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PubMed2006

A new classification system and an algorithm for the reconstruction of nasal defects.

A new, comprehensive system for scoring and classification of nasal defects is proposed in this article. The soft tissue coverage of the nose is in continuity with the cheeks, glabella and upper lip and the osteocartilaginous infrastructure is in continuity with the two nasofrontal buttresses, the frontal bar and the palate. Soft tissues and the skeletal framework are divided into sub-units and these anatomic features are schematized on a logo. The sub-units are graded on the logo, depending on their gravity in reconstructive strategies. Any given nasal defect is described by shading the involved sub-units on the logo and the sum of the points appended each sub-unit gives the total score of defect. The severity of the tissue loss is assessed according to a "Classification System" which is derived from this scoring system. Nasal defects are classified into one of four main Types corresponding to their scores. One hundred twenty seven patients who were operated on for various nasal pathologies have been reviewed and nasal defects are scored and classified according to the proposed system. Application of this system to the spectrum of cases encountered in a 6 years period shows that it is based on anatomic grounds, easy to document and efficient transmission of objective information becomes possible. It also offers a useful algorithm to approach the reconstruction of nasal defects.

Adult↗
PubMed2000

Using UMLS semantics for classification purposes.

The Unified Medical Language System (UMLS) contains semantic information about terms from various sources; each concept can be understood and located by its relationships to other concepts. We describe a method in which the semantic relationships between UMLS concepts are exploited for the purpose of classification. This method combines three existing components: 1) Mapping terms to UMLS concepts; 2) Restricting UMLS concepts to MeSH; and 3) Mapping MeSH terms to disease categories. When applied to the automatic classification of condition terms into broad disease categories in the Clinical Trials database, this method assigned relevant categories to 92% of the 1823 condition terms encountered. 135 (7%) failed to be classified and 14 (.77%) were misclassified. The limits of this method are discussed, as well as the reuse of existing components, and the tuning required to achieve automatic classification.

Algorithms↗
PubMed2006

Vaginal reconstruction: an algorithm approach to defect classification and flap reconstruction.

Vaginal defects from oncologic resection present a complex array of reconstructive challenges. Increased use of adjuvant radiation and chemotherapy demands uncomplicated wound healing. As patients are being diagnosed at earlier stages of disease and at younger ages, maintenance of sexual function and body image are fundamental goals. This review provides an algorithm approach to defect classification and flap reconstruction. Carefully appreciation of the specific defect facilitates flap choice. There are two basic defect types partial (Type I) and circumferential defects (Type II) 1. These defect types can be further subclassified. Type IA defects are partial and involve the anterior and/or lateral wall. Type IB defects are also partial, but involve the posterior vaginal wall. Type IIA defects are circumferential, involving the upper two-thirds of the vagina. Type IIB defects represent circumferential, total vaginal resection, most commonly following pelvic exenteration. Using this method of defect classification, three pedicled flaps can be used to successfully reconstruct the majority of defects: the Singapore (or pudendal thigh) flap, the rectus flap, and the gracilis flap. With appropriate flap choice and a multidisciplinary approach to patient care, rapid wound healing, restoration of the pelvic floor, and re-establishment of sexual function may be most reliably achieved.

Algorithms↗
PubMed2005

Online clustering algorithms for radar emitter classification.

Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: One is model-based using the Minimum Description Length (MDL) criterion and the other is based on competitive learning. Computational complexity is analyzed for each algorithm and then compared. Simulation results show the superior performance of the model-based algorithm over competitive learning in terms of better classification accuracy, flexibility, and stability.

Algorithms↗
PubMed2002

Multivariate analysis of the ecoregion delineation for aquatic systems.

