293 research outputs found
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
Study on the application of oligomers in paper reinforcement protection
In order to improve the strength of paper, solve the problem of poor permeability of traditional resins to paper, the low molecular weight hexamethylenediisocyanate (HDI) trimer was investigated, and the chemical and physical properties of paper samples were tested in this work. Results showed the paper treated by HDI trimer had good mechanical property, the tensile strength was increased from 1105 to 4151 N/m, the folding endurance was increased from 20.8 to 275; and had good glossiness and brightness. Therefore, the prepared HDI trimer has great application prospects in the protection of paper
RNA editing of nuclear transcripts in Arabidopsis thaliana
<p>Abstract</p> <p>Background</p> <p>RNA editing is a transcript-based layer of gene regulation. To date, no systemic study on RNA editing of plant nuclear genes has been reported. Here, a transcriptome-wide search for editing sites in nuclear transcripts of Arabidopsis (<it>Arabidopsis thaliana</it>) was performed.</p> <p>Results</p> <p>MPSS (massively parallel signature sequencing) and PARE (parallel analysis of RNA ends) data retrieved from public databases were utilized, focusing on one-base-conversion editing. Besides cytidine (C)-to-uridine (U) editing in mitochondrial transcripts, many nuclear transcripts were found to be diversely edited. Interestingly, a sizable portion of these nuclear genes are involved in chloroplast- or mitochondrion-related functions, and many editing events are tissue-specific. Some editing sites, such as adenosine (A)-to-U editing loci, were found to be surrounded by peculiar elements. The editing events of some nuclear transcripts are highly enriched surrounding the borders between coding sequences (CDSs) and 3′ untranslated regions (UTRs), suggesting site-specific editing. Furthermore, RNA editing is potentially implicated in new start or stop codon generation, and may affect alternative splicing of certain protein-coding transcripts. RNA editing in the precursor microRNAs (pre-miRNAs) of <it>ath-miR854</it> family, resulting in secondary structure transformation, implies its potential role in microRNA (miRNA) maturation.</p> <p>Conclusions</p> <p>To our knowledge, the results provide the first global view of RNA editing in plant nuclear transcripts.</p
Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing
Current natural language processing (NLP) models such as BERT and RoBERTa
have achieved high overall performance, but they often make systematic errors
due to bias or certain difficult features to learn. Thus research on slice
detection models (SDM) which automatically identifies underperforming groups of
datapoints has gradually caught more attention, which aims at both
understanding model behaviors and providing insights for future model training
and designing. However, there is little systematic research on SDM and
quantitative evaluation of its assessment for NLP models. Our paper fills this
gap by proposing "Discover, Explanation, Improvement" framework that discovers
coherent and underperforming groups of datapoints and unites datapoints of each
slice under human-understandable concepts; it also provides comprehensive
evaluation tasks and the corresponding quantitative metrics, which enable
convenient comparison for future works. Results show that our framework can
accurately select error-prone datapoints with informative semantic features
that summarize error patterns, based on which it directly boosts model
performance by an average of 2.85 points based on trained models without tuning
any parameters across multiple datasets.Comment: 15 pages, 5 figure
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