Boolean Query Reformulation with the Query Tree Classifier

One of the difficulties in using the current Boolean-based information retrieval systems is that it is hard for a user, especially a novice, to formulate an effective Boolean query. Query reformulation can be even more difficult and complex than formulation since the user can have difficulty in incorporating the new information gained from the previous search into his/her next query. In this research, query reformulation is viewed as a classification problem (i.e., classifying documents as either relevant or nonrelevant), and a new reformulation algorithm is proposed which builds a treestructured classifier (named the query tree) at each reformulation from a set of feedback documents retrieved from the previous search. The query tree can be easily transformed into a Boolean query. The query tree and two of the most important current query reformulation algorithms were compared on benchmark test sets (CACM. CISI, and MedJars). The query tree showed significant improvements over the current algorithms in most experiments. We attribute this improved performance to the ability of the query tree algorithm to select good search terms and to represent the relationships among search terms into a tree structure

Causes of prehospital misinterpretations of ST elevation myocardial infarction

Objectives: To determine the causes of software misinterpretation of ST elevation myocardial infarction (STEMI) compared to clinically identified STEMI to identify opportunities to improve prehospital STEMI identification. Methods: We compared ECGs acquired from July 2011 through June 2012 using the LIFEPAK 15 on adult patients transported by the Los Angeles Fire Department. Cases included patients ≥18 years who received a prehospital ECG. Software interpretation of the ECG (STEMI or not) was compared with data in the regional EMS registry to classify the interpretation as true positive (TP), true negative (TN), false positive (FP), or false negative (FN). For cases where classification was not possible using registry data, 3 blinded cardiologists interpreted the ECG. Each discordance was subsequently reviewed to determine the likely cause of misclassification. The cardiologists independently reviewed a sample of these discordant ECGs and the causes of misclassification were updated in an iterative fashion. Results: Of 44,611 cases, 50% were male (median age 65; inter-quartile range 52–80). Cases were classified as 482 (1.1%) TP, 711 (1.6%) FP, 43371 (97.2%) TN, and 47 (0.11%) FN. Of the 711 classified as FP, 126 (18%) were considered appropriate for, though did not undergo, emergent coronary angiography, because the ECG showed definite (52 cases) or borderline (65 cases) ischemic ST elevation, a STEMI equivalent (5 cases) or ST-elevation due to vasospasm (4 cases). The sensitivity was 92.8% [95% CI 90.6, 94.7%] and the specificity 98.7% [95% CI 98.6, 98.8%]. The leading causes of FP were ECG artifact (20%), early repolarization (16%), probable pericarditis/myocarditis (13%), indeterminate (12%), left ventricular hypertrophy (8%), and right bundle branch block (5%). There were 18 additional reasons for FP interpretation (<4% each). The leading causes of FN were borderline ST-segment elevations less than the algorithm threshold (40%) and tall T waves reducing the ST/T ratio below threshold (15%). There were 11 additional reasons for FN interpretation occurring ≤3 times each. Conclusion: The leading causes of FP automated interpretation of STEMI were ECG artifact and non-ischemic causes of ST-segment elevation. FN were rare and were related to ST-segment elevation or ST/T ratio that did not meet the software algorithm threshold

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

Background: Grain commodities are important to people's daily lives and their fluctuations can cause instability for households. Accurate prediction of grain prices can improve food and social security. Methods & Materials: This study proposes a hybrid Long Short-Term Memory (LSTM)-Convolutional Neural Network (CNN) model to forecast weekly oat, corn, soybean and wheat prices in the United States market. The LSTM-CNN is a multivariate model that uses weather data, macroeconomic data, commodities grain prices and snow factors, including Snow Water Equivalent (SWE), snowfall and snow depth, to make multistep ahead forecasts. Results: Of all the features, the snow factor is used for the first time for commodity price forecasting. We used the LSTM-CNN model to evaluate the 5, 10, 15 and 20 weeks ahead forecasting and this hybrid model had the lowest Mean Squared Error (MSE) at 5, 10 and 15 weeks ahead of prediction. In addition, Shapley values were calculated to analyse the feature contribution of the LSTM-CNN model when forecasting the testing set. Based on the feature contribution, SWE ranked third, fifth and seventh in feature importance in the 5-week ahead forecast for corn, oats and wheat, respectively, and 7–8 places higher than total precipitation, indicating the potential use of SWE in grain price forecasting. Conclusion: The hybrid multivariate LSTM-CNN model outperformed other models and the newly involved climate data, SWE, showed the research potential of using snow as an input variable to predict grain prices over a multistep ahead time horizon

