Automated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical Records.

Off-label use of a drug occurs when it is used in a manner that deviates from its FDA label. Studies estimate that 21% of prescriptions are off-label, with only 27% of those uses supported by evidence of safety and efficacy. We have developed methods to detect population level off-label usage using computationally efficient annotation of free text from clinical notes to generate features encoding empirical information about drug-disease mentions. By including additional features encoding prior knowledge about drugs, diseases, and known usage, we trained a highly accurate predictive model that was used to detect novel candidate off-label usages in a very large clinical corpus. We show that the candidate uses are plausible and can be prioritized for further analysis in terms of safety and efficacy

Free Entropy Dimension in Amalgamated Free Products

We calculate the microstates free entropy dimension of natural generators in an amalgamated free product of certain von Neumann algebras, with amalgamation over a hyperfinite subalgebra. In particular, some `exotic' Popa algebra generators of free group factors are shown to have the expected free entropy dimension. We also show that microstates and non--microstates free entropy dimension agree for generating sets of many groups. In the appendix by Wolfgang Lueck, the first L^2-Betti number for certain amalgamated free products of groups is calculated.Comment: The second revised version significantly generalized the main result of the original one, (see the abstract) and contains a new appendix by Wolfgang Lueck. The third and fourth revisions correct some minor mistakes. The fifth version adds a result about embeddability of amalgmated free product

Shirking, Monitoring, and Risk Aversion

This paper studies the effect of risk aversion on effort under different monitoring schemes. It uses a theoretical model which relaxes the assumption of agents being risk neutral, and investigates changes of effort as monitoring varies. The predictions of the theoretical model are tested using an original experimental setting where the level of risk aversion is measured and monitoring rates vary exogenously. Our results show that shirking decreases with risk aversion, being female, and monitoring. Moreover, monitoring is more effective to curtail shirking behaviors for subjects who are less risk averse, although the size of the impact is rather small

Building an IT Taxonomy with Co-occurrence Analysis, Hierarchical Clustering, and Multidimensional Scaling

Different information technologies (ITs) are related in complex ways. How can the relationships among a large number of ITs be described and analyzed in a representative, dynamic, and scalable way? In this study, we employed co-occurrence analysis to explore the relationships among 50 information technologies discussed in six magazines over ten years (1998-2007). Using hierarchical clustering and multidimensional scaling, we have found that the similarities of the technologies can be depicted in hierarchies and two-dimensional plots, and that similar technologies can be classified into meaningful categories. The results imply reasonable validity of our approach for understanding technology relationships and building an IT taxonomy. The methodology that we offer not only helps IT practitioners and researchers make sense of numerous technologies in the iField but also bridges two related but thus far largely separate research streams in iSchools - information management and IT management

Improving Palliative Care with Deep Learning

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.Comment: IEEE International Conference on Bioinformatics and Biomedicine 201

When chiral photons meet chiral fermions - Photoinduced anomalous Hall effects in Weyl semimetals

The Weyl semimetal is characterized by three-dimensional linear band touching points called Weyl nodes. These nodes come in pairs with opposite chiralities. We show that the coupling of circularly polarized photons with these chiral electrons generates a Hall conductivity without any applied magnetic field in the plane orthogonal to the light propagation. This phenomenon comes about because with all three Pauli matrices exhausted to form the three-dimensional linear dispersion, the Weyl nodes cannot be gapped. Rather, the net influence of chiral photons is to shift the positions of the Weyl nodes. Interestingly, the momentum shift is tightly correlated with the chirality of the node to produce a net anomalous Hall signal. Application of our proposal to the recently discovered TaAs family of Weyl semimetals leads to an order-of-magnitude estimate of the photoinduced Hall conductivity which is within the experimentally accessible range.Comment: 9 pages, 4 figure