The ecoregion concept is a popular method of understanding the spatial distribution of the environment', however, it has yet to be adequately demonstrated that the environment is distributed in accordance with these bounded units. In this paper, we generated a testable hypothesis based on the current usage of ecoregions: the ecoregion classification will allow for discrimination between lakes of different water quality. The ecoregion classification should also be more effective better than a comparably scaled classification based on political boundaries, land-use class, or random grouping. To test this hypothesis we used the Environmental Monitoring and Assessment Program (EMAP) lake water chemistry data from the northeast United States. The water chemistry data were reduced to four components using principal component analysis. For comparison to an optimal grouping of these data we used K-means cluster analysis to define the extent at which these lakes could be segregated into distinct classes. Jackknifed discriminant analysis was used to determine the classification rate of ecoregions, the three alternative spatial classification methods, and the clustering algorithm. The classification based on ecoregions was successful for 35% of the lakes included in this study, in comparison to the clustered groups accuracy of 98%. These results suggest that the large scale spatial distribution of ecosystem types is more complicated than that suggested by the present ecoregion boundaries. Further tests of ecoregion delineations are needed and alternative large-scale management strategies should be investigated.

Algorithms↗
PubMed2005

[The application of BP neural network improved with LM algorithm in surface EMG signal classification].

The method of BP neural network improved by Levenberg-Marquardt algorithm in surface EMG signal classification is proposed. The data reduction and preprocessing operations of the signals are performed by means of the wavelet transform. The classifier can identify four classes of forearm movement: hand extension, hand grasp,forearm pronation and forearm supination with a high accuracy.Experimental result shows that the BP neural netwok improved by LM algorithm has greatly increased the speed and the accuracy of signal classification in practical application of prothesis control.

Algorithms↗
PubMed2002

A classification system and reconstructive algorithm for acquired vaginal defects.

Although multiple flaps have been used for vaginal reconstruction, a logical approach to reconstruction of these often complex defects has not been described. The objective of this study was to establish a classification system for acquired vaginal defects and to develop a reconstructive algorithm derived from this system. This study is a retrospective review of a 7-year experience with 51 flaps in 37 consecutive vaginal reconstructions. Twenty-two partial defects and 15 circumferential defects were reconstructed in 35 patients. Average patient age was 48 years (range, 19 to 69 years). Of the 22 patients with partial vaginal defects, six involved primarily the anterior and lateral wall and 16 the posterior vaginal wall. Among the 15 patients with circumferential defects, four included only the upper two-thirds of the vagina and 11 encompassed the entire vagina. On the basis of these defects, a classification system was developed. Partial defects involving the anterior or lateral vaginal wall were classified as type IA defects and were reconstructed primarily with pedicled Singapore fasciocutaneous flaps. Partial defects involving the posterior wall were classified as type IB and were reconstructed with pedicled rectus abdominis myocutaneous flaps. Circumferential defects involving the upper two-thirds of the vagina were classified as type IIA defects and were reconstructed with a rolled rectus flap or, less commonly, sigmoid colon (one patient). Total circumferential defects, type IIB, were reconstructed largely with bilateral gracilis flaps. Six patients had major complications, including one perioperative death, one complete flap loss, one partial flap loss, and three pelvic abscesses. Three patients had minor complications that included delayed wound healing and donor-site infection. Vaginal defects can be categorized into one of four types on the basis of the location and extent of resection. Flap selection is determined on the basis of the type of defect. Using this algorithm, immediate vaginal reconstruction with pedicled regional flaps can be performed with minimal patient morbidity and few surgical complications.

Adenocarcinoma↗
PubMed2006

Adaptive polarimetric sensing for optimum radar signature classification using a genetic search algorithm.

An automated technique for adaptive radar polarimetric pattern classification is described. The approach is based on a genetic algorithm that uses a probabilistic pattern separation distance function and searches for those transmit and receive states of polarization sensing angles that optimize this function. Seven pattern separation distance functions--the Rayleigh quotient, the Bhattacharyya, divergence, Kolmogorov, Matusta, Kullback-Leibler distances, and the Bayesian probability of error--are used on real, fully polarimetric synthetic aperture radar target signatures. Each of these signatures is represented as functions of transmit and receive polarization ellipticity angles and the angle of polarization ellipse. The results indicate that, based on the majority of the distance functions used, there is a unique set of state of polarization angles whose use will lead to improved classification performance.

Journal Article↗
PubMed2006

Enhancing instance-based classification with local density: a new algorithm for classifying unbalanced biomedical data.

