Patient-specific early classification of multivariate observations.
Early classification of time series has been receiving a lot of attention recently. In this paper we present a model, which we call the Early Classification Model (ECM), that allows for early, accurate and patient-specific classification of multivariate observations. ECM is comprised of an integration of the widely used Hidden Markov Model (HMM) and Support Vector Machine (SVM) models. It attained very promising results on the datasets we tested it on: in one set of experiments based on a published dataset of response to drug therapy in Multiple Sclerosis patients, ECM used only an average of 40% of a time series and was able to outperform some of the baseline models, which needed the full time series for classification. In the set of experiments tested on a sepsis therapy dataset, ECM was able to surpass the standard threshold-based method and the state-of-the-art method for early classification of multivariate time series.
Mohamed F
MF
Ghalwash
Dušan
D
Ramljak
Zoran
Z
Obradović
D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D008390:Markov Chains; D009103:Multiple Sclerosis / Q000188:drug therapy; D015999:Multivariate Analysis; D018805:Sepsis / Q000145:classification / Q000175:diagnosis; D060388:Support Vector Machine