97 research outputs found

    デンシ カルテ ト システム ノ カクリツ

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    In the medical treatment with a strong subjective, experienced element, the flow of informationization is rapid in the maturation process of the society. Accuracy and the efficiency improvement of the medical treatment are strongly requested by the change in a social background, and a more systematic medical treatment system is needed though the medical treatment is complicated more and has upgraded. Environment, which improves the quality of the medical treatment and offers medical treatment efficiently is requested for the achievement. In the environment the technology of the information infrastructure, security, and the information exchange, information literacy, and the legislation etc. are maintained. EMR (Electronic Medical Record) with the function which becomes the center begins to spread, and the introduction in the clinic and the hospital is accelerating. The current state of an electronic medical record, the problem, and the constructive process of the system are outlined

    Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement

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    H.265/HEVC achieves an average bitrate reduction of 50% for fixed video quality compared with the H.264/AVC standard, while computation complexity is significantly increased. The purpose of this work is to improve coding efficiency for the next-generation video-coding standards. Therefore, by developing a novel spatial neighborhood subset, efficient spatial correlation-based motion vector prediction (MVP) with the coding-unit (CU) depth-prediction algorithm is proposed to improve coding efficiency. Firstly, by exploiting the reliability of neighboring candidate motion vectors (MVs), the spatial-candidate MVs are used to determine the optimized MVP for motion-data coding. Secondly, the spatial correlation-based coding-unit depth-prediction is presented to achieve a better trade-off between coding efficiency and computation complexity for interprediction. This approach can satisfy an extreme requirement of high coding efficiency with not-high requirements for real-time processing. The simulation results demonstrate that overall bitrates can be reduced, on average, by 5.35%, up to 9.89% compared with H.265/HEVC reference software in terms of the Bjontegaard Metric

    Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model

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    Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application

    Description and valuation of health-related quality of life among the general public in Japan by the EuroQol

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    Objective. Health-related quality of life plays an important role in assessing the effectiveness of health care. The EuroQol is a generic instrument for describing and valuing health-related quality of life. To elicit health state descriptions and their preferences among the general public in Japan and compare them with cross-national data, a feasibility study for applying the EuroQol to the general public in Japan was carried out. Subjects and methods. The subjects were 120 people aged 40s-60s randomly selected in a suburban area at Aichi Prefecture in Japan. In assessing health states and their preferences, the EuroQol valuation instrument (version 12, 1991) translated into Japanese was used. The questionnaires were distributed and collected by public health nurses. The valid responses (rate) were 89 (74%). Results. The mean scores (raw scores) using the visual analogue scale (VAS) for one’s own health was 89.2. No statistically significant difference in VAS scores was observed for both sex and age. The contribution ratio of own health status, sex and age was 0.326 (p<0.0001). The main independent variables were three dimensions of health status.Valuations for core health states varied from 96.3 (no problems in each health status) to 6.8 (dead). These VAS scores in Japan were correlated with those from other countries (p<0.001). A multivariate analysis indicated that bias from own health status on preference valuations for core health states was not observed. Conclusion. The health states and their preferences among the general public in Japan were estimated by using the EuroQol. The results show the feasibility of evaluation for health states quantitatively. Moreover, this study suggested cross-national and cross-cultural applicability of the EuroQol

    Needs assessment for healthcare technology assessment in Japan

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    Objective: Healthcare technology assessment (HTA) plays an essential role to support decision making in both health policy and clinical practice. To identify needs for HTA among general practitionners in Japan, a questionnaire survey carried out. Methods: A questionnaire survey was carried out. The subjects were 42 members of a association of general practitionner at Osaka in Japan. The valid response (rate) was 33 (79%). A questionnaire consisted of two parts. The first part dealt with needs and benefits for HTA. The second part dealt with prioritization of HTA. Results: A proportion of respondents who strongly pointed out the need of assessment for clinical effectiveness, efficiency, and ethical problems was 69%, 38%, and 63%, respectively. The most frequently suggested organizations for HTA was medical and dental associations (27%). A high priority was given to medical procedures as a type of healthcare technologies (HTs). Frequently mentioned HTs for HTA were as follows ; gene therapy, cancer chemotherapy, organ tansplantation, cancer screening, minimally invasive therapy, emergency medicine and so on. Conclusion : Most general pracititionners in an associations in Osaka, Japan have great concerns for needs of HTA. HTs identified as candidates for HTA were gene therapy, cancer chemotherapy, organ tansplantation and so on. On the basis of these results, further evaluations and discussions on priority settings for HTA should be done

    DRMS for Patient-Level Lymph Node Status Classification

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    Generally, automatic diagnosis of the presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Detection and classification of breast cancer metastases have high clinical relevance, especially in whole-slide images of histological lymph node sections. Fast early detection leads to huge improvement of patient’s survival rate. However, currently pathologists mainly detect the metastases with microscopic assessments. This diagnosis procedure is extremely laborious and prone to inevitable missed diagnoses. Therefore, automated, accurate patient-level classification would hold great promise to reduce the pathologist’s workload while also reduce the subjectivity of diagnosis. In this paper, we provide a novel deep regional metastases segmentation (DRMS) framework for the patient-level lymph node status classification. First, a deep segmentation network (DSNet) is proposed to detect the regional metastases in patch-level. Then, we adopt the density-based spatial clustering of applications with noise (DBSCAN) to predict the whole metastases from individual slides. Finally, we determine patient-level pN-stages by aggregating each individual slide-level prediction. In combination with the above techniques, the framework can make better use of the multi-grained information in histological lymph node section of whole-slice images. Experiments on large-scale clinical datasets (e.g., CAMELYON17) demonstrate that our method delivers advanced performance and provides consistent and accurate metastasis detection in clinical trials
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