A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3
Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks
Volume-3 | Issue-3
Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4
Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3
Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3
Future Challenges of the Internet of Things in the Health Care Domain - An Overview
Volume-3 | Issue-4
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
Volume - 2 | Issue - 2 | june 2020
Published
10 May, 2020
Patient preference management is an essential work for any healthcare scheme to give priority to the needy patient. The work is generally carryout by a caretaker in the healthcare block to enroll their details of the patient on computer to find out and suggest an available consultant and time slot for the patient. These kind of usual works can be helpful up to certain normal conditions only. During uncertain times like viral explosion or war or nature disaster, the usual system will make the patient to wait in a queue for enrollment process. Most of the time it is intolerable to make a severe injured person to wait in the queue for the treatment. At the same time, during viral explosion the people were asked to stay at their home and for treatment they have to make a phone call to the care taking team for expressing their situation and health status. Attending a huge number of phone calls manually and providing a good suggestion to the caller is a challenging work for any healthcare team. The proposed IoT based computer vision system suggests the patient to send their status through a mobile phone message or email to the healthcare server to segregate the status of patient as emergency, severe and follow-up categories. This makes the healthcare team to identify the needy patient at right time to serve them. The proposed system is simulated with different computer vision algorithm and analyses its accuracy, time delay and drop rate to make a reliable patient preference management system.
KeywordsIoT data management IoT patient management big data energy consumption
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