application of context rules, while the third communication shows an improvement in the robustness by the simultaneous application of 9 different networks obtained from 9 subject types which were used in combination with context rules.

Algorithms↗