A Real-Time Professional Content Recommendation System for Healthcare Providers’ Knowledge Acquisition

2018 ◽  
pp. 367-371
Author(s):  
Lu Qin ◽  
Xiaowei Xu ◽  
Jiao Li
Keyword(s):  
Knowledge Acquisition ◽  
Real Time ◽  
Recommendation System ◽  
Healthcare Providers ◽  
Content Recommendation

scholarly journals A Neural Network Aided Real-Time Hospital Recommendation System

2020 ◽  
Vol 5 (2) ◽  
pp. 217-235
Author(s):  
Om Adideva Paranjay ◽  
V Rajeshkumar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Recommendation System ◽  
Healthcare Providers ◽  
Geographical Location ◽  
Neural Model ◽  
The Past ◽  
Trained Neural Network ◽  
The Right

Over the past few years, there have been an overwhelming number of healthcare providers, hospitals and clinics. In such a situation, finding the right hospital for the right ailment can be a considerable challenge. Inspired by this challenge, this work attempts to build a model that can automatically recommend hospitals based on user requirements. In the past there have been important works in physician recommendation.  Our proposed work aims to be more inclusive and provide an automated hospital recommendation system to patients based on neural networks driven classification. We suggest a model that considers several unique parameters, including geographical location. To optimize its usefulness, we design a system that recommends hospitals for general consultation, specialty hospitals, and in view of the pandemic, hospitals recommended for treatment of COVID-19. In this work, we adopt Neural Networks and undertake a comparative analysis between several different available supervised algorithms to identify one best suited neural architecture that can work best in the applied fields. Based on our results from the analysis, we train the selected neural network with context relevant data. In the image of the recommendation system, we develop a website that uses the trained neural network on its backend and displays the recommendation results in a manner interpretable by the end user. We highlight the process of choosing the right neural model for the backend of the website.  To facilitate the working of the website in real-time, we use real time databases hosted on Google Firebase and edge devices on hospital ends. Additionally, we suggest two hospital side data updation tools. These tools would ensure that hospitals can update the parameters which change quickly in the real world to their latest values so as to maintain the precision of the system. We test the website with test data and find that the website recommends hospitals with sufficient precision in the specified format. The model has been designed with the limited amount of data available in this field, but its performance and utility can be easily improved with better quality and more abundant data.

scholarly journals Psychosocial impact of COVID-19 on cancer patients, survivors, and carers in Australia: a real-time assessment of cancer support services

2021 ◽  
Author(s):  
Rhiannon Edge ◽  
Carolyn Mazariego ◽  
Zhicheng Li ◽  
Karen Canfell ◽  
Annie Miller ◽  
...  
Keyword(s):  
Content Analysis ◽  
Real Time ◽  
Cancer Patients ◽  
Online Community ◽  
Information Needs ◽  
Healthcare Providers ◽  
Service Demand ◽  
Cancer Support ◽  
Cancer Council ◽  
Psychosocial Impacts

Abstract Purpose This study aimed to explore the psychosocial impacts of the coronavirus disease (COVID-19) pandemic on cancer patients, survivors, and carers in Australia. Methods Using real-time insights from two Cancer Council NSW services—131120 Information and Support Line and Online Community (CCOC) forums—we assessed service demand trends, distress levels (using the distress thermometer), and content from 131120 calls and online posts between 01 December 2019 and 31 May 2020. Emergent themes were identified through an inductive conventional content analysis with 131120 call notes, followed by a deductive directed content analysis on CCOC posts. Results In total, 688 COVID-19-related 131120 calls (n = 496) and online posts (n = 192) were analysed. Service demand peaked in March 2020 and self-reported distress peaked in May 2020 at an average of 8/10 [Mean = 7.5; SD = 0.9]. Five themes emerged from the qualitative analysis: psychological distress and fear of virus susceptibility, practical issues, cancer service disruptions, information needs, and carer Issues. Conclusions The psychosocial impacts of COVID-19 on people affected by cancer are multifaceted and likely to have long-lasting consequences. Our findings drove the development of six recommendations across three domains of support, information, and access. Cancer patients, survivors, and carers already face stressful challenges dealing with a cancer diagnosis or survivorship. The added complexity of restrictions and uncertainty associated with the pandemic may compound this. It is important that healthcare providers are equipped to provide patient-centred care during and after this crisis. Our recommendations provide points of consideration to ensure care is tailored and patient oriented.

