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一种针对新冠肺炎感染患者的新型服务机器人分配方法：医学数据驱动决策的案例

A novel service robot assignment approach for COVID-19 infected patients: a case of medical data driven decision making.

作者信息

Jena Kalyan Kumar, Nayak Soumya Ranjan, Bhoi Sourav Kumar, Verma K D, Prakash Deo, Gupta Abhishek

机构信息

Department of Computer Science and Engineering, PMECParala Maharaja Engineering College, Berhampur, India.

PradeshAmity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India.

出版信息

Multimed Tools Appl. 2022;81(29):41995-42021. doi: 10.1007/s11042-022-13524-5. Epub 2022 Sep 3.

DOI:10.1007/s11042-022-13524-5
PMID:36090152
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9440332/
Abstract

Coronavirus Disease-19 (COVID-19) is a major concern for the entire world in the current era. Coronavirus is a very dangerous infectious virus that spreads rapidly from person to person. It spreads in exponential manner on a global scale. It affects the doctors, nurse and other COVID-19 warriors those who are actively involved for the treatment of COVID-19 infected (CI) patients. So, it is very much essential to focus on automation and artificial intelligence (AI) in different hospitals for the treatment of such infected patients and all should be very much careful to break the chain of spreading this novel virus. In this paper, a novel patient service robots (PSRs) assignment framework and a priority based (PB) method using fuzzy rule based (FRB) approach is proposed for the assignment of PSRs for CI patients in hospitals in order to provide safety to the COVID-19 warriors as well as to the CI infected patients. This novel approach is mainly focused on lowering the active involvement of COVID-19 warriors for the treatment of high asymptotic COVID-19 infected (HACI) patients for handling this tough situation. In this work, we have focused on HACI and low asymptotic COVID-19 infected (LACI) patients. Higher priority is given to HACI patients as compared to LACI patients to handle this critical situation in order to increase the survival probability of these patients. The proposed method deals with situations that practically arise during the assignment of PSRs for the treatment of such patients. The simulation of the work is carried out using MATLAB R2015b.

摘要

新型冠状病毒肺炎（COVID-19）是当前全球关注的重大问题。冠状病毒是一种非常危险的传染性病毒，可在人与人之间迅速传播。它在全球范围内呈指数级传播。它影响着医生、护士和其他积极参与治疗COVID-19感染（CI）患者的抗疫勇士。因此，不同医院在治疗此类感染患者时，关注自动化和人工智能（AI）非常重要，所有人都应非常小心地打破这种新型病毒的传播链。本文提出了一种新颖的患者服务机器人（PSR）分配框架以及一种基于优先级（PB）的方法，该方法使用基于模糊规则（FRB）的方法为医院中的CI患者分配PSR，以保障抗疫勇士以及CI感染患者的安全。这种新颖的方法主要致力于减少抗疫勇士对高症状COVID-19感染（HACI）患者治疗的积极参与，以应对这一严峻形势。在这项工作中，我们关注的是HACI患者和低症状COVID-19感染（LACI）患者。与LACI患者相比，HACI患者被赋予更高的优先级，以应对这一危急情况，从而提高这些患者的生存概率。所提出的方法处理了在为治疗此类患者分配PSR过程中实际出现的情况。使用MATLAB R2015b对该工作进行了仿真。

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