In this paper, we discussed the reduction of traffic energy consumption maintaining people's mobility. First of all, we defined the level of mobility and the level of traffic energy efficiency, and then we quantified these indexes by analyzing the person trip data of Kei-Han-Shin Metropolitan Area. Second, by comparing the level of traffic energy efficiency with the city characteristics, we clarified the relationship between urban structure and traffic energy efficiency. Third we clarified the algorithm of the traffic modal split which archives the maximum energy consumption reduction maintaining people's mobility. Finally, we simulated the effect of policy that improves in traffic energy efficiency.
Recently, electronic medical record (EMR) systems have become popular in Japan, and number of discharge summaries is stored electronically, though they have not been reutilized yet. We performed text mining with Tf-idf method and morphological analysis in the discharge summaries from three Hospitals (Chiba University Hospital, St. Luke's International Hospital and Saga University Hospital). We showed differences in the styles of summaries, between hospitals, while the rate of properly classified DPC (Diagnosis Procedure Combination) codes were almost the same. Beyond different styles of the discharge summaries, text mining method could obtain proper extracts of proper DPC codes. Improvement was observed by using integrated model data between the hospitals. It seemed that huge database which contains the data of many hospitals can improve the precision of text mining.
We performed the multi-year project to collect discharge summary from multiple hospitals and made the big text database to build a common document vector space, and developed various applications. We extracted 243,907 discharge summaries from seven hospitals. There was a difference in term structure and number of terms between the hospitals, however the differences by disease were similar. We built the vector space using TF-IDF method. We performed a cross-match analysis of DPC selection among seven hospitals. About 80% cases were correctly matched. The use of model data of other hospitals reduced selection rate to around 10%; however, integrated model data from all hospitals restored the selection rate.
Abstract Background The impact of the surgical margin (SM) on long-term survival remains controversial. This study retrospectively investigated the impact of the SM on prognosis and recurrence of intrahepatic cholangiocarcinoma (ICC) and evaluated the optimal margin width. Methods We reviewed the medical records of 58 ICC patients who underwent macroscopically curative surgery. Results The patients were classified into five categories according to the SM; R1, 0 to < 1 mm, 1 to < 5 mm, 5 to < 10 mm, and ≥ 10 mm. The prognosis tended to be different for SM < 1 mm or SM ≥ 1 mm, therefore, the cut-off value was set at 1 mm. Thirty-three (56.9%) patients had an SM ≥ 1 mm, and 25 (43.1%) had an SM < 1 mm. The multivariate analysis identified SM < 1 mm ( p = 0.027) and microvascular invasion ( p = 0.026) as independent prognostic factors of overall survival. After the propensity score-matching based on tumor-related factors, the overall survival and relapse-free survival rates of the SM < 1 mm group were significantly lower than those of the SM ≥ 1 mm group ( p = 0.013 and p = 0.025, respectively). Peritoneal dissemination was significantly increased in the SM < 1 mm group than in the SM ≥ 1 mm group ( p = 0.007). The post-recurrence survival rate of the SM < 1 mm group was significantly lower than that of the SM ≥ 1 mm group ( p = 0.012). Conclusions This study suggests that an SM of at least 1 mm should be achieved during ICC resection. An SM < 1 mm may indicate a higher risk of peritoneal dissemination.
We started a multi-year project to collect discharge summaries from multiple hospitals and create a big text database to build a common document vector space, and develop various applications such as the autoselection of the disease. As the first step, we extracted discharge summary from two hospitals. Using a text mining method, we carried out a DPC selection. There was a difference in term structure and number of terms between the discharge summaries from both hospitals. Nevertheless, the selection rate of the disease is resembled closely.
