J Med Syst
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This article aims at building clinical data groups for Electronic Medical Records (EMR) in China. These data groups can be reused as basic information units in building the medical sheets of Electronic Medical Record Systems (EMRS) and serve as part of its implementation guideline. The results were based on medical sheets, the forms that are used in hospitals, which were collected from hospitals. ⋯ As a pilot study of health information standards in China, the development of EMR data groups combined international standards with Chinese national regulations and standards, and this was the most critical part of the research. The original medical sheets from hospitals contain first hand medical information, and some of their items reveal the data types characteristic of the Chinese socialist national health system. It is possible and critical to localize and stabilize the adopted international health standards through abstracting and categorizing those items for future sharing and for the implementation of EMRS in China.
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In this study, power spectrum of the EEG data and the heartbeat data obtained from 250 patients has been applied to the designed Neural network system. A backpropagation artificial neural network has been developed which contains 53 nodes in the input layer, 27 nodes in the hidden and 1 node in the output layer. In the artificial neural network inputs, the power spectral density values corresponding 1-50 Hz frequency interval of the EEG slices which has 10 seconds of time interval, the ratio of the total of the PSD values of current EEG slice to the total PSD values of EEG slice of pre-anesthesia, the ratio of the total PSD values of the EEG data to the total PSD values of the previous EEG data, and the previous anaesthetic gas ratio values have been applied and the network has been educated. ⋯ In the anesthetic gas prediction according to the anesthesia level, successful results have been obtained with the designed system. The system has been able to correctly purposeful responses in average accuracy of 94% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.
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This paper presents the framework for forecasting the surgery time by taking into account the surgical environment in an ophthalmology department (experience of surgeon in years, experience of anesthetist in years, staff experience in years, type of anesthesia etc.). The estimation of surgery times is done using three techniques, such as the Adaptive Neuro Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN) and Multiple Linear Regression Analysis (MLRA) and the results of estimation accuracy were compared. ⋯ It is hypothesized that by accurately knowing the surgery times, one can schedule the operations optimally resulting in the efficient utilization of the operating rooms. This increase in the efficiency is demonstrated through computer simulations of the operating theater.