Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value

Learning Concept Interestingness for Identifying Key Structures from Social Networks

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIdentifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studies has been performed independently, leading to huge computational overheads. To address this challenge, this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework. Specifically, we first implement the FCA-based representation of a social network and then generate the fine-grained knowledge representation, namely concept. Then, an efficient concept interestingness calculation algorithm suitable for social network scenarios is proposed. Next, we then leverage concept interestingness to quantify the hidden relations between concepts and network structures. Finally, an efficient algorithm for jointly key structures detection is developed based on constructed mapping relations. Extensive experiments conducted on real-world networks demonstrate that the efficiency and effectiveness of our proposed approach.Fundamental Research Funds for the Central Universitie

A deep learning based community detection approach

Community Detection in On Line Social Networks is a classic feature in networked systems, from the fields of biology, economics, politics and computer science, as well. This paper describes a novel Community Detection method based on a deep learning approach, facing the challenging problems related to the dimensions of the involved data structures, and proposing a novel convolutional technique particularly useful for sparse matrices. Several experiments have been reported and discussed in real scenarios