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Objective In this paper, we focus on three aspects: (1) to annotate a set of standard corpus in Chinese discharge summaries; (2) to perform word segmentation and named entity recognition in the above corpus; (3) to build a joint model that performs word segmentation and named entity recognition. Design Two independent systems of word segmentation and named entity recognition were built based on conditional random field models. In the field of natural language processing, while most approaches use a single model to predict outputs, many works have proved that performance of many tasks can be improved by exploiting combined techniques. Therefore, in this paper, we proposed a joint model using dual decomposition to perform both the two tasks in order to exploit correlations between the two tasks. Three sets of features were designed to demonstrate the advantage of the joint model we proposed, compared with independent models, incremental models and a joint model trained on combined labels. Measurements Micro-averaged precision (P), recall (R), and F-measure (F) were used to evaluate results. Results The gold standard corpus is created using 336 Chinese discharge summaries of 71 355 words. The framework using dual decomposition achieved 0.2% improvement for segmentation and 1% improvement for recognition, compared with each of the two tasks alone. Conclusions The joint model is efficient and effective in both segmentation and recognition compared with the two individual tasks. The model achieved encouraging results, demonstrating the feasibility of the two tasks. [ABSTRACT FROM AUTHOR]

Classic tissue engineering paradigms are limited by the incorporation of a functional vasculature and a reliable means for reimplantation into the host circulation. We have developed a novel approach to overcome these obstacles using autologous explanted microcirculatory beds (EMBs) as bioscaffolds for engineering complex three-dimensional constructs. In this study, EMBs consisting of an afferent artery, capillary beds, efferent vein, and surrounding parenchymal tissue are explanted and maintained for 24 h ex vivo in a bioreactor that preserves EMB viability and function. Given the rapidly advancing field of stem cell biology, EMBs were subsequently seeded with three distinct stem cell populations, multipotent adult progenitor cells (MAPCs), and bone marrow and adipose tissue-derived mesenchymal stem cells (MSCs). We demonstrate MAPCs, as well as MSCs, are able to egress from the microcirculation into the parenchymal space, forming proliferative clusters. Likewise, human adipose tissue-derived MSCs were also found to egress from the vasculature and seed into the EMBs, suggesting feasibility of this technology for clinical applications. We further demonstrate that MSCs can be transfected to express a luciferase protein and continue to remain viable and maintain luciferase expression in vivo. By using the vascular network of EMBs, EMBs can be perfused ex vivo and seeded with stem cells, which can potentially be directed to differentiate into neo-organs or transfected to replace failing organs and deficient proteins. [ABSTRACT FROM AUTHOR]

The article discusses the case of a 54-year-old woman with a thyroid nodule which was found out to be a Zenker's diverticulum. The ultrasonography performed to the patient demonstrates a hypoechoic nodule in the posterior part of the left thyroid lobe. The pathology reveals a thyroid tissue with lymphocytic infiltrate and vegetative material.

*: Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building medical text mining applications. To achieve this, we establish a mapping system consisting of a Wikipedia-based scoring algorithm and a named entity normalization method (NEN). The mapping system makes full use of information available on Wikipedia, which is a comprehensive Internet medical knowledge base. We also built a new ontology, Tree of Human Body Parts (THBP), from core anatomical parts by referring to anatomical experts and Unified Medical Language Systems (UMLS) to make the mapping system efficacious for clinical treatments. *: Result The gold standard is derived from 50 discharge summaries from our previous work, in which 2,224 anatomical entities are included. The F1-measure of the baseline system is 70.20%, while our algorithm based on Wikipedia achieves 86.67% with the assistance of NEN. *: Conclusions We construct a framework to map anatomical entities to THBP ontology using normalization and a scoring algorithm based on Wikipedia. The proposed framework is proven to be much more effective and efficient than the main baseline system.

