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13th International Conference on Computer and Knowledge Engineering
An overview of Business Intelligence research in healthcare organizations using a topic modeling approach
Authors :
Mohammad Mehraeen
1
Laya Mahmoudi
2
Mohammad Hossein Sharifi
3
1- Professor, Dept. of Management Ferdowsi University of Mashhad Mashhad, Iran
2- Ph.D Student at FUM, Dept. of Management Ferdowsi University of Mashhad Mashhad, Iran
3- Ph.D Student at FUM, Dept. of Management Ferdowsi University of Mashhad Mashhad, Iran
Keywords :
Business intelligence،Healthcare organizations،Topic Modeling،Text mining
Abstract :
The intersection of Business intelligence (BI) and data analytics with healthcare has witnessed remarkable growth in recent decades. With the aim of identifying the most attentive themes, topics, and research priorities, this review endeavors to perform a literature analysis on research articles addressing business intelligence in healthcare. To conduct the study, a total of 751 articles, published between 2002 and 2023, were analyzed through topic modeling, a technique used in natural language processing (NLP) and machine learning. As a result, seven major themes were identified: Unique Device Identification, Big Data and Data Analytics, Operational Efficiency, Effective Decision-Making, Healthcare Delivery Optimization, Resource Optimization and Cost Reduction, and Nosocomial Infections are growing. The analysis revealed that there is a growing interest in leveraging business intelligence for infection control and operational improvements using health information technology and data analytics, while efforts to optimize resources/reduce costs appear to be on the decline. The research findings shed light on the increasing capabilities and use of business intelligence in healthcare, highlighting opportunities for future research.
Papers List
List of archived papers
Enhanced Duplicate Bug Report Detection in Anonymized Environments: A Parallelized Multi-Task Learning Framework
Alireza Shorafa - Abolfazl Zarghani
Uncertainty-Aware Deep Ensembles for Confident Customer Churn Prediction with Rejection Option
Fatemeh Moradi - Mehran Tarif - Mohammadhossein Homaei
A New Time Series Approach in Churn Prediction with Discriminatory Intervals
Hedieh Ahmadi - Seyed Mohammad Hossein Hasheminejad
Improving LoRaWAN Scalability for IoT Applications using Context Information
Hamed Mahmoudi - Behrouz ShahgholiGhahfarokhi
A parallel CNN-BiGRU network for short-term load forecasting in demand-side management
Arghavan Irankhah - Sahar Rezazadeh Saatlou - Mohammad Hossein Yaghmaee - Sara Ershadi-Nasab - Mohammad Alishahi
Emotion Recognition In Persian Speech Using Deep Neural Networks
Ali Yazdani - Hossein Simchi - Yasser Shekofteh
An Efficient Approach for Breast Abnormality Detection through High-Level Features of Thermography Images
Farhad Abedinzadeh Torghabeh - Yeganeh Modaresnia - Seyyed Abed Hosseini
Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
Seyed Mohammad Hossein Hashemi - Leila Safari - Mohsen Hooshmand - Amirhossein Dadashzadeh Taromi
SCDS: A Secure Clustering Protocol Using Dempster-Shafer Theory for VANET in Smart City
Hoda Mosadegh - Nazbanoo Farzaneh
An Automated Visual Defect Segmentation for Flat Steel Surface Using Deep Neural Networks
Dorna Nourbakhsh Sabet - Mohammad Reza Zarifi - Javad Khoramdel - Yasamin Borhani - Esmaeil Najafi
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