Exploring Pattern Structures of Syntactic Trees for Relation Extraction

International audienceIn this paper we explore the possibility of defining an original pattern structure for managing syntactic trees.More precisely, we are interested in the extraction of relations such as drug-drug interactions (DDIs) in medical texts where sentences are represented as syntactic trees.In this specific pattern structure, called STPS, the similarity operator is based on rooted tree intersection.Moreover, we introduce "Lazy Pattern Structure Classification" (LPSC), which is a symbolic method able to extract and classify DDI sentences w.r.t. STPS.To decrease computation time, a projection and a set of tree-simplification operations are proposed.We evaluated the method by means of a 10-fold cross validation on the corpus of the DDI extraction challenge 2011, and we obtained very encouraging results that are reported at the end of the paper

Performance of binary prediction models in high-correlation low-dimensional settings:a comparison of methods

BACKGROUND: Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate. METHODS: We compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations. RESULTS: In the conducted simulations, no effect of collinearity was observed on predictive outcomes (AUC, R(2), Intercept, Slope) across methods. However, a negative effect of collinearity on the stability of predictor selection was found, affecting all compared methods, but in particular methods that perform strong predictor selection (e.g., Lasso). Methods for which the included set of predictors remained most stable under increased collinearity were Ridge, PCLR, LAELR, and Dropout. CONCLUSIONS: Based on the results, we would recommend refraining from data-driven predictor selection approaches in the presence of high collinearity, because of the increased instability of predictor selection, even in relatively high events-per-variable settings. The selection of certain predictors over others may disproportionally give the impression that included predictors have a stronger association with the outcome than excluded predictors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-021-00115-5

The relation between prediction model performance measures and patient selection outcomes for proton therapy in head and neck cancer

Background: Normal-tissue complication probability (NTCP) models predict complication risk in patients receiving radiotherapy, considering radiation dose to healthy tissues, and are used to select patients for proton therapy, based on their expected reduction in risk after proton therapy versus photon radiotherapy (ΔNTCP). Recommended model evaluation measures include area under the receiver operating characteristic curve (AUC), overall calibration (CITL), and calibration slope (CS), whose precise relation to patient selection is still unclear. We investigated how each measure relates to patient selection outcomes. Methods: The model validation and consequent patient selection process was simulated within empirical head and neck cancer patient data. By manipulating performance measures independently via model perturbations, the relation between model performance and patient selection was studied. Results: Small reductions in AUC (-0.02) yielded mean changes in ΔNTCP between 0.9–3.2 %, and single-model patient selection differences between 2–19 %. Deviations (-0.2 or +0.2) in CITL or CS yielded mean changes in ΔNTCP between 0.3–1.4 %, and single-model patient selection differences between 1–10 %. Conclusions: Each measure independently impacts ΔNTCP and patient selection and should thus be assessed in a representative sufficiently large external sample. Our suggested practical model selection approach is considering the model with the highest AUC, and recalibrating it if needed

Word-level loss extensions for neural temporal relation classification

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A Survey on Temporal Reasoning for Temporal Information Extraction from Text

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Temporal information extraction by predicting relative time-lines

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