Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction

The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets

Reassessment of the systematic position of Orthocomotis DOGNIN (Lepidoptera: Tortricidae) based on molecular data with description of new species of Euliini

The application of molecular analyses for resolving taxonomic problems in the family Torticidae (Lepidoptera) is still uncommon. The majority of papers concern the assessment of population variability of economically important species; reports on the systematic positions of Neotropical Tortricidae taxa are rare. The Neotropical genus Orthocomotis was classified initially as a member of the tribe Euliini. Then, based on genital morphology, it was moved to the tribe Polyorthini. A comparison of homologous 606 bp fragments of the COI mitochondrial gene revealed that Orthocomotis should be transfered back into the tribe Euliini. Based on an analysis of phylogenetic relationships the studied genera of Euliini form a monophyletic cluster, clearly separated from tribe Polyorthini in which they were temporarily included. Moreover, in the current paper we describe two new species of the tribe Euliini: Galomecalpa lesta RAZOWSKI & PELZ, sp. n., Gauruncus ischyros RAZOWSKI & PELZ, sp. n

Diversidad y patrones de distribución de mariposas de la subtribu Pronophilina (Lepidoptera : Nymphalidae : Satyrinae) en un transecto altitudinal en el nor-oeste de Ecuador

Samplings of Pronophilina, a species-rich group of neotropical montane butterflies, were carried out along an elevational transect in Ecuador to assess the effect of altitude on their distribution patterns, diversity and community structure. All diversity indices were significantly correlated with altitude. Maximum diversity expressed in species-richness, Shannon index and Fisher alpha was recorded at 2600 m. Two assemblages of species were identified in the lower (below 2100 m) and upper (above 2300 m) sections of the transect by means of correspondence (CA) and cluster analysis. A comparison of Sørensen similarity coefficients showed lower values, thus higher turnover in the intermediate elevational band. Several closely related morphologically and ecologically species were found to have mutually exclusive altitudinal distribution patterns. A comparison with similar studies in Venezuela, Colombia and Peru revealed far reaching congruency of the patterns of altitudinal diversity of Pronophilina in distant areas of the Andes. In particular, the Shannon index reaches its maximum values at 2600-2850 m, which invariably correspond to ca. 400-500 m below the upper limit of cloud forest. Increase of diversity of Pronophilina with altitude is marginally related to higher limited resource availability. The lower pressure of predators and parasites at higher elevation can contribute with higher abundance, but cannot be directly correlated with higher diversity. Higher diversity is related with intrisic characteristics of the group, such as aggregated diversity by overlapping of elevational faunal assemblages and higher speciation ratio towards high elevations, particularly near timberline

Predictors of 6-month mortality among nursing home residents: Diagnoses maybe more predictive than functional disability

Objective: Loss of daily living functions can be a marker for end of life and possible hospice eligibility. Unfortunately, data on patient\u27s functional abilities is not available in all settings. In this study we compare predictive accuracy of two indices designed to predict 6-month mortality among nursing home residents. One is based on traditional measures of functional deterioration and the other on patients\u27 diagnoses and demography. Methods: We created the Hospice ELigibility Prediction (HELP) Index by examining mortality of 140,699 Veterans Administration (VA) nursing home residents. For these nursing home residents, the available data on history of hospital admissions were divided into training (112,897 cases) and validation (27,832 cases) sets. The training data were used to estimate the parameters of the HELP Index based on (1) diagnoses, (2) age on admission, and (3) number of diagnoses at admission. The validation data were used to assess the accuracy of predictions of the HELP Index. The cross-validated accuracy of the HELP Index was compared with the Barthel Index (BI) of functional ability obtained from 296,052 VA nursing home residents. A receiver operating characteristic curve was used to examine sensitivity and specificity of the predicted odds of mortality. Results: The area under the curve (AUC) for the HELP Index was 0.838. This was significantly (α \u3c0.01) higher than the AUC for the BI of 0.692. Conclusions: For nursing home residents, comorbid diagnoses predict 6-month mortality more accurately than functional status. The HELP Index can be used to estimate 6-month mortality from hospital data and can guide prognostic discussions prior to and following nursing home admission

The LEM3 System for Multitype Evolutionary Optimization

LEM3 is the newest version of the learnable evolution model (LEM), a non-Darwinian evolutionary computation methodology that employs machine learning to guide evolutionary processes. Due to the deep integration of different modes of operation, several novel elements in its algorithm, and the use of the advanced machine learning system AQ21, the LEM3 system is a highly efficient and effective implementation of the methodology. LEM3 is particularly attractive for multitype optimization because it supports, and treats accordingly, different attribute types for describing candidate solutions in the population. These attribute types are nominal, ordinal, structured, cyclic, interval, and ratio. Application to optimization of parameters of a complex system illustrates multitype optimization problem