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COMPARISON BETWEEN MANUAL AND automatic coding OF MEDICAL RECORD STATISTICAL CARDS AT A UNIVERSITY HOSPITAL

MEDICAL INFORMATICS 1993年第1期18卷 53-59页

作者： ROSSI, CR ALBERTI, V MANCINO, G FLOR, L MARTELLO, T POETA, L LISE, M DIANA, L PUPPINI, G Institute of Clinical Surgery II Padua University Hospital Italy.

The coding error rate of systems for medical record statistical cards (MRSCs) throughout health services is about 30%. A program using automatic coding has been developed at the Institute of Clinical Surgery II, Padua University Hospital, with a view to reducing this percentage. Out of an overall sample of 4776 MRSCs from all departments of the hospital, 54 were automatically coded at our institute. Categories of discrepancy between the discharge diagnosis codes of the 4722 manually coded MRSCs and the other 54 MRSCs were classified as follows: types I-III, diagnosis assigned to an erroneous under-class, class or heading (ICD-9) respectively;type IV, incorrect diagnosis formulation precluding code assignment;type V, two or more discrepancies on MRSC;and type VI, secondary diagnosis not coded. Discrepancy rates were as follows: 22.3% and 0.0% for type 1;21.3% and 0.0% for type II;17.6% and 0.0% for type III;1.9% and 0.0% for type IV;5.8% and 0.0% for type V;31% and 1.9% for type VI. Code discrepancy rates for surgical procedures, which were also compared, ranged from 7.0 to 12.5% for manual coding, while no discrepancy was observed in automatically-coded MRSCs. The results clearly demonstrate the utility of the system reported on, and it is suggested that it should be used in a modified form in other hospital departments.

关键词： automatic coding MEDICAL RECORD STATISTICAL CARD HOSPITAL DATA QUALITY COMPUTERIZED MEDICAL ARCHIVES

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automatic Classification and coding of Prefabricated Components Using IFC and the Random Forest Algorithm

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BUILDINGS 2022年第5期12卷 688页

作者： Xu, Zhao Xie, Zheng Wang, Xuerong Niu, Mi Southeast Univ Dept Civil Engn Nanjing 210000 Peoples R China Southeast Univ Dept Software Engn Suzhou 215000 Peoples R China Nanjing Audit Univ Sch Informat Engn Nanjing 210000 Peoples R China

The management of prefabricated component staging and turnover lacks the effective integration of informatization and complexity, as relevant information is stored in the heterogeneous systems of various stakeholders. BIM and its underlying data schema, IFC, provide for information collaboration and sharing. In this paper, an automatic classification and coding system for prefabricated building, based on BIM technology and Random Forest, is developed so as to enable the unique representation of components. The proposed approach starts with classifying and coding information regarding the overall design of the components. With the classification criteria, the required attributes of the components are extracted, and the process of attribute extraction is illustrated in detail using wall components as an example. The Random Forest model is then employed for IFC building component classification training and testing, which includes the selection of the datasets, the construction of CART, and the voting of the component classification results. The experiment results illustrate that the approach can automate the uniform and unique coding of each component on a Python basis, while also reducing the workload of designers. Finally, based on the IFC physical file, an extended implementation process for component encoding information is designed to achieve information integrity for prefabricated component descriptions. Additionally, in the subsequent research, it can be further combined with Internet-of-Things technology to achieve the real-time collection of construction process information and the real-time control of building components.

关键词： prefabrication IFC Random Forest automatic coding information classification

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automatic RadLex coding of Chinese structured radiology reports based on text similarity ensemble

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2021年第SUPPL 9期21卷 247-247页

作者： Chen, Yani Nan, Shan Tian, Qi Cai, Hailing Duan, Huilong Lu, Xudong Zhejiang Univ Coll Biomed Engn & Instrument Sci Zheda Rd Hangzhou 310027 Peoples R China Minist Educ Key Lab Biomed Engn Hangzhou Peoples R China Eindhoven Univ Technol Sch Ind Engn Eindhoven Netherlands Hainan Univ Sch Biomed Engn Haikou Hainan Peoples R China

Background: Standardized coding of plays an important role in radiology reports' secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research. Methods: We present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese-English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms. Results: The results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% a

关键词： automatic coding Hybrid translation Text similarity ensemble Standardized radiology reports

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Evaluation of automatic Collaborative Learning Process coding Using Deep Learning Methods Based on Multi-Dimensional coding Scheme

Evaluation of Automatic Collaborative Learning Process Codin...

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IEEE International Conference on Teaching, Assessment, and Learning for Engineering (IEEE TALE) - Engineering Next-Generation Learning

作者： Shibata, Chihiro Ando, Kimihiko Inaba, Taketoshi Tokyo Univ Technol Grad Sch Bion Comp & Media Sci Tokyo Japan Tokyo Univ Technol Cloud Serv Ctr Tokyo Japan

ISBN: (纸本)9781538665220

In computer-supported collaborative learning research, it is important to determine the guidelines for appropriate scaffolding by extracting indicators for distinguishing groups with poor progress in a collaborative process by analyzing the mechanism of interactive activation. coding and statistical analysis are often adopted for analyzing such collaborative processes. In this study, using a multi-dimensional coding scheme with four dimensions designed to analyze the collaborative learning process more comprehensively and multilaterally, automatic coding is performed using deep learning methods and its accuracy is verified. In addition, this method is used to predict another dataset and determine its validity. The experiments of this study show that except for some typical misclassifications, the overall results were positive. However, in order to solve this misclassification problem, we also have to reconsider the redesign of the coding scheme and reexamine the classification technique by deep learning.