Objectives: To determine inter-lab reliability in sleep stage scoring using the 2014 American Academy of Sleep Medicine (AASM) manual. To understand in-depth reasons for disagreement and provide suggestions for improvement., Methods: This study consisted of 40 all-night polysomnographys (PSGs) from different samples. PSGs were segmented into 37,642 30-s epochs. Five doctors from China and two doctors from America scored the epochs following the 2014 AASM standard. Scoring disagreement between two centers was evaluated using Cohen's kappa (κ). After visual inspection of PSGs of deviating scorings, potential disagreement reasons were analyzed., Results: Inter-lab reliability yielded a substantial degree (κ = 0.75 ± 0.01). Scoring for stage W (κ = 0.89) and R (κ = 0.87) achieved the highest agreement, while stage N1 (κ = 0.45) reflected the lowest. Considering the relative disagreement ratio, N2-N3 (22.09%), W-N1 (19.68%), and N1-N2 (18.75%) were the most frequent combinations of discrepancy. American and Chinese doctors showed certain characteristics in the scoring of discrepancy combination W-N1, N1-N2, and N2-N3. There are seven reasons for disagreement, namely "on-threshold characteristic" (29.21%), "context influence" (18.06%), "characteristic identification difficulty" (8.81%), "arousal-wake confusion" (7.57%), "derivation inconsistence" (2.15%), "on-borderline characteristic" (0.92%), and "misrecognition" (33.27%)., Conclusions: This study demonstrated the sleep stage scoring agreement of the 2014 AASM manual and explored potential sources of labeling ambiguity. Improvement measures were suggested accordingly to help remove ambiguity for scorers and improve scoring reliability at the international level.

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wrist actigraphy derived from a wearable device., Methods: The proposed method consists of two phases: multi-level feature learning and recurrent neural networks-based (RNNs) classification. The feature learning phase is designed to extract low- and mid-level features. Low-level features are extracted from raw signals, capturing temporal and frequency domain properties. Mid-level features are explored based on low-level ones to learn compositions and structural information of signals. Sleep staging is a sequential problem with long-term dependencies. RNNs with bidirectional long short-term memory architectures are employed to learn temporally sequential patterns., Results: To better simulate the use of wearable devices in the daily scene, experiments were conducted with a resting group in which sleep was recorded in the resting state, and a comprehensive group in which both resting sleep and non-resting sleep were included. The proposed algorithm classified five sleep stages (wake, non-rapid eye movement 1-3, and rapid eye movement) and achieved weighted precision, recall, and F 1 score of 66.6%, 67.7%, and 64.0% in the resting group and 64.5%, 65.0%, and 60.5% in the comprehensive group using leave-one-out cross-validation. Various comparison experiments demonstrated the effectiveness of the algorithm., Conclusions: Our method is efficient and effective in scoring sleep stages. It is suitable to be applied to wearable devices for monitoring sleep at home., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Reconstruction after surgical treatment of head and neck cancers can be challenging. Goals for reconstruction include restoration of appearance as well as function when appropriate. Commonly encountered sites requiring reconstruction include the soft tissues of the face (including the critical areas of the eyes, ears, nose, and lips), scalp, tongue and oral cavity, maxilla, mandible, and pharynx. Advanced reconstructive techniques using microsurgery may be preferable to simpler techniques to obtain optimal outcomes. In this article, techniques for reconstruction in these areas as well as anticipated outcomes are discussed., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The task of microscopy cell detection is of great biological and clinical importance. However, existing algorithms for microscopy cell detection usually ignore the large variations of cells and only focus on the shape feature/descriptor design. Here we propose a new two-layer model for cell centre detection by a two-layer structure prediction framework, which is respectively built on classification for the cell centres implicitly using rich appearances and contextual information and explicit structural information for the cells. Experimental results demonstrate the efficiency and effectiveness of the proposed method over competing state-of-the-art methods, providing a viable alternative for microscopy cell detection., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Objective: To develop a system to extract follow-up information from radiology reports. The method may be used as a component in a system which automatically generates follow-up information in a timely fashion., Methods: A novel method of combining an LSP (labeled sequential pattern) classifier with a CRF (conditional random field) recognizer was devised. The LSP classifier filters out irrelevant sentences, while the CRF recognizer extracts follow-up and time phrases from candidate sentences presented by the LSP classifier., Measurements: The standard performance metrics of precision (P), recall (R), and F measure (F) in the exact and inexact matching settings were used for evaluation., Results: Four experiments conducted using 20,000 radiology reports showed that the CRF recognizer achieved high performance without time-consuming feature engineering and that the LSP classifier further improved the performance of the CRF recognizer. The performance of the current system is P=0.90, R=0.86, F=0.88 in the exact matching setting and P=0.98, R=0.93, F=0.95 in the inexact matching setting., Conclusion: The experiments demonstrate that the system performs far better than a baseline rule-based system and is worth considering for deployment trials in an alert generation system. The LSP classifier successfully compensated for the inherent weakness of CRF, that is, its inability to use global information.