Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report

Close relationship between superconductivity and the bosonic mode in Ba0.6K0.4Fe2As2 and Na(Fe0.975Co0.025)As

Since the discovery of high temperature superconductivity in the iron pnictides and chalcogenides in early 2008, a central issue has been the microscopic origin of the superconducting pairing. Although previous experiments suggest that the pairing may be induced by exchanging the antiferromagnetic spin fluctuations and the superconducting order parameter has opposite signs in the electron and hole pockets as predicted by the S+- pairing model, it remains unclear whether there is a bosonic mode from the tunneling spectrum which has a close and universal relationship with superconductivity as well as the spin excitation. In this paper, based on the measurements of scanning tunneling spectroscopy, we show the clear evidence of a bosonic mode with the energy identical to that of the neutron spin resonance in two completely different systems Ba0.6K0.4Fe2As2 and Na(Fe0.975Co0.025)As with different superconducting transition temperatures. In both samples, the superconducting coherence peaks and the mode feature vanish simultaneously inside the vortex core or above Tc, indicating a close relationship between superconductivity and the bosonic mode. Our data also demonstrate a universal ratio between the mode energy and superconducting transition temperature, that is [mode energy]/kBTc ~ 4.3, which underlines the unconventional mechanism of superconductivity in the iron pnictide superconductors.Comment: 13 pages, 10 figure

Electronic specific heat in BaFe2−x_{2-x}Nix_xAs2_2

We have systematically studied the low-temperature specific heat of the BaFe$_{2-x}$Ni$_x$As$_2$ single crystals covering the whole superconducting dome. Using the nonsuperconducting heavily overdoped x = 0.3 sample as a reference for the phonon contribution to the specific heat, we find that the normal-state electronic specific heats in the superconducting samples may have a nonlinear temperature dependence, which challenges previous results in the electron-doped Ba-122 iron-based superconductors. A model based on the presence of ferromagnetic spin fluctuations may explain the data between x = 0.1 and x = 0.15, suggesting the important role of Fermi-surface topology in understanding the normal-state electronic states.Comment: 7 pages, 5 figure