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

Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions

Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem. To incorporate grid topology to the NN model, the proposed GNN-for-OPF framework innovatively exploits the locality property of locational marginal prices and voltage magnitude. Furthermore, we develop a physics-aware (ac-)flow feasibility regularization approach for general OPF learning. The advantages of our proposed designs include reduced model complexity, improved generalizability and feasibility guarantees. By providing the analytical understanding on the graph subspace stability under grid topology contingency, we show the proposed GNN can quickly adapt to varying grid topology by an efficient re-training strategy. Numerical tests on various test systems of different sizes have validated the prediction accuracy, improved flow feasibility, and topology adaptivity capability of our proposed GNN-based learning framework

What Is a Better Marketing Strategy for Live Streaming Broadcasters? A Topic Model of Social Interactions

Live streaming has spawned a new business model called live-streaming commerce (LSC). Interactive LSC features affect viewer purchasing behavior. This study empirically examines two types of social interactions in danmaku: transaction-oriented and relationship-oriented. Viewers in the first category focus on products and transactions and tend to talk non-emotionally. While relationship-oriented viewers might treat broadcasters as friends, using emotional language in their interactions. Our econometric model shows a curvilinear association of relationship-oriented social interaction and viewer purchase behaviors in LSC, but social interactions have varying effects on viewer purchase behaviors.We discuss implications of heterogeneous social-interaction strategies across different broadcasters

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

Imidazole-dione conjugate induces apoptosis and inhibits proliferation of osteosarcoma cells via activation of p65NFκB

Purpose: To investigate the effect of imidazole-dione conjugate (IMC) on proliferation of MG63 osteosarcoma cells. Methods: The effect of IMC on proliferation of MG63 osteosarcoma cells was determined using 3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, while mRNA expressions of PTEN, FasL and FasR were assayed with real-time reverse transcription polymerase chain reaction (RT-PCR). Cell apoptosis was studied by flow cytometry. The protein expression level of IκBα was determined using western blotting. Results: There were reductions in the proliferation of IMC-treated MG63 cells and Saos-2 cells at IMC dose of ≥ 4 μM (p < 0.05). Degree of proliferation of MG63 cells on exposure to 1, 2, 4, 6, 8 and 10 μM IMC was 99, 98, 76, 59, 34 and 21 %, respectively, relative to 100 % in untreated cultures. In MG63 cell cultures, treatment with 4, 6, 8 and 10 μM IMC led to 22, 39, 62 and 69 % apoptosis, respectively, when compared with 0.9 % apoptosis in control cell cultures (p < 0.05). Concentration-dependent increases were observed in PTEN, FasL and FasR mRNA in IMC-treated MG63 cells. Western blot assay showed a marked increase in the level of IκBα in MG63 cells following treatment with IMC. IMC treatment also caused a concentration-dependent increase in the expression of phospho-Ser536 p65NF-κB (p < 0.05). Conclusion: IMC exerts inhibitory effect on the proliferation of MG63 cells via up-regulation of NF-κB phosphorylation. Thus, IMC may be useful as a therapeutic agent for osteosarcoma

Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg¨ ha´1 ) was significantly lower than that of the SF (114 Mg¨ ha´1 ). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0–10 cm layer accounted for more than one third of the total soil C stock over 0–50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg¨ ha´1 , respectively, 64% of which was soil C for both stands (205 Mg¨ ha´1 for the SF and 127 Mg¨ ha´1 for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C

Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg¨ ha´1 ) was significantly lower than that of the SF (114 Mg¨ ha´1 ). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0–10 cm layer accounted for more than one third of the total soil C stock over 0–50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg¨ ha´1 , respectively, 64% of which was soil C for both stands (205 Mg¨ ha´1 for the SF and 127 Mg¨ ha´1 for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C