Semi-supervised fuzzy co-clustering for hospital-cost analysis from electronic medical records

It has been widely recognized that decision-making is a crucial part of hospital management which goes through a process of medical behavior. Even though data mining and knowledge discovery techniques have been used to clinical medicine frequently, little research has been conducted on hospital decision-making especially hospital-cost analysis on treatment therapies among inpatients which is considered as an important aspect of annual hospital evaluation and accreditation. In this paper, we propose a novel semi-supervised fuzzy co-clustering method for hospital-cost analysis from electronic medical records. Fuzzy co-clustering is a well-known technique that performs simultaneous fuzzy clustering of objects and features which result in dynamic dimensionality reduction mechanism for categorizing high-dimensional data. However, in many real-world applications, prior knowledge of a dataset is actually available to the users; thus it is necessary to integrate this information in the clustering process. This approach is called semi-supervised fuzzy co-clustering which has been investigated and developed further within an application to hospital-cost analysis of Hanoi Medical University Hospital (HMUH), Vietnam. The findings of the paper suggest the most crucial factors for medical expense in HMUH which are significant to gradually reduce the cost of treatment meanwhile improve the quality of services.