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The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. FKBP inhibitor In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the better the performance. The RNKN gradually reveals the interpretation of knowledge embeddings as the number of training epochs increases. In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities. OBJECTIVE Medical knowledge graph (KG) is attracting attention from both academic and healthcare industry due to its power in intelligent healthcare applications. In this paper, we introduce a systematic approach to build medical KG from electronic medical records (EMRs) with evaluation by both technical experiments and end to end application examples. MATERIALS AND METHODS The original data set contains 16,217,270 de-identified clinical visit data of 3,767,198 patients. The KG construction procedure includes 8 steps, which are data preparation, entity recognition, entity normalization, relation extraction, property calculation, graph cleaning, related-entity ranking, and graph embedding respectively. We propose a novel quadruplet structure to represent medical knowledge instead of the classical triplet in KG. A novel related-entity ranking function considering probability, specificity and reliability (PSR) is proposed. Besides, probabilistic translation on hyperplanes (PrTransH) algorithm is used to learn grlue. The reliability value can measure how reliable is the relationship between Si and Oij. The reason for the definition is the higher value of Nco(Si, Oij), the relationship is more reliable. However, the reliability values of the two relationships should not have a big difference if both of their co-occurrence numbers are very big. In our study, we finally set Ncomin = 10 and R = 1 after some experiments. For instance, if co-occurrence numbers of three relationships are 1, 100 and 10000, their reliability values are 1, 2.96 and 5 respectively. AIMS To systematically review the diagnostic value of the central vein sign (CVS) in multiple sclerosis (MS) and to meta-analyse the proportion of positive lesions for CVS needed to distinguish MS from non-MS mimics. MATERIALS AND METHODS A literature review was performed and a proportion meta-analysis was performed to examine the proportion of the CVS in MS lesions. Studies reporting a threshold of the CVS containing lesions with 100% diagnostic accuracy were included in the meta-analysis. This was compared to MS mimics in order to establish the discriminative value of the CVS. RESULTS The CVS was found to be viable at lower field strengths (3 T and 1.5 T) and automated analysis is currently less accurate than manual counting. Five studies were included for the proportional meta-analysis. From the analysis, a proportion of 45% of lesions having the CVS was suggested given that the findings that the weighted proportion was 46.4% (95% confidence interval [CI] of 40.3%-52.6%) with low heterogeneity (I2 = 0.0%; p=0.5). CONCLUSION Although the CVS is a clinically relevant and viable sign, further work is needed to integrate this into the existing diagnostic criteria. As manual determination is a time-consuming process, the development of automated methods will be beneficial. With improvements in computational imaging techniques, the CVS will have an important role in the diagnosis and differentiation of MS. BACKGROUND The aim of this study was to evaluate the accuracy of mitral regurgitation (MR) volume quantified on three-dimensional (3D) color Doppler transesophageal echocardiography (TEE) using new semiautomated software compared with conventional two-dimensional (2D) proximal isovelocity surface area (PISA) transthoracic echocardiography (TTE) and TEE and cardiac magnetic resonance imaging (CMR). METHODS Fifty-one patients (mean age, 63 ± 16 years; 35 men) prospectively underwent TTE, TEE, and CMR for MR evaluation. Regurgitant volume (RVol) by 3D MR flow quantification was compared with 2D TTE, TEE, and CMR, and the accuracy of evaluation of severe MR by 3D MR flow quantification was compared against guideline criteria by TEE. RESULTS Twenty-nine patients had severe MR, 16 had moderate MR, and six had mild MR. Three-dimensional MR flow quantification was feasible in all patients, including prolapse (n = 37), restriction (n = 9), functional MR (n = 5), and eccentric or multiple jects (n = 41). RVol on 3D MR flow quantification correlated well with RVol on 2D PISA TTE (interclass correlation coefficient [ICC] = 0.
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