目的基于临床护理分类(clinical care classification,CCC)系统2.5版,构建患儿体温过高护理程序知识库。方法以患儿常见症状"腋下温度≥37.5℃"为研究内容,采用循证护理法,研究小组结合临床护理常规及医院管理制度,汇总与&qu...目的基于临床护理分类(clinical care classification,CCC)系统2.5版,构建患儿体温过高护理程序知识库。方法以患儿常见症状"腋下温度≥37.5℃"为研究内容,采用循证护理法,研究小组结合临床护理常规及医院管理制度,汇总与"腋下温度≥37.5℃"相关的所有护理措施。基于CCC系统2.5版"腋下温度≥37.5℃"相关护理诊断、核心护理干预措施、护理活动类型修饰语及护理结局修饰语,构建患儿体温过高护理程序知识库初稿。按照CCC系统2.5版中"具体护理措施编码=核心护理干预编码+护理活动类型修饰语编码""护理结局编码=护理诊断编码+护理结局修饰语编码"的编码规则,对知识库初稿内容进行编码,然后通过专家咨询对知识库初稿内容及编码进行评价、讨论与修改,确定知识库终稿。结果患儿体温过高护理程序知识库终稿包括1项护理诊断、19项具体护理措施和4种护理结局。结论患儿体温过高护理程序知识库具有专业性、科学性与实用性,标准化了护理语言,有利于信息共享。展开更多
In order to improve the learning ability of robots, we present a reinforcement learning approach with a knowledge base for mapping natural language instructions to executable action sequences. A simulated platform wit...In order to improve the learning ability of robots, we present a reinforcement learning approach with a knowledge base for mapping natural language instructions to executable action sequences. A simulated platform with physical engine is built as interactive environment. Based on the knowledge base, a reward function with immediate rewards and delayed rewards is designed to handle sparse reward problems. Also, a list of object states is produced by retrieving the knowledge base, as a standard to define the quality of action sequences. Experimental results demonstrate that our approach yields good performance on accuracy of action sequences production.展开更多
文摘目的基于临床护理分类(clinical care classification,CCC)系统2.5版,构建患儿体温过高护理程序知识库。方法以患儿常见症状"腋下温度≥37.5℃"为研究内容,采用循证护理法,研究小组结合临床护理常规及医院管理制度,汇总与"腋下温度≥37.5℃"相关的所有护理措施。基于CCC系统2.5版"腋下温度≥37.5℃"相关护理诊断、核心护理干预措施、护理活动类型修饰语及护理结局修饰语,构建患儿体温过高护理程序知识库初稿。按照CCC系统2.5版中"具体护理措施编码=核心护理干预编码+护理活动类型修饰语编码""护理结局编码=护理诊断编码+护理结局修饰语编码"的编码规则,对知识库初稿内容进行编码,然后通过专家咨询对知识库初稿内容及编码进行评价、讨论与修改,确定知识库终稿。结果患儿体温过高护理程序知识库终稿包括1项护理诊断、19项具体护理措施和4种护理结局。结论患儿体温过高护理程序知识库具有专业性、科学性与实用性,标准化了护理语言,有利于信息共享。
基金supported by National Natural Science Foundation of China (No. 61773239)Shenzhen Future Industry Special Fund (No. JCYJ20160331174814755)
文摘In order to improve the learning ability of robots, we present a reinforcement learning approach with a knowledge base for mapping natural language instructions to executable action sequences. A simulated platform with physical engine is built as interactive environment. Based on the knowledge base, a reward function with immediate rewards and delayed rewards is designed to handle sparse reward problems. Also, a list of object states is produced by retrieving the knowledge base, as a standard to define the quality of action sequences. Experimental results demonstrate that our approach yields good performance on accuracy of action sequences production.