关键词： computer-supported collaborative learning coding scheme deep learning methods automatic coding

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automatic SNOMED CT coding of Chinese clinical terms via attention-based semantic matching

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2022年第0期159卷 104676-104676页

作者： Chen, Yani Hu, Danqing Li, Mengyang Duan, Huilong Lu, Xudong Zhejiang Univ Coll Biomed Engn & Instrument Sci Zheda Rd Hanghzou 310027 Peoples R China

Background: A considerable amount of meaningful information is routinely recorded in Chinese clinical data in text format, referred to as Chinese clinical terms. The lack of coding is a major difficulty hindering the application of clinical terms. SNOMED CT is a widely used and comprehensive clinical health care terminology collection because of its coverage, granularity, clinical orientation, and logical underpinning. It is useful and efficient for automatically assigning SNOMED CT codes to Chinese clinical terms, but it still faces several problems. Current cross-language clinical term matching studies rely on external resources, such as machine translation and rule-based methods. Semantic matching methods have achieved strong performance on text matching, but few studies have been done on cross-language clinical term matching. We present an effective attention-based semantic matching algorithm to automatically cross-language code Chinese clinical terms with SNOMED ***: Firstly, BERT was used to turn the input into word embedding. Then, the word embeddings were encoded through a BiLSTM with self-attention to focus on capturing distant relationships among words with different weights depending on their contribution to semantic matching. Then, decomposable attention was used to make semantic matching trivially parallelizable to speed up calculation. Finally, fully connected layers and a sigmoid were utilized to output matching ***: The 29,960 manually coded Chinese clinical terms, 30,040 unmatched Chinese clinical terms and SNOMED CT codes were collected to evaluate the proposed method. Compared with the existing semantic matching method, the proposed approach achieves state-of-the-art results demonstrating the effectiveness of the method with an accuracy of 0.905, a precision of 0.856, a recall of 0.518, and an F-measure of 0.645. The proposed Chinese-English bilingual term mapping, Chinese character-level and word-level encoder, English word-leve

关键词： automatic coding SNOMED CT Semantic matching Decomposble attention

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automatic ICD-10 coding: Deep semantic matching based on analogical reasoning

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HELIYON 2023年第4期9卷 e15570页

作者： Chen, Yani Chen, Han Lu, Xudong Duan, Huilong He, Shilin An, Jiye Zhejiang Univ Coll Biomed Engn & Instrument Sci Zheda Rd Hangzhou 310027 Zhejiang Peoples R China Hainan Hosp Chinese PLA Gen Hosp Dept Informat Haitang Bay Sanya 572013 Hainan Peoples R China Zhejiang Univ 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Hainan Hosp Chinese PLA Gen Hosp Haitang Bay Sanya 572013 Hainan Peoples R China

Background: ICD-10 has been widely used in statistical analysis of mortality rates and medical reimbursement. automatic ICD-10 coding is desperately needed because manually assigning codes is expensive, time-consuming, and labor-intensive. Diagnoses described in medical records differ significantly from those used in ICD-10 classification, making it impossible for existing automatic coding techniques to perform well enough to support medical billing, resource allocation, and research requirements. Meanwhile, most of the current automatic coding approaches are oriented toward English ICD-10. This method for automatically assigning ICD-10 codes to diagnoses extracted from Chinese discharge records was provided in this paper. Method: First, BERT creates word representations of the two texts. Second, the context representation layer incorporates contextual information into the representation of each time step of the word representations using a bidirectional Long Short-Term Memory. Third, the matching layer compares each contextual embedding of the uncoded diagnosis record against a weighted version of all contextual character embeddings of the manually coded diagnosis record. The matching strategy is element-wise subtraction and element-wise multiplication and then through a neural network layer. Fourth, the matching vectors are combined using a one-layer convolutional neural network. A sigmoid is then used to output matching results. Results: To evaluate the proposed method, 1,003,558 manually coded primary diagnoses were gathered from the homepage of the discharge medical records. The experimental results showed that the proposed method outperformed popular deep semantic matching algorithms, such as DSSM, ConvNet, ESIM, and ABCNN, and demonstrated state-of-the-art results in a single text matching with an accuracy of 0.986, a precision of 0.979, a recall of 0.983, and an F1-score of 0.981. Conclusion: The automatic ICD-10 coding of Chinese diagnoses is successful whe

关键词： automatic coding ICD-10 Semantic matching Analogical reasoning

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Automation of reversible steganographic coding with nonlinear discrete optimisation

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CONNECTION SCIENCE 2022年第1期34卷 1719-1735页

