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
Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, whic...Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.展开更多
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。
基金the Artificial Intelligence Innovation and Development Project of Shanghai Municipal Commission of Economy and Information (No. 2019-RGZN-01081)。
文摘Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.
