Search

Clinical decision support system (CDSS) is an effective tool for improving healthcare quality. Since the clinical symptoms of some primary headache disorders in individual patients often overlap and that ill-defined boundaries for some headache features may be vague, current rule-based CDSS cannot perform as well as expected. In this paper, we develop a CDSS for primary headache disorder diagnosis based on rule-based and case-based reasoning in order to simulate a headache specialist's thinking process. First, the new case is evaluated by rule-based reasoning, the rules come from headache clinical guideline; second, if rule-based reasoning was unable to get accurate answer, case-based reasoning will find the most similar case in case library based on similarity matching. In particular, we define a similarity calculating method for primary headaches case. The final results show that the proposed approach improves the diagnostic accuracy dramatically compared to the rule-based primary headache diagnosis systems.

On the basis of traditional image-based diagnostic, there have been many research results shown that the comparative analysis would be more comprehensive, intuitive and targeted. In actual clinical environment, the comparison of medical imaging data on the clinician workstations is inefficient and cumbersome due to the limited support of an integrated way for presenting these data, and this leads to the emergence of the integrated visualization of these data. With the large amount of medical imaging data distributed in achieve servers, the integrated visualization on traditional clinician workstations often lead to poor user interaction. Based on the relatively mature framework of traditional clinician workstations, this paper has located the bottlenecks of data transmission and data parsing. In addition, it provides several optimization schemas against these bottlenecks, such as Streaming concept, multi-core programming and an optimization based on Intel IPP, to enhance the user interaction for the integrated visualization of imaging data. This optimized framework has been applied to many hospitals and proven to meet the clinical requirements.

Because the development of medical imaging equipment such as CT and MRI, the data for visualization which be processed continue increasing. In order to meet the demand of the large volume data of real-time processing, a novel technique for texture- based volume rending is discussed in this paper, which based on coordinate transformation and programming graphics processing unit. The algorithm makes full use of the parallel processing of graphics hardware and reduces the transmission of data between CPU and GPU, improves the speed of texture mapping algorithm. The experiments show that the proposed algorithm can improve the frame rate up to 10%-15%.