How can Deep Learning Retrieve the Write-Missing Additional Diagnosis from Chinese Electronic Medical Record For DRG

The purpose of write-missing diagnosis detection is to find diseases that have been clearly diagnosed from medical records but are missed in the discharge diagnosis. Unlike the definition of missed diagnosis, the write-missing diagnosis is clearly manifested in the medical record without further reasoning. The write-missing diagnosis is a common problem, often caused by physician negligence. The write-missing diagnosis will result in an incomplete diagnosis of medical records. While under DRG grouping, the write-missing diagnoses will miss important additional diagnoses (CC, MCC), thus affecting the correct rate of DRG enrollment. Under the circumstance that countries generally start to adopt DRG enrollment and payment, the problem of write-missing diagnosis is a common and serious problem. The current manual-based method is expensive due to the complex content of the full medical record. We think this problem is suitable to be solved as natural language processing. But to the best of our knowledge, no researchers have conducted research on this problem based on natural language processing methods. We propose a framework for solving the problem of write-missing diagnosis, which mainly includes three modules: disease recall module, disease context logic judgment module, and disease relationship comparison module. Through this framework, we verify that the problem of write-missing diagnosis can be solved well, and the results are interpretable. At the same time, we propose advanced solutions for the disease context logic judgment module and disease relationship comparison module, which have obvious advantages compared with the mainstream methods of the same type of problems. Finally, we verified the value of our proposed framework under DRG medical insurance payment in a tertiary hospital

Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization

The disease is a core concept in the medical field, and the task of normalizing disease names is the basis of all disease-related tasks. However, due to the multi-axis and multi-grain nature of disease names, incorrect information is often injected and harms the performance when using general text data augmentation techniques. To address the above problem, we propose a set of data augmentation techniques that work together as an augmented training task for disease normalization. Our data augmentation methods are based on both the clinical disease corpus and standard disease corpus derived from ICD-10 coding. Extensive experiments are conducted to show the effectiveness of our proposed methods. The results demonstrate that our methods can have up to 3\% performance gain compared to non-augmented counterparts, and they can work even better on smaller datasets

The development trend and prospect of automobile energy-saving standard system under the goal of peak carbon dioxide emissions

This paper conducts research on the development trend of automobile energy-saving standard system under China’s goal of peak carbon dioxide emissions by 2030. The research first sorted out the carbon dioxide emission standards and regulations of major automobile developed countries in the world, systematically analyzed the current status of China's automobile energy-saving standard system, and proposed the key problems at this stage. With the goal of peak carbon dioxide emissions as the core, the key tasks for the next phase of the construction of the automotive energy-saving standard system are proposed, including comprehensively promoting the formulation of fuel consumption standards for passenger cars and commercial vehicles from 2025 to 2030, and accelerating the construction of NEV energy-saving standard system

Holistic CNN Compression via Low-rank Decomposition with Knowledge Transfer

近日，国际顶级学术刊物《IEEE Transactions on Pattern Analysis and Machine Intelligence》（PAMI）接收了厦门大学信息科学与技术学院纪荣嵘团队的最新研究成果“Holistic CNN Compression via Low-rank Decomposition with Knowledge Transfer”。PAMI是计算机科学领域最顶级的国际期刊，其影响因子为 9.45。 该论文提出了一种统一的全局卷积神经网络压缩框架，简称为LRDKT，其目标在于统一加速与压缩卷积神经网络。该工作是厦门大学博士生林绍辉和导师纪荣嵘教授团队的阶段性研究成果，目前论文相关代码已开源。团队该方向的前期成果已经发表在AAAI/IJCAI等CCF-A类国际会议上。该论文由我校博士生林绍辉与其导师纪荣嵘教授（通讯作者）、硕士研究生陈超、悉尼大学陶大成教授、美国罗彻斯特大学罗杰波教授等合作完成，这也是我校研究生第二次在计算机领域的最顶级刊物上以第一作者身份发表论文，标志着我校信息学科研究生培养质量的突破。【Abstract】Convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks, which are extremely powerful to deal with massive training data by using tens of millions of parameters. However, CNNs often cost significant memory and computation consumption, which prohibits their usage in resource-limited environments such as mobile or embedded devices. To address the above issues, the existing approaches typically focus on either accelerating the convolutional layers or compressing the fully-connected layers separatedly, without pursuing a joint optimum. In this paper, we overcome such a limitation by introducing a holistic CNN compression framework, termed LRDKT, which works throughout both convolutional and fully-connected layers. First, a low-rank decomposition (LRD) scheme is proposed to remove redundancies across both convolutional kernels and fully-connected matrices, which has a novel closed-form solver to significantly improve the efficiency of the existing iterative optimization solvers. Second, a novel knowledge transfer (KT) based training scheme is introduced. To recover the accumulated accuracy loss and overcome the vanishing gradient, KT explicitly aligns outputs and intermediate responses from a teacher (original) network to its student (compressed) network. We have comprehensively analyzed and evaluated the compression and speedup ratios of the proposed model on MNIST and ILSVRC 2012 benchmarks. In both benchmarks, the proposed scheme has demonstrated superior performance gains over the state-of-the-art methods. We also demonstrate the proposed compression scheme for the task of transfer learning,including domain adaptation and object detection, which show exciting performance gains over the state-of-the-arts. Our source code and compressed models are available at https://github.com/ShaohuiLin/LRDKT.This work is supported by the National Key R&D Program (No.2017YFC0113000, No.2016YFB1001503), Natural Science Foundation of China (No.U1705262, No.61705262,No.61772443, No.61572410). 该项研究得到了国家重点研发专项（No.2017YFC0113000, and No.2016YFB1001503）、国家自然科学基金联合重点项目（No.U1705262）的资助