MOTIVATION: Classification is an important data mining task in biomedicine. In particular, classification on biomedical data often claims the separation of pathological and healthy samples with highest discriminatory performance for diagnostic issues. Even more important than the overall accuracy is the balance of a classifier, particularly if datasets of unbalanced class size are examined. RESULTS: We present a novel instance-based classification technique which takes both information of different local density of data objects and local cluster structures into account. Our method, which adopts the basic ideas of density-based outlier detection, determines the local point density in the neighborhood of an object to be classified and of all clusters in the corresponding region. A data object is assigned to that class where it fits best into the local cluster structure. The experimental evaluation on biomedical data demonstrates that our approach outperforms most popular classification methods. AVAILABILITY: The algorithm LCF is available for testing under http://biomed.umit.at/upload/lcfx.zip.

Algorithms↗
PubMed2005

An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems.

There are numerous combinations of neural networks (NNs) and evolutionary algorithms (EAs) used in classification problems. EAs have been used to train the networks, design their architecture, and select feature subsets. However, most of these combinations have been tested on only a few data sets and many comparisons are done inappropriately measuring the performance on training data or without using proper statistical tests to support the conclusions. This paper presents an empirical evaluation of eight combinations of EAs and NNs on 15 public-domain and artificial data sets. Our objective is to identify the methods that consistently produce accurate classifiers that generalize well. In most cases, the combinations of EAs and NNs perform equally well on the data sets we tried and were not more accurate than hand-designed neural networks trained with simple backpropagation.

Algorithms↗
PubMed1998

On the use of neural network techniques to analyze sleep EEG data. Third communication: robustification of the classificator by applying an algorithm obtained from 9 different networks.

This is the third communication on the use of neural network techniques to classify sleep stages. In our first communication we presented the algorithms and the selection of the feature space and its reduction by using evolutionary and genetic procedures. In our second communication we trained the evolutionary optimized networks on the basis of multiple subject data in context with some smoothing algorithms in analogy of Rechtschaffen and Kales (RK). In this third communication we could demonstrate that the robustness concerning individual specific features of automatically generated sleep profiles could be reasonably improved by an additional modification of the procedure used by SASCIA (Sleep Analysis System to Challenge Innovative Artificial Networks). The outputs of nine different networks that were created by the data of 9 different subjects were used simultaneously for classification. The medians of the values obtained in each output measure were selected for the allocation to a sleep stage. The fitness criteria of 16 automatically generated sleep profiles showed reasonable concordance with the expert profile. Even though in single cases the concordance between conventional RK classifications and automatically generated profiles were a few percentages lower, the average correct classification of the 12 classified subjects improved substantially, thus proving that the classifier is more robust against individuum-specific variability. Despite the fact that the expert generally employs three channels (EEG, EMG and EOG), at least to build up sleep profiles, the SASCIA system was able to produce profiles on the basis of only one EEG channel with 80% concordance and a correlation coefficient of 0.86. The feature selections were performed by genetic algorithms and the topologies of the networks were optimized by evolutionary algorithms. This algorithm will now be used for larger sample forward classification.

Algorithms↗
PubMed2026

Machine learning-based drug susceptibility prediction from Candida genomic data.

OBJECTIVES: Invasive Candida infection is an increasing clinical concern, with antifungal resistance rising across multiple species. However, rapid and accurate antifungal susceptibility testing (AFST) remains limited in routine practice. The study evaluated species distribution and antifungal susceptibility of invasive Candida isolates in China and assessed the feasibility of combining whole-genome sequencing (WGS) with machine learning to predict minimum inhibitory concentrations (MICs). METHODS: Consecutive non-repetitive isolates were collected from 20 hospitals in 13 provinces during 2022-2023. MICs of nine antifungal agents were determined by broth microdilution, and WGS was performed for species accounting for >5% of the total isolates. Genomic 11-mer features were extracted and used to train random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models, followed by optimization of the best-performing algorithm. RESULTS: A total of 337 isolates were obtained from blood (n = 232) and sterile body fluids (n = 105), comprising C. albicans (n = 103), C. tropicalis (n = 71), C. parapsilosis (n = 67), and C. glabrata (n = 63). Non-albicans Candida showed higher azole and echinocandin resistance, with C. tropicalis notably resistant to azoles and C. glabrata to echinocandins. Among the three models, RF demonstrated the best performance on 304 sequenced isolates. The optimized RF model was evaluated by the receiver operating characteristic (ROC) curve analysis and achieved an average area under the ROC curve (AUC) of 0.979 (95% CI: 0.974-0.984), essential agreement over 90.1%, and categorical agreement over 93.2% across species. CONCLUSIONS: These findings underscore the clinical challenge posed by non-albicans Candida resistance, and indicate that WGS-based MIC prediction may offer a highly accurate reference for earlier antifungal therapy.

