To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,...To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.展开更多
目的:儿外科手术因手术难度高、风险大,手术质量控制与评价困难。考虑到手术难度与手术质量的评价和疾病严重度密切相关,设计构建新型儿外科手术质量评价模型,用于评估儿外科手术质量。方法:将疾病严重度(severity of illness,SOI)作为...目的:儿外科手术因手术难度高、风险大,手术质量控制与评价困难。考虑到手术难度与手术质量的评价和疾病严重度密切相关,设计构建新型儿外科手术质量评价模型,用于评估儿外科手术质量。方法:将疾病严重度(severity of illness,SOI)作为手术质量评价的重要参数,查阅相关文献,历史数据回顾分析,初步确定SOI评估指标,采用网络分析法计算各指标的权重值;并将合并手术及合并症对手术结局的影响纳入模型,参考APR-DRGs为每种疾病和手术赋予权重。通过真实数据校正模型中各因子的β系数。结果:模型在北京市某三级甲等儿童专科医院予以应用,用于比较8个手术科室间的手术复杂度和手术质量评分,验证了模型的可行性。结论:儿外科手术评价模型基于疾病严重度、手术难度、院内资源消耗度的手术质量综合评价方法,为实现不同医院、不同科室、不同医生间的儿外科手术质量评价提供了一种的新方法。展开更多
基金The National Natural Science Foundation of China(No.72173018).
文摘To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.
文摘目的:儿外科手术因手术难度高、风险大,手术质量控制与评价困难。考虑到手术难度与手术质量的评价和疾病严重度密切相关,设计构建新型儿外科手术质量评价模型,用于评估儿外科手术质量。方法:将疾病严重度(severity of illness,SOI)作为手术质量评价的重要参数,查阅相关文献,历史数据回顾分析,初步确定SOI评估指标,采用网络分析法计算各指标的权重值;并将合并手术及合并症对手术结局的影响纳入模型,参考APR-DRGs为每种疾病和手术赋予权重。通过真实数据校正模型中各因子的β系数。结果:模型在北京市某三级甲等儿童专科医院予以应用,用于比较8个手术科室间的手术复杂度和手术质量评分,验证了模型的可行性。结论:儿外科手术评价模型基于疾病严重度、手术难度、院内资源消耗度的手术质量综合评价方法,为实现不同医院、不同科室、不同医生间的儿外科手术质量评价提供了一种的新方法。