作者： Chang, Ching-Chun Univ Warwick Dept Comp Sci Coventry W Midlands England

Authentication mechanisms are at the forefront of defending the world from various types of cybercrime. Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management. Nevertheless, despite being generally imperceptible to human sensory systems, any degree of steganographic distortion might be inadmissible in fidelity-sensitive situations such as forensic science, legal proceedings, medical diagnosis and military reconnaissance. This has led to the development of reversible steganography. A fundamental element of reversible steganography is predictive analytics, for which powerful neural network models have been effectively deployed. Another core element is reversible steganographic coding. Contemporary coding is based primarily on heuristics, which offers a shortcut towards sufficient, but not necessarily optimal, capacity-distortion performance. While attempts have been made to realise automatic coding with neural networks, perfect reversibility is unattainable via such learning machinery. Instead of relying on heuristics and machine learning, we aim to derive optimal coding by means of mathematical optimisation. In this study, we formulate reversible steganographic coding as a nonlinear discrete optimisation problem with a logarithmic capacity constraint and a quadratic distortion objective. Linearisation techniques are developed to enable iterative mixed-integer linear programming. Experimental results validate the near-optimality of the proposed optimisation algorithm when benchmarked against a brute-force method.

关键词： automatic coding mathematical optimisation reversible steganography

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coding OF SPATIAL INFORMATION BY YOUNG AND ELDERLY ADULTS

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JOURNALS OF GERONTOLOGY 1982年第1期37卷 80-86页

作者： MCCORMACK, PD Dept. of Psychology Carleton Univ. OttawaOntario Canada KIS 5B6.

Two experiments are reported in which the presumed automatic nature of spatial coding is examined. In experiment 1, 48 young and 48 elderly female human adults, half of each group aware and half unaware of the task requirements, studied 16 cards with 4 nouns arranged vertically on each card. They were then given these 64 targets, as well as 64 distractors, and were required to sort the targets into 1 of 4 input spatial locations and to discard distractors. Awareness of the location requirement had no effect on performance and the 2 age groups were equivalent with respect to overall spatial memory accuracy. In a 2nd experiment, 40 young and 40 elderly unaware female adults were shown 64 cards with 1 noun per card in 1 of 4 spatial locations. There were no reliable age differences in location performance;the young adults, however, exhibited superior free recall.

关键词： Aging and memory automatic coding Fundamental attributes of memory Hasher-Zacks model Spatial coding

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Generative Pre-trained Transformers for coding Text Data? An Analysis with Classroom Orchestration Data 1

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18th European Conference on Technology Enhanced Learning (EC-TEL)

作者： Amarasinghe, Ishari Marques, Francielle Ortiz-Beltran, Ariel Hernandez-Leo, Davinia Univ Pompeu Fabra ICT Dept Barcelona Spain Radboud Univ Nijmegen Natl Educ Lab AI Nijmegen Netherlands

ISBN: (数字)9783031426827

ISBN: (纸本)9783031426810;9783031426827

Video content analysis is of importance for researchers in technology-enhanced learning. A common starting point typically involves transcribing video into textual transcripts that enable the application of a coding scheme to group the text into key themes. However, manual coding is demanding and requires time and effort of human annotators. Therefore, this study explores the possibility of using Generative Pre-trained Transformer 3 (GPT-3) models for automating the text data coding compared to baseline classicalmachine learning approaches using a dataset manually coded for the orchestration actions of six teachers in classroom collaborative learning sessions. The findings of our study showed that a fine-tuned GPT-3 (curie) model outperformed classical approaches (F1 score of 0.87) and reached a 0.77 Cohen's kappa, which indicated a moderate agreement between manual and machine coding. The study also brings out the limitations of our text transcripts and highlights the importance of multimodal observations that capture the context of orchestration actions.

关键词： Collaborative learning automatic coding Machine Learning Generative AI Classroom orchestration

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Commodity coding Research and Application of Ceramic E-commerce Platform

Commodity Coding Research and Application of Ceramic E-comme...

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3rd International Conference on Advanced Design and Manufacturing Engineering (ADME 2013)

作者： He, Fubao Zhang, Yilai Guo, Tongjun Chen, Hong Jingdezhen Ceram Inst Jingdezhen 333001 Jiangxi Peoples R China

ISBN: (纸本)9783037858431

This paper formulates the commodity coding rules based on the commodities classification of the existing ceramic E-commerce platform, and completes coding the existing ceramic commodities of the platform and automatic coding when add new commodity or modify commodity according to the rules. This can facilitate the unified management to the commodities of the ceramic E-commerce platform greatly, and can be well realize seamless connection with the enterprise management system related to the commodities of the informatization integrated service platform for ceramic enterprises, let the enterprise management system can connect both inside and outside freely and share resources with the e-commerce platform. Finally, uses the commodity code in commodity searching, supply the lack purchasing of ceramic kit, distribution management systems of the ceramic e-commerce platform and quick searching and statistics according to the classification in commodities of the background.

关键词： Commodity Code coding Rules automatic coding Ceramic E-commerce Platform

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