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At least 649 records · Page 36Linked to original sources
PubMed2003

Inherited thrombocytopenias: a proposed diagnostic algorithm from the Italian Gruppo di Studio delle Piastrine.

BACKGROUND: Although no epidemiological study has so far been performed, inherited thrombocytopenias are considered to be very rare based on the number of case reports in the literature. However, diagnosis of these disorders is often difficult and requires competences that are limited to specialized centers. We, therefore, suspect that inherited thrombocytopenias are underreported because their diagnosis is often missed. OBJECTIVES: The aim of this study was to develop a diagnostic algorithm that utilizes the simplest possible diagnostic tests and that can also be used in centers that are not highly specialized. METHODS: The basic features of the diagnostic algorithm (definition and classification of hereditary thrombocytopenias, laboratory tests, sequence of investigations) were discussed by the members of the Italian Gruppo di Studio delle Piastrine during two meetings held in October 2000 and 2001. The sources of information were literature and personal experience. The final proposed diagnostic algorithm was produced by the authors of this paper and approved during a third meeting in May 2002. PERSPECTIVES: A definite diagnosis for patients with hereditary thrombocytopenias serves many purposes. It is essential to define the prognosis of the patients and to identify the best therapeutic approach. It also defines the risk of transmitting the disorder to progeny and, in many cases, allows prenatal diagnosis. Finally, several pieces of evidence indicate that not all the genetic thrombocytopenias have yet been identified. Exclusion of known disorders by accurate investigation is, therefore, the starting point for the discovery of these new illnesses.

Algorithms↗
PubMed1992

Relative risk trees for censored survival data.

A method is developed for obtaining tree-structured relative risk estimates for censored survival data. The first step of a full likelihood estimation procedure is used in a recursive partitioning algorithm that adopts most aspects of the widely used Classification and Regression Tree (CART) algorithm of Breiman et al. (1984, Classification and Regression Trees, Belmont, California: Wadsworth). The performance of the technique is investigated through stimulation and compared to the tree-structured survival methods proposed by Davis and Anderson (1989, Statistics in Medicine 8, 947-961) and Therneau, Grambsch, and Fleming (1990, Biometrika 77, 147-160).

Age Factors↗
PubMed2006

Towards automated classification of intensive care nursing narratives.

Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content. In this paper, we addressed two issues related to designing an automated classifier: domain experts' agreement on the content of the classes into which the data are to be classified, and the ability of the machine-learning algorithm to perform the classification on an acceptable level. The data we used were a set of Finnish intensive care nursing narratives. By using Cohen's kappa, we assessed the agreement of three nurses on the content of the classes Breathing, Blood Circulation and Pain, and by using the area under ROC curve (AUC), we measured the ability of the Least Squares Support Vector Machine (LS-SVM) algorithm to learn the classification patterns of the nurses. On average, the values of kappa were around 0.8. The agreement was highest in the class Blood Circulation, and lowest in the class Breathing. The LS-SVM algorithm was able to learn the classification patterns of the three nurses on an acceptable level; the values of AUC were generally around 0.85. Our results indicate that one way to develop electronic patient records could be tools that handle the free text in nursing documentation.

Algorithms↗
PubMed2006

Optimizing the classification of acute lymphoblastic leukemia and acute myeloid leukemia samples using artificial neural networks.

Accurate classification of human blood cells plays a decisive role in the diagnosis and treatment of diseases. Artificial Neural Networks (ANN) have been consistently used as a trusted classification tool for this type of analysis. In this study, a new Artificial Neural Network approach is proposed for the multidimensional classification of two of the most common forms of leukemia: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML), also sometimes called Acute Myelogenous Leukemia. Beckman-Coulter Corporation supplied flow cytometry data of 120 patients that were used in the training and testing phases. The ANN algorithm was thus developed to exploit the different features of the different blood cells provided in an optimized fashion. The goal was to establish a programming tool, supported through this new ANN development, for the identification of normal and abnormal blood samples and provide information to medical doctors in the form of diagnostic references for the specific disease state that is considered for this study. The application of the ANN algorithm produced remarkable classification accuracy results that show a 95% classification accuracy for the normal blood samples and 90% classification accuracy for the abnormal samples even under the ubiquitous problem of overlap.

Blood Cell Count↗
PubMed2006

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences.

