A Multi-View Learning based Clustering Method for Health Care System

Abstract

Abstract: Patients in hospitals have faced complications due to measurement errors, missing data, privacy issues etc. in electronic medical records. However, these medical records from heterogeneous sources have both structured and unstructured data. In particular, unstructured clinical data is valuable source of information including patient’s records of pathology data, radiology findings, medication order etc. However, to scrutinize, construe and presentation of this unstructured and high dimensional data is one of the significant modeling challenge that clinical support system has faced from many years before. Therefore, there is a need of some standard technique to locate both subjective and objective guesstimates of patient’s condition. Our endowments in this paper are twofold. First, present a multi-view learning technique, i.e. Collective Matrix Factorization to combine the extracted features from multiple views and gives a low dimensional representation of combined clinical data. Second, proposed a Genetic-K-means based clustering algorithm based on Collective Matrix Factorization for heterogeneous clinical records. It has been observed by the experiments that proposed method gives more accurate clustering results than existing method. Keywords: Clinical notes; Collective Matrix Factorization; Genetic; heterogeneous data; K-means; Multi-view learning.