Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from l...Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.展开更多
目的探索心胸外科重症监护室(Cardiothoracic Intensive Care Unit,CTICU)智能管理系统的应用成效,以支持医疗决策和个性化医疗。方法通过患者数据构建数字孪生库,利用数字孪生技术(Digital Twinning,DT)生成患者的虚拟模型,并实时比对...目的探索心胸外科重症监护室(Cardiothoracic Intensive Care Unit,CTICU)智能管理系统的应用成效,以支持医疗决策和个性化医疗。方法通过患者数据构建数字孪生库,利用数字孪生技术(Digital Twinning,DT)生成患者的虚拟模型,并实时比对实际患者数据,以实现高度精确的监护和管理。结果引入DT技术建立了智能且人性化的CTICU管理系统,与传统ICU相比,在查询响应时间、数据读取和写入等性能上有显著提升(均P<0.05),为医疗护理和决策提供了有力支持。结论基于DT技术的CTICU智能管理系统建设有助于提升治疗效果和患者安全性,为医疗团队提供了更多支持,推动医疗领域的发展。展开更多
基金the Special Project of the Shanghai Municipal Commission of Economy and Information Technology for Promoting High-Quality Industrial Development(No.2024-GZL-RGZN-02011)the Shanghai City Digital Transformation Project(No.202301002)the Project of Shanghai Shenkang Hospital Development Center(No.SHDC22023214)。
文摘Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.
文摘目的探索心胸外科重症监护室(Cardiothoracic Intensive Care Unit,CTICU)智能管理系统的应用成效,以支持医疗决策和个性化医疗。方法通过患者数据构建数字孪生库,利用数字孪生技术(Digital Twinning,DT)生成患者的虚拟模型,并实时比对实际患者数据,以实现高度精确的监护和管理。结果引入DT技术建立了智能且人性化的CTICU管理系统,与传统ICU相比,在查询响应时间、数据读取和写入等性能上有显著提升(均P<0.05),为医疗护理和决策提供了有力支持。结论基于DT技术的CTICU智能管理系统建设有助于提升治疗效果和患者安全性,为医疗团队提供了更多支持,推动医疗领域的发展。