BACKGROUND: The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. RESULTS: There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. CONCLUSION: The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

Algorithms↗
PubMed2006

Predicting survival within the lung cancer histopathological hierarchy using a multi-scale genomic model of development.

BACKGROUND: The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis-spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance. METHODS AND FINDINGS: Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan-Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis. CONCLUSIONS: From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.

Adenocarcinoma↗
PubMed2004

Risk classification of Medicare HMO enrollee cost levels using a decision-tree approach.

OBJECTIVE: To determine whether classification tree techniques used on survey data collected at enrollment from older adults in a Medicare HMO could predict the likelihood of an individual being in a low, medium, high, or very high cost group in the subsequent year. METHODS: Data from comprehensive health risk assessment (HRA) screening of 11 744 new enrollees (age > or = 65 years) continuously enrolled in a Medicare HMO for at least 24 months between 1997 and 2002 were combined with complete healthcare service utilization data for each enrollee in the postenrollment period to create cost groups. An original clinically devised algorithm and a Classification and Regression Tree (CART) that used the HRA data were compared with respect to their ability to correctly place an individual within the cost groups over 12 months of enrollment. RESULTS: The variables that best classified enrollees into 12-month cost groups included quality of life, age, and preenrollment health services. Classification was best for CART: a sensitivity of 39.8% and a specificity of 83% was achieved for very high cost enrollees. Compared with the clinical algorithm, CART also utilized only one third as many predictor variables to define risk categories. CONCLUSION: Brief self-report survey data collected at enrollment can be modeled to provide moderate sensitivity in predicting very high costs in the postenrollment year. The ease of applicability of software to create empirically derived profiles of high-cost enrollees precludes the need to rely on clinically devised algorithms to identify enrollees at risk for very high costs.

Aged↗
PubMed1999

Bayesian predictive identification and cumulative classification of bacteria.

In this paper we give a mathematically precise formulation of an old idea in bacterial taxonomy, namely cumulative classification, where the taxonomy is continuously updated and possibly augmented as new strains are identified. Our formulation is based on Bayesian predictive probability distributions. The criterion for founding a new taxon is given a firm theoretical foundation based on prediction and it is given a clear-cut interpretation. We formulate an algorithm for cumulative classification and apply it to a large database of bacteria belonging to the family Enterobacteriaceae. The resulting taxonomy makes microbiological sense.

Bacteria↗
PubMed2006

Differentiation of sCJD and vCJD forms by automated analysis of basal ganglia intensity distribution in multisequence MRI of the brain--definition and evaluation of new MRI-based ratios.

We present a method for the analysis of basal ganglia (including the thalamus) for accurate detection of human spongiform encephalopathy in multisequence magnetic resonance imaging (MRI) of the brain. One common feature of most forms of prion protein diseases is the appearance of hyperintensities in the deep grey matter area of the brain in T2-weighted magnetic resonance (MR) images. We employ T1, T2, and Flair-T2 MR sequences for the detection of intensity deviations in the internal nuclei. First, the MR data are registered to a probabilistic atlas and normalized in intensity. Then smoothing is applied with edge enhancement. The segmentation of hyperintensities is performed using a model of the human visual system. For more accurate results, a priori anatomical data from a segmented atlas are employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance with the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. The caudate nuclei are highlighted as main areas of diagnosis of sporadic Creutzfeldt-Jakob Disease (sCJD), in agreement with the histological data. The algorithm permitted the classification of the intensities of abnormal signals in sCJD patient FLAIR images with a higher hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Defining normalized MRI measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sCJD and variant CJD (vCJD) patients, in an attempt to create an automatic classification tool of human spongiform encephalopathies.

Algorithms↗
PubMed2006

Reconstruction of maxillectomy and midfacial defects with free tissue transfer.

The maxillary bones are part of the midfacial skeleton and are closely related to the eyeglobe, nasal airway, and oral cavity. Together with the overlying soft tissues, the two maxillae are responsible to a large extent for facial contour. Maxillectomy defects become more complex when critical structures such as the orbit, globe, and cranial base are resected, and reconstruction with distant tissues become essential. In this article, we describe a classification system and algorithm for reconstruction of these complex defects using various pedicled and free flaps. Most defects that involve resection of the maxilla and adjacent soft tissues may be classified into one of the following four types: Type I defects, Limited maxillectomy; Type II defects, Subtotal maxillectomy; Type III defects, Total maxillectomy; and Type IV defects, Orbitomaxillectomy. Using this classification, reconstruction of maxillectomy and midfacial defects may be approached considering the relationship between volume and surface area requirements, that is, addressing the bony defect first, followed by assessment of the associated soft tissue, skin, palate, and cheek-lining deficits. In our experience, most complex maxillectomy defects are best reconstructed using free tissue transfer. The rectus abdominis and radial forearm free flap in combination with immediate bone grafting or as an osteocutaneous flap reliably provide the best aesthetic and functional results. A temporalis muscle pedicled flap is used for reconstruction of maxillectomy defects only in those patients who are not candidates for a microsurgical procedure.

