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These studies utilized a prospective cohort design with an average sample size of 531. CONCLUSION The majority of literature describing the use of ML in clinical medicine is retrospective in nature and often outlines proof-of-concept approaches to impact patient care. We postulate that identifying and overcoming key translational barriers, including real-time access to clinical data, data security, physician approval of "black box" generated results, and performance evaluation will allow for a fundamental shift in medical practice, where specialized tools will aid the healthcare team in providing better patient care. BACKGROUND AND OBJECTIVE The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires (1) a manual examination of the ultrasound image for the localization of a region of interest (ROI), a fast and useful operation when only a small number of images need to be measured; and (2) an automatic delineation of the CIM region within the ROI. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. In this work, we propose a fully automatic single-step approach based on semantic segmentation that allows us to segment the plaque and to estimate the CIMT in a fast and useful manner for large data sets of images. METHODS Our single-step approach is based on densely connected convolutional neural networks (DenseNets) for semantic segmentation of the whole image. It has two remarka Bulb, respectively. To test the generalization power, the method has also been tested with another data set (NEFRONA) that includes images acquired with different equipment. CONCLUSIONS The validation carried out demonstrates that the proposed method is accurate and objective for both plaque detection and CIMT measurement. Moreover, the robustness and generalization capacity of the method have been proven with two different data sets. As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents. By adopting a joint modeling manner of topics and events, our model is able to produce more meaningful and event-indicative words compare to prior topic models. In addition, we introduce a language model embeddings to capture context-dependent features. Experimental results show that our approach outperforms various baselines in a commonly used multi-level event extraction corpus. OBJECTIVE Electronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify "right patient", "right drug", "right dose", "right route", and "right time" from doctor order information. METHODS We propose a fusion framework to extract typical treatment patterns based on multi-view similarity Network Fusion (SNF) method. The multi-view SNF method involves three similarity measures content-view similarity, sequence-view similarity and duration-view similarity. check details An EMR dataset and two metrics were utilized to evaluate the performance and to extract typical treatment patterns. RESULTS Experimental results on a real-world EMR dataset show that the multi-view similarity network fusion method outperforms all the single-view similarity measures and also outperforms the existing similarity measure methods. Furthermore, we extract and visualize typical treatment patterns by clustering analysis. CONCLUSION The extracted typical treatment patterns by combining doctor order content, sequence, and duration views can provide data-driven guidelines for artificial intelligence in medicine and help clinicians make better decisions in clinical practice. Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual DCE-MRI examination can be difficult and error-prone. The early stage of breast tissue segmentation, in a typical CAD, is crucial to increase reliability and reduce the computational effort by reducing the number of voxels to analyze and removing foreign tissues and air. In recent years, the deep convolutional neural networks (CNNs) enabled a sensible improvement in many visual tasks automation, such as image classification and object recognition. These advances also involved radiomics, enabling high-throughput extraction of quantitative features, resulting in a strong improvement in automatic diagnosis through medical imaging. However, machine learning and, in particular, deep learning approaches are gaining popularity in the radiomics field for tissue segmentation. This work aims to accurately segment breast parenchyma from the air and other tissues (such as chest-wall) by applying an ensemble of deep CNNs on 3D MR data. The novelty, besides applying cutting-edge techniques in the radiomics field, is a multi-planar combination of U-Net CNNs by a suitable projection-fusing approach, enabling multi-protocol applications. The proposed approach has been validated over two different datasets for a total of 109 DCE-MRI studies with histopathologically proven lesions and two different acquisition protocols. The median dice similarity index for both the datasets is 96.60 % (±0.30 %) and 95.78 % (±0.51 %) respectively with p  less then  0.05, and 100% of neoplastic lesion coverage.