Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all‐cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all‐cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free‐living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random Under‐ Sampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data‐driven and disease‐agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness

Multivariate calibration of energy-dispersive X-ray diffraction data for predicting the composition of pharmaceutical tablets in packaging

A system using energy-dispersive X-ray diffraction (EDXRD) has been developed and tested using multivariate calibration for the quantitative analysis of tablet-form mixtures of common pharmaceutical ingredients. A principal advantage of EDXRD over the more traditional and common angular dispersive X-ray diffraction technique (ADXRD) is the potential of EDXRD to analyse tablets within their packaging, due to the higher energy X-rays used. In the experiment, a series of caffeine, paracetamol and microcrystalline cellulose mixtures were prepared and pressed into tablets. EDXRD profiles were recorded on each sample and a principal component analysis (PCA) was carried out in both unpackaged and packaged scenarios. In both cases the first two principal components explained >98% of the between-sample variance. The PCA projected the sample profiles into two dimensional principal component space in close accordance to their ternary mixture design, demonstrating the discriminating potential of the EDXRD system. A partial least squares regression (PLSR) model was built with the samples and was validated using leave-one-out cross-validation. Low prediction errors of between 2% and 4% for both unpackaged and packaged tablets were obtained for all three chemical compounds. The prediction capability through packaging demonstrates a truly non-destructive method for quantifying tablet composition and demonstrates good potential for EDXRD to be applied in the field of counterfeit medicine screening and pharmaceutical quality control

Quantitative breast MRI: 2D histogram analysis of diffusion tensor parameters in normal tissue

OBJECT: Diffusion tensor imaging (DTI) of the breast may provide a powerful new approach for the detection of intraductal processes. The aim of this investigation was to characterize the relation between diffusion tensor parameters [fractional anisotropy (FA), mean diffusivity (MD)] in normal breast tissue to obtain information on the microenvironment of the diffusing water molecules and to provide a systematic approach for DTI analysis. MATERIALS AND METHODS: Seven female, healthy volunteers underwent prospective double-spin-echo prepared echo-planar diffusion-weighted sequence (TR/TE 8,250 ms/74 ms, b values 0 and 500 s/mm (2), six encoding directions, 12 averages, 35 slices) in 4 consecutive weeks (3.0 T). Quantitative maps of diffusion tensor parameters were computed offline with custom routines. The interdependence of MD and FA in different voxels was analysed by linear and exponential regression. RESULTS: All MD and FA maps were of excellent quality. A consistent pattern was observed in that lower fractional anisotropy values were more likely associated with higher mean diffusivity values. The dependence exhibited an exponential behavior with a correlation coefficient R = 0.60 (R linear = 0.57). CONCLUSION: The likelihood with which FA and MD values are observed in a voxel within normal breast tissue is characterized by a specific pattern, which can be described by an exponential model. Moreover, we could show that the proposed technique does not depend on the menstrual cycle

A technological study of Assyrian clay tablets from Nineveh, Tell Halaf and Nimrud: a pilot case study

Ancient Middle Eastern clay tablets inscribed with cuneiform writing have traditionally been studied more as textual documents than as archaeological objects per se. In contrast to previous analytical studies which, with few exceptions, focused on provenance and palaeo-environmental reconstruction, the current study aims to describe the tablet makers’ technological choices, to understand whether a specific series of steps or chaîne opératoire was followed to produce these important documents. Twenty cuneiform tablets found at the sites of Nineveh, Nimrud (Iraq) and Tell Halaf (Syria), and curated in the British Museum collection, were analysed by optical microscopy of minero-petrographic thin sections and scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM–EDX). Six of these tablets were also analysed by X-ray computed tomography (CT), to investigate the potential for this non-invasive technique to address the technological questions and to select objects for invasive analyses. The results show that the tablets were made following similar steps to pottery making, either carefully levigating calcareous clays, or adding plant matter to make the clay less plastic. Petrographic and CT analyses are readily comparable and CT results permit a more targeted approach to invasive sampling

Neutron tomography of sealed copper alloy animal coffins from ancient Egypt

Animal mummification was commonplace in ancient Egypt, with the remains of many animals placed inside statues or votive boxes with representations of animals or hybrid human–animal creatures. Votive boxes were made from a variety of materials and often sealed; some boxes are still preserved in this state in museum collections. A prior study of sealed copper alloy votive boxes from the collection of the British Museum used X-ray computed tomography to search for animal remains, where poor image quality resulted due to attenuation from the boxes and apparent dense metals inside. In this study, neutron tomography was applied to six of the votive boxes previously examined. Animal remains, likely from lizards, and fragments of textile wrappings were discovered inside three of the boxes. Evidence of the manufacturing process and subsequent repairs of the boxes were uncovered by neutrons. Significant quantities of lead were also identified in three boxes. The findings demonstrate the effectiveness of neutron tomography for the study of mummified remains inside sealed metal containers, and give evidence linking the animal figures represented on top of votive boxes to the concealed remains

To what extent does the health professions admission test-ireland predict performance in early undergraduate tests of communication and clinical skills? – an observational cohort study