Dynamic lactate indices as predictors of outcome in critically ill patients

INTRODUCTION: Dynamic changes in lactate concentrations in the critically ill may predict patient outcome more accurately than static indices. We aimed to compare the predictive value of dynamic indices of lactatemia in the first 24 hours of intensive care unit (ICU) admission with the value of more commonly used static indices. METHODS: This was a retrospective observational study of a prospectively obtained intensive care database of 5,041 consecutive critically ill patients from four Australian university hospitals. We assessed the relationship between dynamic lactate values collected in the first 24 hours of ICU admission and both ICU and hospital mortality. RESULTS: We obtained 36,673 lactate measurements in 5,041 patients in the first 24 hours of ICU admission. Both the time weighted average lactate (LACTW₂₄) and the change in lactate (LACΔ₂₄) over the first 24 hours were independently predictive of hospital mortality with both relationships appearing to be linear in nature. For every one unit increase in LACTW₂₄ and LACΔ₂₄ the risk of hospital death increased by 37% (OR 1.37, 1.29 to 1.45; P < 0.0001) and by 15% (OR 1.15, 1.10 to 1.20; P < 0.0001) respectively. Such dynamic indices, when combined with Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, improved overall outcome prediction (P < 0.0001) achieving almost 90% accuracy. When all lactate measures in the first 24 hours were considered, the combination of LACTW₂₄ and LACΔ₂₄ significantly outperformed (P < 0.0001) static indices of lactate concentration, such as admission lactate, maximum lactate and minimum lactate. CONCLUSIONS: In the first 24 hours following ICU admission, dynamic indices of hyperlactatemia have significant independent predictive value, improve the performance of illness severity score-based outcome predictions and are superior to simple static indices of lactate concentration

Petrology, Palynology, and Geochemistry of Gray Hawk Coal (Early Pennsylvanian, Langsettian) in Eastern Kentucky, USA

This study presents recently collected data examining the organic petrology, palynology, mineralogy and geochemistry of the Gray Hawk coal bed. From the Early Pennsylvanian, Langsettian substage, Gray Hawk coal has been mined near the western edge of the eastern Kentucky portion of the Central Appalachian coalfield. While the coal is thin, rarely more than 0.5-m thick, it has a low-ash yield and a low-S content, making it an important local resource. The Gray Hawk coal palynology is dominated by Lycospora spp., and contains a diverse spectrum of small lycopods, tree ferns, small ferns, calamites, and gymnosperms. The maceral assemblages show an abundance of collotelinite, telinite, vitrodetrinite, fusinite, and semifusinite. Fecal pellet-derived macrinite, albeit with more compaction than is typically seen in younger coals, was observed in the Gray Hawk coal. The minerals in the coal are dominated by clay minerals (e.g., kaolinite, mixed-layer illite/smectite, illite), and to a lesser extent, pyrite, quartz, and iron III hydroxyl-sulfate, along with traces of chlorite, and in some cases, jarosite, szomolnokite, anatase, and calcite. The clay minerals are of authigenic and detrital origins. The occurrence of anatase as cell-fillings also indicates an authigenic origin. With the exception of Ge and As, which are slightly enriched in the coals, the concentrations of other trace elements are either close to or much lower than the averages for world hard coals. Arsenic and Hg are also enriched in the top bench of the coal and probably occur in pyrite. The elemental associations (e.g., Al2O3/TiO2, Cr/Th-Sc/Th) indicate a sediment-source region with intermediate and felsic compositions. Rare metals, including Ga, rare earth elements and Ge, are highly enriched in the coal ashes, and the Gray Hawk coals have a great potential for industrial use of these metals. The rare earth elements in the samples are weakly fractionated or are characterized by heavy-REE enrichment, indicating an input of natural waters or probably epithermal solutions

Latent variables underlying the memory beliefs of Chartered Clinical Psychologists, Hypnotherapists and undergraduate students

In courts in the United Kingdom understanding of memory phenomena is often assumed to be within the ‘common knowledge’ of the average juror. Many studies suggest that this is not a safe assumption, demonstrating that even professional groups sometimes express beliefs that are not in accordance with the scientific consensus. To test this assumption three hundred and thirty seven UK respondents, consisting of 125 Chartered Clinical Psychologists, 88 individuals who advertised their services as Hypnotherapists in a classified directory, the Yellow PagesTM, and 124 first year undergraduate psychology students, completed a questionnaire that assessed their knowledge of ten memory phenomena about which there is a broad scientific consensus. Hypnotherapists’ responses were the most inconsistent with the scientific consensus, scoring lowest on six of these ten items. Principal Components Analysis indicated two latent variables – reflecting beliefs about memory quality and malleability – underlying respondents’ responses. In addition, respondents were asked to rate their own knowledge of the academic memory literature in general. There was no significant relationship between participants’ self reported knowledge and their actual knowledge (as measured by their responses to the ten-item questionnaire). There was evidence of beliefs among the Hypnotherapists that could give rise to some concern (e.g., that early memories from the first year of life are accurately stored and are retrievable)