Adult↗
PubMed1993

Classification and physical diagnosis of instability of the shoulder.

Recent clinical and basic science work has increased our knowledge of shoulder instability. Valuable information has been gained by cutting studies of the capsuloligamentous complex to clarify the biomechanics of instability. It is now clear that a significant overlap exists in the concepts of instability and impingement relating to anterior shoulder pain. These concepts have added new importance to organization and standardization when classifying and diagnosing shoulder instability. This manuscript presents an algorithmic approach to classification of shoulder girdle complaints that may be related to instability. This classification system is designed around the direction, degree, chronology, cause, and volition of instability. With accurate and detailed analysis of the historical presentation, most shoulder instability complaints can be classified using this approach. The physical examination also should proceed in an orderly fashion, including general impression of the musculoskeletal system, inspection, palpation, and range of motion of both the uninvolved and involved shoulder, neurologic examination, rotator cuff evaluation, and stability assessment. Stability assessment includes glenohumeral translation examination with apprehension and provocative testing. Management techniques are predicted on an accurate diagnosis. With a thorough and organized examination, the correct diagnosis can be achieved.

Adult↗
PubMed2006

Artificial neural network-based classification to screen for dysphonia using psychoacoustic scaling of acoustic voice features.

SUMMARY: For diagnosis and classification of dysphonia, voice specialists can choose from an array of diagnostic tools like perceptual tests or acoustic voice analysis. These methods have in common that they require a high level of specialized training and experience, and therefore are mostly reserved to specialized centers. We aimed at developing an acoustic voice analysis system that could be used as a screening device to monitor, document, and diagnose voice problems that are also encountered by non-voice specialists, such as anesthesiologists, head and neck surgeons, and general surgeons before surgery of the thyroid gland and the upper thoracic aperture. An acoustical feature extraction paradigm that focused on jitter, shimmer, standard deviation of fundamental frequency, and the glottal-to-noise excitation ratio was used to reanalyse 120 voice samples previously analyzed by Schönweiler et al (A Novel Approach to Acoustical Voice Analysis Using Artificial Neural Networks. JARO. 2000:1;270-282). An improved artificial neural network (ANN) was used for classification. Building on this preliminary work, we modified the mathematical algorithm to further improve classification accuracy. Eighty percent of all voice samples could be classified correctly as either healthy or hoarse (sensitivity: 63.0%; specificity: 93.9%; area under the curve: 0.854). The adaptation of the ANN-voice analysis system for mobile use may facilitate its use and acceptance by non-voice specialists for the discovery and documentation of preexisting voice disorders, and may thereby lead to a timely initiation of further diagnosis and therapy by voice specialists.

Diagnosis, Differential↗
PubMed2003

National and regional rates and variation of cervical discectomy with and without anterior fusion, 1990-1999.

STUDY DESIGN: A national hospitalization database was used to determine rates and trends in the treatment of cervical disc disease. OBJECTIVE: To examine the temporal and geographic variations in hospitalizations and surgical procedures for cervical disc disease. SUMMARY OF BACKGROUND DATA: Studies of spinal surgery during the 1980s showed significant increases in the rates for all procedures, particularly those involving fusion. The management of cervical disc disease continues to be controversial. METHODS: Data from the National Hospital Discharge Survey from 1990 through 1999 were analyzed. Records were selected and categorized according to an algorithm of International Classification of Diseases (ICD-9) procedure and diagnosis codes. RESULTS: During the study period, the rate of hospitalization for surgical and nonsurgical treatment of cervical disc disease did not increase significantly. There was, however, a statistically significant increase in the proportion of hospitalizations for the surgical treatment of cervical disc disease that included a fusion procedure. There also was significant geographic variation in the rate of fusion procedures, with the South having the highest rate. CONCLUSIONS: Although the rate of surgery for cervical disc disease did not increase significantly during the 1990s, the rate of fusion procedures did rise significantly.

