Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding

We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts

Machine learning reduced workload with minimal risk of missing studies: development and evaluation of an RCT classifier for Cochrane Reviews

BACKGROUND: To describe the development, calibration and evaluation of a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. METHODS: A machine learning classifier for retrieving RCTs was developed (the ‘Cochrane RCT Classifier’), with the algorithm trained using a dataset of title-abstract records from Embase, manually labelled by the Cochrane Crowd. The classifier was then calibrated using a further dataset of similar records manually labelled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. RESULTS: The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs) and our bootstrap validation found the classifier had recall of 0.99 (95% CI 0.98 to 0.99) and precision of 0.08 (95% CI 0.06 to 0.12) in this dataset. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. CONCLUSIONS: The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production

Single vs double anti-crossing in the strong coupling between surface plasmons and molecular excitons (article)

This is the final version. Available on open access from AIP Publishing via the DOI in this recordThe dataset associated with this article is located in ORE at: https://doi.org/10.24378/exe.3023Strong coupling between surface plasmons and molecular excitons may lead to the formation of new hybrid states—polaritons—that are part light and part matter in character. A key signature of this strong coupling is an anti-crossing of the exciton and surface plasmon modes on a dispersion diagram. In a recent report on strong coupling between the plasmon modes of a small silver nano-rod and a molecular dye, it was shown that when the oscillator strength of the exciton is large enough, an additional anti-crossing feature may arise in the spectral region where the real part of the permittivity of the excitonic material is zero. However, the physics behind this double anti-crossing feature is still unclear. Here, we make use of extensive transfer matrix simulations to explore this phenomenon. We show that for low oscillator strengths of the excitonic resonance, there is a single anti-crossing arising from strong coupling between the surface plasmon and the excitonic resonance, which is associated with the formation of upper and lower plasmon–exciton polaritons. As the oscillator strength is increased, we find that a new mode emerges between these upper and lower polariton states and show that this new mode is an excitonic surface mode. Our study also features an exploration of the role played by the orientation of the excitonic dipole moment and the relationship between the modes we observe and the transverse and longitudinal resonances associated with the excitonic response. We also investigate why this type of double splitting is rarely observed in experiments.Engineering and Physical Sciences Research Council (EPSRC)European Research Council (ERC

The PAMINO-project: evaluating a primary care-based educational program to improve the quality of life of palliative patients

<p>Abstract</p> <p>Background</p> <p>The care of palliative patients challenges the health care system in both quantity and quality. Especially the role of primary care givers needs to be strengthened to provide them with the knowledge and the confidence of applying an appropriate end-of-life care to palliative patients. To improve health care services for palliative patients in primary care, interested physicians in and around Heidelberg, Germany, are enabled to participate in the community-based program 'Palliative Medical Initiative North Baden (PAMINO)' to improve their knowledge in dealing with palliative patients. The impact of this program on patients' health and quality of life remains to be evaluated.</p> <p>Methods/Design</p> <p>The evaluation of PAMINO is a non-randomized, controlled study. Out of the group of primary care physicians who took part in the PAMINO program, a sample of 45 physicians and their palliative patients will be compared to a sample of palliative patients of 45 physicians who did not take part in the program. Every four weeks for 6 months or until death, patients, physicians, and the patients' family caregivers in both groups answer questions to therapy strategies, quality of life (QLQ-C15-PAL, POS), pain (VAS), and burden for family caregivers (BSFC). The inclusion of physicians and patients in the study starts in March 2007.</p> <p>Discussion</p> <p>Although participating physicians value the increase in knowledge they receive from PAMINO, the effects on patients remain unclear. If the evaluation reveals a clear benefit for patients' quality of life, a larger-scale implementation of the program is considered. </p> <p><b>Trial registration</b>: The study was registered at ‘current controlled trials (CCT)’, registration number: ISRCTN78021852.</p

A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation

We consider the task of automatically annotating free texts describing clinical trials with concepts from a controlled, structured medical vocabulary. Specifically, we aim to build a model to infer distinct sets of (ontological) concepts describing complementary clinically salient aspects of the underlying trials: the populations enrolled, the interventions administered and the outcomes measured, i.e., the PICO elements. This important practical problem poses a few key challenges. One issue is that the output space is vast, because the vocabulary comprises many unique concepts. Compounding this problem, annotated data in this domain is expensive to collect and hence sparse. Furthermore, the outputs (sets of concepts for each PICO element) are correlated: specific populations (e.g., diabetics) will render certain intervention concepts likely (insulin therapy) while effectively precluding others (radiation therapy). Such correlations should be exploited. We propose a novel neural model that addresses these challenges. We introduce a Candidate-Selector architecture in which the model considers setes of candidate concepts for PICO elements, and assesses their plausibility conditioned on the input text to be annotated. This relies on a 'candidate set' generator, which may be learned or relies on heuristics. A conditional discriminative neural model then jointly selects candidate concepts, given the input text. We compare the predictive performance of our approach to strong baselines, and show that it outperforms them. Finally, we perform a qualitative evaluation of the generated annotations by asking domain experts to assess their quality

