| 面向医疗设备的深度问答系统的设计 |
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| 引用本文： | 饶林尚,吴怡,冯前进.面向医疗设备的深度问答系统的设计[J].计算机应用与软件,2019(6):171-176. |
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| 作者姓名： | 饶林尚 吴怡 冯前进 |
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| 作者单位： | 1.南方医科大学生物医学工程学院;2.广东省医学图像处理重点实验室 |
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| 基金项目： | 广东省重大科技专项(2015B010106008) |
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| 摘 要： | 提出辅助医疗设备维修保养的深度问答系统的设计方案。为医院设备工程师提供智能化的设备信息咨询平台,提供日趋复杂而广泛的设备知识服务,增加医院设备的效益。系统包括算法模块和应用模块,算法模块通过深度学习卷积神经网络实现。通过设计实验进行答案搜索任务测试,在问题相似度前三的反馈信息里面,包含搜索目标的准确率达65%,证明算法可搜索到有效信息。将算法模型嵌入到Web应用中,进一步实现问答的功能。
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| 关 键 词： | 深度学习 CNN 深度问答系统 SPRINGMVC 医疗设备 |
DESIGN OF DEEP QUESTION ANSWERING SYSTEM FOR MEDICAL EQUIPMENT |
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| Rao Linshang,Wu Yi,Feng Qianjin.DESIGN OF DEEP QUESTION ANSWERING SYSTEM FOR MEDICAL EQUIPMENT[J].Computer Applications and Software,2019(6):171-176. |
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| Authors: | Rao Linshang Wu Yi Feng Qianjin |
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| Affiliation: | (School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, Guangdong, China;Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, Guangdong, China) |
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| Abstract: | This paper presented a design of deep question answering system to assist medical equipment maintenance.It provided an intelligent equipment information consulting platform for hospital equipment engineers,provided increasingly complex and extensive equipment knowledge services,and increased the benefits of hospital equipment.The system included algorithm module and application module.The algorithm module was implemented by deep learning convolutional neural network.We designed the experiment and tested the answer search task.The accuracy of the search target contained in the feedback information with the first three questions similarity was up to 65%.This proves that the algorithm can search for effective information,and embeds the algorithm model into the Web application to further realize the function of question answering. |
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| Keywords: | Deep learning CNN Deep QA system Spring MVC Medical equipment |
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