Age Distribution↗
PubMedDate not supplied

Color image analysis in neuroanatomical research: application to senile plaque subtype quantification in Alzheimer's disease.

Many problems in neuroanatomy and neuropathology require the collection of large data sets and would benefit from a method that allows for rapid quantitative analysis to be carried out on a routine basis. An example is the quantification and subtype classification of the number of senile plaques in post-mortem Alzheimer's disease tissue. A method to reliably automate the analysis of plaques and their underlying subtypes would allow more rigorous and quantitative correlations to be investigated. Computer assisted image analysis of data typically utilizes gray scale images. These methods, however, are only applicable to quantification of objects labeled with a single marker. We sought to extend this type of analysis to double-labeled tissue sections so we could quantify dual labels separately based on their peroxidase color characteristics, analyze the resultant occurrence of overlap between the two labels, and classify senile plaques into discrete subtypes. We present a method for semi-automated color image analysis which allows one to identify separate labels based on histogram mapping of hue, saturation and value as well as apply overlapping feature detection algorithms. The technique is application driven, so that a trained observer can set threshold or object criteria and verify the desired results. These methods were able to yield total "amyloid load" and "dystrophic neurite load" values, generate plaque histograms based on total size, and subtype plaques into diffuse/primitive and neuritic/classical categories. By adjusting feature criteria, we were able to achieve promising agreement (Fisher's R to Z correlation of 0.94) between a human observer and the computer algorithm in the classification of plaque subtypes using three AD cases.

Algorithms↗
PubMed1998

On-line 3D evaluation of left ventricular wall motion in magnetic resonance imaging.

In the present research a system for medical image processing has been proposed, which allows dynamic 3D visualization and successive left ventricular wall analysis. The 3D volume rendering algorithm results are presented using a friendly Graphical User Interface (GUI) giving the possibility to change interactively image processing and visualization parameters at any step, to perform simple and effective image manipulations. The left ventricular wall detection algorithm is based on the evaluation of classified volumetric data during volume rendering algorithm. In fact classification operation includes a 3D segmentation. The system has been tested in medical environment, for Magnetic Resonance (MR) images. Resulting studies have demonstrated a very high global efficiency in practical situations by using typical data volume dimensions.

Algorithms↗
PubMed2005

Constructing a speculative kernel machine for pattern classification.

We propose and investigate the performance of a new geometry-based algorithm designed to identify potentially informative data points for classification. An incremental QR update scheme is used to build a classifier using a subset of these points as radial basis function centers. The minimum descriptive length and the leave-one-out error criteria are employed for automatic model selection. The proposed scheme is shown to generate parsimonious models, which perform generalization comparable to the state-of-the-art support and relevance vector machines.

Algorithms↗
PubMed1990

Methodology of ECG interpretation in the Nagoya program.

The outline of the Nagoya Program, the most representative software package for computerized electrocardiography in Japan, is described. First, the history and current status of research and its transfer to commercial products for computerized interpretation of electrocardiograms is briefly reviewed. Second, development and testing of the Nagoya Program is depicted, referring to a gold standard for evaluation and the database being used for this purpose, and to its participation in the CSE study. Third, a description of signal acquisition and wave identification is given, followed by the detection of rhythm disturbances. Next, algorithms for contour classification, which consist of decision-tree logic established through trial and error and making use of the database of ECGs, are described. Finally, special features and future plans of the Nagoya Program are mentioned.

Algorithms↗
PubMed1996

A neural network approach to real-time spike discrimination during simultaneous recording from several multi-unit nerve filaments.

A multi-channel, real-time, unsupervised spike discriminator was developed in order to reconstruct single spike trains from several simultaneously recorded multi-unit nerve filaments. The program uses a Self Organising Map (SOM) algorithm for the classification of the spikes. In contrast to previous similar techniques, the described method is made for use on a PC, and the method may thus be implemented at relatively low cost. In order to test the accuracy of the program, a robustness test was performed, where noise with different RMS levels was superimposed on the spikes. Furthermore, the maximal classification rate was determined. The program is easy to use, since the only manual inputs needed are the voltage threshold for spike detection, and the number of units present in each recorded nerve filament.

Action Potentials↗