Treatment Strategy of Septicemia Using DCP Data with pLSA which especially can Model Clients' Change along with Time

Abstract

This paper show our research about treatment strategy of septicemia using DCP data with pLSA, probabilistic latent semantic analysis, which especially can model clients' change along with time. We used a variable which combine clients' ID and the days from hospitalization when we made some medical treatment and what we do as treatment to cluster our clients with pLSA, then summarized death rate and medical cost, days clients stay in this hospital of each cluster. Through researching clients' change along with time, We found some pattern how a client in some cluster move to another cluster those in this tend to die more, although there are some other clients who stay same cluster enough days after some treatments. Our research and method are accepted as some possibility of DCP data to assist treatment strategy in medical field.