The data are randomly divided to generate the training set for the unsupervised learning and the testing set for the supervised learning. The other is a structured dataset on primary hypertension.

One is a plain text data set indexed by medical experts. There are two data sets used in the experiments. Then a support vector machine model is adopted to DBN at the second step of the supervised learning. At the first step, an optimized seven-layer deep belief network (DBN) is applied as an unsupervised learning algorithm to perform model training to acquire feature representation. The purpose of this paper is to evaluate a deep learning architecture as an effective solution for CAMDM.Ī two-step model is applied in our study. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM.

Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities.

Lowermore schoo lzip