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Crowdsourcing successfully strives to become a widely used means of collecting large-scale scientific corpora. Many research fields, including Information Retrieval, rely on this novel way of data acquisition. However, it seems to be undermined by a significant share of workers that are primarily interested in producing quick generic answers rather than correct ones in order to optimise their time-efficiency and, in turn, earn more money. Recently, we have seen numerous sophisticated schemes of identifying such workers. Those, however, often require additional resources or introduce artificial limitations to the task. In this work, we take a different approach by investigating means of a priori making crowdsourced tasks more resistant against cheaters.

Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. One important reason is that the amount of postoperative data is too massive and too high-dimensional to be effectively processed by the human operator. We therefore sought to develop a deep-learning-based algorithm that is able to predict postoperative AKI prior to the onset of symptoms and complications. Based on 96 routinely collected parameters we built a recurrent neural network (RNN) for real-time prediction of AKI after cardiothoracic surgery. From the data of 15,564 admissions we constructed a balanced training set (2224 admissions) for the development of the RNN. The model was then evaluated on an independent test set (350 admissions) and yielded an area under curve (AUC) (95% confidence interval) of 0.893 (0.862–0.924). We compared the performance of our model against that of experienced clinicians. The RNN significantly outperformed clinicians (AUC = 0.901 vs. 0.745, p < 0.001) and was overall well calibrated. This was not the case for the physicians, who systematically underestimated the risk (p < 0.001). In conclusion, the RNN was superior to physicians in the prediction of AKI after cardiothoracic surgery. It could potentially be integrated into hospitals’ electronic health records for real-time patient monitoring and may help to detect early AKI and hence modify the treatment in perioperative care.

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem.We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e., linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned.Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing stateof-the-art methods.

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A cute stroke care changed dramatically in 2015 with the publication of several randomized control trials demonstrating that endovascular therapy is more effective than alteplase alone for large vessel occlusion (LVO) stroke (1-7). Furthermore, the endovascular therapy treatment effect is profoundly time dependent, and every minute that we work faster to achieve vessel recanalization we can provide the gift of a week of disability-free life to patients (8). LVO stroke is a medical diagnosis that cannot be missed and must be made quickly. One of the commonly used methods to confirm or exclude the presence of a LVO quickly is with CT angiography, a 3-minute examination that can easily be performed following the noncontrast head CT that is standard of care for all acute stroke imaging (9-11). Multiphase CT angiography is a protocol recently introduced for acute stroke imaging that aims to both improve LVO detection and improve patient selection for endovascular therapy (12-14). Recent advances in deep learning, a class of machine learning, inspired new research on the uses of convolutional neural networks to perform at high levels in computer vision problems (15). These architectures hold great promise for enhancing the workflow in radiology. Specifically, in the neuroradiology domain, deep learning has been used to segment microhemorrhages at MRI with a sensitivity greater than 93% (16), to automate identification and to segment ischemic brain lesions on diffusion-weighted MRI scans (17), to detect early ischemic infarct at noncontrast CT with precision similar to diffusion-weighted MRI (18), and to detect intracranial hemorrhage at CT by reducing the time to diagnosis by 96% (19).