scholarly journals Phone-Based Restaurant Recommendation System by Using Position Information and Association Rules

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Association Rules ◽  
Recommendation System ◽  
Rapid Development ◽  
Smart Phone ◽  
Smart Phones ◽  
Location Information ◽  
Position Information ◽  
The Real

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.

MEDICARE AI-ASSISTED AI IN HEALTHCARE

2021 ◽  
pp. 40-44
Author(s):  
Nitish Nagar ◽  
Plaban Roy ◽  
Saurabh Deswal ◽  
Parth Pandya ◽  
Aashna Bhardwaj ◽  
...  
Keyword(s):  
Real Time ◽  
Healthcare Providers ◽  
Medical Checkup ◽  
Severity Of The Disease

Articial Intelligence is nowadays at the peak, and considering chatbots is one of the primary examples contributing to success. In the project paper, we implemented an instant diagnostic medical checkup for the patient, which can help doctors/healthcare providers, as this order saves time. By analyzing the symptoms, the Assist AI system will help determine the severity of the disease in real-time and will respectively guide the patient with the different stages – disease denition, precaution and recommendation.

A Cross-Platform Blood Donation Application with a Real-Time, Intelligent, and Rational Recommendation System

2021 ◽  
Author(s):  
Md Rafat Jamader Maraz ◽  
Rashik Rahman ◽  
Md. Mehedi Ul Hasnain ◽  
Hasan Murad
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Blood Donation ◽  
Cross Platform

scholarly journals An Improved Social Media Analysis on 3 Layers: A Real Time Enhanced Recommendation System

2018 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohamed Amine ◽  
Hicham AIT ◽  
Reda MOULOUKI ◽  
Saida NKIRI ◽  
Mohamed AZOUAZI
Keyword(s):  
Social Media ◽  
Real Time ◽  
Recommendation System ◽  
Media Analysis ◽  
Social Media Analysis

scholarly journals Demand Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Fuel Charging Mechanism

2019 ◽  
pp. 38-44
Author(s):  
Selvi C ◽  
Keerthana D
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Large Scale ◽  
Recommendation System ◽  
Important Research ◽  
Taxi Drivers ◽  
Charging Station ◽  
Gps Trajectories ◽  
Charging Mechanism ◽  
Supply Level

Data mining depends on large-scale taxi traces is an important research concepts. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. The project first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. This project constructs a recommendation system by jointly considering the profits of both drivers and passengers. At last, the qualified candidates are suggested to drivers based on analysis. The project also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi’s historical recharging actions and real-time GPS trajectories, the present operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, recommend a charging station that leads to the minimal total time before its recharging starts.

Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

2017 ◽  
pp. 113-124
Author(s):  
Başar Öztayşi ◽  
Ahmet Tezcan Tekin ◽  
Cansu Özdikicioğlu ◽  
Kerim Caner Tümkaya
Keyword(s):  
Recommendation System ◽  
Predictive Analytics ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Web Contents ◽  
Interest Level ◽  
History Of ◽  
Content Recommendation ◽  
Publishing Services ◽  
Web Publishing

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting

2020 ◽  
Vol 270 ◽  
pp. 115144 ◽  
Author(s):  
Wenqiang Li ◽  
Guangcai Gong ◽  
Houhua Fan ◽  
Pei Peng ◽  
Liang Chun
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Learning Strategy ◽  
User Preferences ◽  
Cooling Load ◽  
Meta Learning
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