Background: Internationally, tests of general mental ability are used in the selection of medical students. Examples include the Medical College Admission Test, Undergraduate Medicine and Health Sciences Admission Test and the UK Clinical Aptitude Test. The most widely used measure of their efficacy is predictive validity. A new tool, the Health Professions Admission Test-Ireland (HPAT-Ireland), was introduced in 2009. Traditionally, selection to Irish undergraduate medical schools relied on academic achievement. Since 2009, Irish and EU applicants are selected on a combination of their secondary school academic record (measured predominately by the Leaving Certificate Examination) and HPAT-Ireland score. This is the first study to report on the predictive validity of the HPAT-Ireland for early undergraduate assessments of communication and clinical skills. Method: Students enrolled at two Irish medical schools in 2009 were followed up for two years. Data collected were gender, HPAT-Ireland total and subsection scores; Leaving Certificate Examination plus HPAT-Ireland combined score, Year 1 Objective Structured Clinical Examination (OSCE) scores (Total score, communication and clinical subtest scores), Year 1 Multiple Choice Questions and Year 2 OSCE and subset scores. We report descriptive statistics, Pearson correlation coefficients and Multiple linear regression models. Results: Data were available for 312 students. In Year 1 none of the selection criteria were significantly related to student OSCE performance. The Leaving Certificate Examination and Leaving Certificate plus HPAT-Ireland combined scores correlated with MCQ marks. In Year 2 a series of significant correlations emerged between the HPAT-Ireland and subsections thereof with OSCE Communication Z-scores; OSCE Clinical Z-scores; and Total OSCE Z-scores. However on multiple regression only the relationship between Total OSCE Score and the Total HPAT-Ireland score remained significant; albeit the predictive power was modest. Conclusion: We found that none of our selection criteria strongly predict clinical and communication skills. The HPAT-Ireland appears to measures ability in domains different to those assessed by the Leaving Certificate Examination. While some significant associations did emerge in Year 2 between HPAT Ireland and total OSCE scores further evaluation is required to establish if this pattern continues during the senior years of the medical course

Multiferroic properties and magnetic structure of Sm_{1-x}Y_{x}MnO_{3}

We have successfully induced multiferroic behavior in the A-type antiferromagnet SmMnO(3) by the substitution of Y at the Sm site. A magnetic transition develops at similar to 24 K for Sm(1-x)Y(x)MnO(3) (x = 0.4, 0.5) which is not present in the parent compound. This transition coincides with the onset of electric order, with an electric polarization measured along the c axis. It is proposed that the effect of Y doping is to bring about a subtle distortion of the MnO(6) octahedra, causing a magnetic ordering of the Mn(3+) moments similar to that reported for the well-studied multiferroic TbMnO(3). Following on from our previous study on polycrystalline samples, we present measurements of the magnetic and electric properties of single-crystal Sm(0.6)Y(0.4)MnO(3) and Sm(0.5)Y(0.5)MnO(3). The data are summarized in a phase diagram for each of the principal crystallographic axes for the x = 0.5 compound. Powder neutron diffraction experiments on SmMnO(3) and Sm(0.6)Y(0.4)MnO(3) show that the Y substitution causes a change in the Mn-O-Mn bond angle toward the value found for TbMnO(3). The magnetic structure of Sm(0.6)Y(0.4)MnO(3) has been shown to consist of two phases: a sinusoidal ordering of the Mn(3+) moments below 50 K and a cycloidal ordering below 27 K. The cycloidal ordering occurs at the same temperature as the previously observed ferroelectric polarization, suggesting a similar multiferroic mechanism to that found in TbMnO(3)

Strain coupling in multiferroic phase transitions of samarium yttrium manganite Sm_{0.6}Y_{0.4}MnO_{3}

Sm1−xYxMnO3 (SYM x) is one of a class of multiferroic manganites that has seen significant recent interest due to the intimate connection between cycloidal magnetic order and ferroelectricity in these materials. SYM shows sequential transitions between paramagnetic, sinusoidally ordered antiferromagnetic and cycloidally ordered antiferromagnetic phases with decreasing temperature. As in the other members of the family, the magnetic spin cycloid induces ferroelectricity, although whether there is any elastic coupling involved in this process is not known. In this work, resonant ultrasound spectroscopy (RUS) is used to examine the stiffness and dissipation in SYM 0.4 as the magnetic transitions are traversed. It is found that there are only very small signatures of the transitions in the elastic properties of the material, indicating the weakness of the magnetoelastic (and electroelastic) coupling. The mechanical loss does show a significant decrease upon cooling below TN1=∼50 K, indicating the freezing of some loss mechanism near the temperature where magnetic order is achieved. The strain at these magnetic transitions in a related material, Eu1−xYxMnO3, is examined from data published in the literature, and very low shear strain is observed, along with a more significant volume strain effect. This correlates well with the observations from RUS, as the peak frequencies are more sensitive to shear effects than bulk effects. These results suggest that the weak coupling of the magnetic transitions with shear may be a more general behavior in multiferroic perovskite-related manganites

Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums

AbstractVisualizing the internal structure of museum objects is a crucial step in acquiring knowledge about the origin, state, and composition of cultural heritage artifacts. Among the most powerful techniques for exposing the interior of museum objects is computed tomography (CT), a technique that computationally forms a 3D image using hundreds of radiographs acquired in a full circular range. However, the lack of affordable and versatile CT equipment in museums, combined with the challenge of transporting precious collection objects, currently keeps this technique out of reach for most cultural heritage applications. We propose an approach for creating accurate CT reconstructions using only standard 2D radiography equipment already available in most larger museums. Specifically, we demonstrate that a combination of basic X-ray imaging equipment, a tailored marker-based image acquisition protocol, and sophisticated data-processing algorithms, can achieve 3D imaging of collection objects without the need for a costly CT imaging system. We implemented this approach in the British Museum (London), the J. Paul Getty Museum (Los Angeles), and the Rijksmuseum (Amsterdam). Our work paves the way for broad facilitation and adoption of CT technology across museums worldwide.</jats:p