Antifungal Agents↗
PubMed2005

Image change detection algorithms: a systematic survey.

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

Algorithms↗
PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗
PubMed2002

Auditing the UMLS for redundant classifications.

The UMLS's Semantic Network (SN) serves as a valuable abstraction for the underlying concept repository called the Metathesaurus (META). Specifically, the SN forms a classification layer for the META, with each of the META's constituent concepts assigned to one or more semantic types in the SN. The rule in the design of the SN is to have concepts explicitly assigned to the lowest possible semantic types in the SN's IS-A hierarchy. Implicit assignment to higher semantic types can be inferred via the IS-A relationships. However, in subsequent versions of the UMLS, unnecessary, simultaneous assignments to descendant and ancestor semantic types have been discovered (e.g., 8,622 in the UMLS 1998 version and 12,657 in the 2001 version). The assignment of concepts to such ancestor semantic types is called redundant classification. There is a need for an automated auditing tool that can identify all these redundant classifications. In this paper, an efficient algorithm for this auditing task is introduced. Details of its application to the current (2001) version of the UMLS are presented and the results are discussed.

Algorithms↗
PubMed1996

A robust sequential detection algorithm for cardiac arrhythmia classification.

In [1] qnd [2] Thakor et al. describe a sequential probability ratio test (SPRT) based on threshold crossing intervals (TCI) for the discrimination of ventricular fibrillation (VF) from ventricular tachycardia (VT). However, in applying their algorithm to data from the MIT-BIH malignant arrhythmia database, we observed some overlap in the distributions of TCI for VF and VT resulting in 16% overall error rate for the discrimination. In this communication, we describe a modified SPRT algorithm, using a new feature dubbed blanking variability (BV) as the basis for discrimination. Using the MIT-BIH database, the preliminary results showed that the proposed method decreases the overall error rate to 5%.

Algorithms↗
PubMed2005

Application of sliding-window discretization and minimization of stochastic complexity for the analysis of fAFLP genotyping fingerprint patterns of Vibrionaceae.

Minimization of stochastic complexity (SC) was used as a method for classification of genotypic fingerprints. The method was applied to fluorescent amplified fragment length polymorphism (fAFLP) fingerprint patterns of 507 Vibrionaceae representatives. As the current BinClass implementation of the optimization algorithm for classification only works on binary vectors, the original fingerprints were discretized in a preliminary step using the sliding-window band-matching method, in order to maximally preserve the information content of the original band patterns. The novel classification generated using the BinClass software package was subjected to an in-depth comparison with a hierarchical classification of the same dataset, in order to acknowledge the applicability of the new classification method as a more objective algorithm for the classification of genotyping fingerprint patterns. Recent DNA-DNA hybridization and 16S rRNA gene sequence experiments proved that the classification based on SC-minimization forms separate clusters that contain the fAFLP patterns for all representatives of the species Enterovibrio norvegicus, Vibrio fortis, Vibrio diazotrophicus or Vibrio campbellii, while previous hierarchical cluster analysis had suggested more heterogeneity within the fAFLP patterns by splitting the representatives of the above-mentioned species into multiple distant clusters. As a result, the new classification methodology has highlighted some previously unseen relationships within the biodiversity of the family Vibrionaceae.

Algorithms↗
PubMed1994

Comparison of AR-based algorithms for respiratory sounds classification.

Respiratory sounds of pathological and healthy subjects were analyzed via autoregressive (AR) models with a view to construct a diagnostic aid based on auscultation. Using the AR vectors, two reference libraries, pathological and healthy, were built. Two classifiers, k-nearest neighbour (k-NN) classifier and a quadratic classifier, were designed and compared. Performances of the classifiers were tested for different model orders. The best classification results were obtained for model order 6.

Algorithms↗