A BIGRU-Based Stacked Attention Network for Biomedical Named Entity Recognition with Chinese EMRs.

Biomedical named entity recognition (BNER) is an effective method to structure the medical text data. It is an important basic task for building the medical application services such as the medical knowledge graphs and the intelligent auxiliary diagnosis systems. Existing medical named entity recognition methods generally leverage the word embedding model to construct text representation, and then integrate multiple semantic understanding models to enhance the semantic understanding ability of the model to achieve high-performance entity recognition. However, in the medical field, there are many professional terms that rarely appear in the general field, which cannot be represented well by the general domain word embedding model. Second, existing approaches typically only focus on the extraction of global semantic features, which generate a loss of local semantic features between characters. Moreover, as the word embedding dimension becomes much higher, the standard single-layer structure fails to fully and deeply extract the global semantic features. We put forward the BIGRU-based Stacked Attention Network (BSAN) model for biomedical named entity recognition. Firstly, we use the large-scale real-world medical electronic medical record (EMR) data to fine-tune BERT to build the proprietary embedding representations of the medical terms. Second, we use the Convolutional Neural Network model to extract semantic features. Finally, a stacked BIGRU is constructed using a multi-layer structure and a novel stacking method. It not only enables comprehensive and in-depth extraction of global semantic features, but also requires less time. Experimentally validated on the real-world datasets in Chinese EMRs, the proposed BSAN model achieves 90.9% performance on F1-values, which is stronger than the BNER performance of other state-of-the-art models.

[Effects of different drying methods on ginsenosides based on UHPLC-Q-Exactive Orbitrap MS technique].

The present study quickly identified the ginsenosides in fresh Panax ginseng and specified the effects of different drying methods(50 ℃-drying, 80 ℃-drying, and-70 ℃ freeze-drying) on ginsenosides.Three P.ginseng products by different drying methods were prepared, and the UHPLC-Q-Exactive Orbitrap high-resolution liquid mass spectrometry(MS) technique was applied to perform gradient elution using water-acetonitrile as the mobile phase, and the data collected in the negative ion mode were analyzed using X Calibur 2.2.The results showed that 57 saponins were identified from fresh P.ginseng.As revealed by the comparison with the fresh P.ginseng, in terms of the loss of ginsenosides, the dried products were ranked as the dried product at 50 ℃, freeze-dried products at-70 ℃, and the dried product at 80 ℃ in the ascending order.This study elucidated the effects of different drying methods on the types and relative content of ginsenosides, which can provide references for the processing of P.ginseng in the producing areas.

Diagnostic accuracy of red blood cell distribution width for neonatal sepsis.

INTRODUCTION: Red blood cell distribution width (RDW) is a biomarker for the diagnosis and prognosis of many diseases. However, the relevance between RDW and neonatal sepsis (NS) have not reached a consensus yet; the perform of RDW in the diagnosis of neonatal sepsis is still not clear. The aim of this meta-analysis was to estimate the significance of RDW in neonatal sepsis and the perform of RDW in diagnosis of neonatal sepsis. EVIDENCE ACQUISITION: We used Pubmed, Embase, Web of science, CNKI and Google academic database to find all articles that met the inclusion criteria until July 1, 2020. EVIDENCE SYNTHESIS: Fifteen eligible studies involving 1362 newborns were included in the meta-analysis after two independent investigators read the title, abstract and full text in detail. The pooled result of this meta-analysis showed that RDW was significantly higher in the NS group than in the control group (WMD=3.224; 95%CI: 2.359-4.090, P<0.001). In addition, the overall pooled sensitivity, specificity, PLR, NLR and DOR were 0.88 (95%CI:0.66-0.96), 0.90 (95%CI:0.65-0.98), 9.2 (95%CI:2.1-40.3), 0.14(95%CI:0.04-0.43) and 66.9 (95%CI:8.73-513.26), respectively. The area under the SROC curve (AUC) was 0.95 (95%CI:0.93-0.96). CONCLUSIONS: The meta-analysis demonstrated that newborns with sepsis had an elevated RDW level than healthy controls. RDW levels have significant correlated with neonatal sepsis; and RDW can be used as a cheap and satisfactory diagnostic biomarker for neonatal sepsis with a relatively high performance.