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EMERGENCY DEMAND PREDICTION DEVICE, EMERGENCY DEMAND PREDICTION METHOD, AND PROGRAM

机译：紧急需求预测装置，紧急需求预测方法和程序

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摘要

Provided is a technique for efficiently predicting the number (quantity) of occurrences of emergency medical service requests in a target area. An emergency medical service demand prediction device according to an embodiment obtains actual history data including information about dates and times of occurrences of emergency medical service requests, information about positions of the occurrences of the emergency medical service requests, and information about illnesses and injuries that caused the emergency medical service requests; the device generates a first learning model that receives an input of first learning-purpose data generated on a basis of learning-purpose actual history data and outputs illness/injury groups; the device generates a second learning model that receives an input of second learning-purpose data generated on a basis of the learning-purpose actual history data and the illness/injury groups output from the first learning model and outputs a value indicating a quantity of occurrences of emergency medical service requests for each unit area; and the device predicts a quantity of occurrences of emergency medical service requests in each unit area, by inputting, to the second learning model having been trained, prediction-purpose data generated on a basis of prediction-purpose actual history data and the illness/injury groups output from the first learning model having been trained.

机译：提供了一种用于有效地预测目标区域中应急医疗服务请求的数量（数量）的技术。根据实施例的紧急医疗服务需求预测装置获得了包括关于应急医疗服务请求的日期的信息的实际历史数据，有关紧急医疗服务请求的发生的位置的信息，以及导致的疾病和伤害的信息紧急医疗服务请求;该设备生成第一学习模型，其接收基于学习实际历史数据和输出疾病/伤害组生成的第一学习目的数据的输入;该设备生成第二学习模型，其基于学习目的实际历史数据和从第一学习模型输出的疾病/伤害组来接收第二学习目的数据的输入，并输出指示出现量的值每个单位面积的紧急医疗服务请求;并且该设备通过输入通过输入的第二学习模型预测每个单位区域中的紧急医疗服务请求的发生量，以预测目的的实际历史数据和疾病/伤害生成的训练，预测目的数据从培训的第一学习模型输出的组输出。

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公开/公告号US2022020502A1

专利类型
公开/公告日2022-01-20

原文格式PDF
申请/专利权人 NIPPON TELEGRAPH AND TELEPHONE CORPORATION;
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申请/专利号US201917294514
发明设计人 SUNYONG KIM;IPPEI SHAKE;KAZUAKI OBANA;ATSUHIKO MAEDA;MICHIHARU TAKEMOTO;YUKIO KIKUYA;HIROSHI SATO;TETSUO KAWANO;KENICHI FUKUDA;
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申请日2019-11-08
分类号G16H50/70;G16H50/20;
国家 US
入库时间 2022-08-24 23:24:06

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