Surface enhanced raman scattering artificial nose for high dimensionality fingerprinting

Label-free surface-enhanced Raman spectroscopy (SERS) can interrogate systems by directly fingerprinting its components’ unique physicochemical properties. In complex biological systems however, this can yield highly overlapping spectra that hinder sample identification. Here, we present an artificial-nose inspired SERS fingerprinting approach where spectral data is obtained as a function of sensor surface chemical functionality. Supported by molecular dynamics modelling, we show that mildly selective self-assembled monolayers can influence the strength and configuration in which analytes interact with plasmonic surfaces, diversifying the resulting SERS fingerprints. Since each sensor generates a modulated signature, the implicit value of increasing the dimensionality of datasets is shown using cell lysates for all possible combinations of up to 9 fingerprints. Reliable improvements in mean discriminatory accuracy towards 100% is achieved with each additional surface functionality. This arrayed label-free platform illustrates the wide-ranging potential of high dimensionality artificial-nose based sensing systems for more reliable assessment of complex biological matrices

The magic nature of 132Sn explored through the single-particle states of 133Sn

Atomic nuclei have a shell structure where nuclei with 'magic numbers' of neutrons and protons are analogous to the noble gases in atomic physics. Only ten nuclei with the standard magic numbers of both neutrons and protons have so far been observed. The nuclear shell model is founded on the precept that neutrons and protons can move as independent particles in orbitals with discrete quantum numbers, subject to a mean field generated by all the other nucleons. Knowledge of the properties of single-particle states outside nuclear shell closures in exotic nuclei is important for a fundamental understanding of nuclear structure and nucleosynthesis (for example the r-process, which is responsible for the production of about half of the heavy elements). However, as a result of their short lifetimes, there is a paucity of knowledge about the nature of single-particle states outside exotic doubly magic nuclei. Here we measure the single-particle character of the levels in 133Sn that lie outside the double shell closure present at the short-lived nucleus 132Sn. We use an inverse kinematics technique that involves the transfer of a single nucleon to the nucleus. The purity of the measured single-particle states clearly illustrates the magic nature of 132Sn.Comment: 19 pages, 5 figures and 4 table

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Foot pain and foot health in an educated population of adults: results from the Glasgow Caledonian University Alumni Foot Health Survey

Abstract Background Foot pain is common amongst the general population and impacts negatively on physical function and quality of life. Associations between personal health characteristics, lifestyle/behaviour factors and foot pain have been studied; however, the role of wider determinants of health on foot pain have received relatively little attention. Objectives of this study are i) to describe foot pain and foot health characteristics in an educated population of adults; ii) to explore associations between moderate-to-severe foot pain and a variety of factors including gender, age, medical conditions/co-morbidity/multi-morbidity, key indicators of general health, foot pathologies, and social determinants of health; and iii) to evaluate associations between moderate-to-severe foot pain and foot function, foot health and health-related quality-of-life. Methods Between February and March 2018, Glasgow Caledonian University Alumni with a working email address were invited to participate in the cross-sectional electronic survey (anonymously) by email via the Glasgow Caledonian University Alumni Office. The survey was constructed using the REDCap secure web online survey application and sought information on presence/absence of moderate-to-severe foot pain, patient characteristics (age, body mass index, socioeconomic status, occupation class, comorbidities, and foot pathologies). Prevalence data were expressed as absolute frequencies and percentages. Multivariate logistic and linear regressions were undertaken to identify associations 1) between independent variables and moderate-to-severe foot pain, and 2) between moderate-to-severe foot pain and foot function, foot health and health-related quality of life. Results Of 50,228 invitations distributed, there were 7707 unique views and 593 valid completions (median age [inter-quartile range] 42 [31–52], 67.3% female) of the survey (7.7% response rate). The sample was comprised predominantly of white Scottish/British (89.4%) working age adults (95%), the majority of whom were overweight or obese (57.9%), and in either full-time or part-time employment (82.5%) as professionals (72.5%). Over two-thirds (68.5%) of the sample were classified in the highest 6 deciles (most affluent) of social deprivation. Moderate-to-severe foot pain affected 236/593 respondents (39.8%). High body mass index, presence of bunions, back pain, rheumatoid arthritis, hip pain and lower occupation class were included in the final multivariate model and all were significantly and independently associated with moderate-to-severe foot pain (p < 0.05), except for rheumatoid arthritis (p = 0.057). Moderate-to-severe foot pain was significantly and independently associated lower foot function, foot health and health-related quality of life scores following adjustment for age, gender and body mass index (p < 0.05). Conclusions Moderate-to-severe foot pain was highly prevalent in a university-educated population and was independently associated with female gender, high body mass index, bunions, back pain, hip pain and lower occupational class. Presence of moderate-to-severe foot pain was associated with worse scores for foot function, foot health and health-related quality-of-life. Education attainment does not appear to be protective against moderate-to-severe foot pain