Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks

Scoring patent documents is very useful for technology management. However, conventional methods are based on static models and, thus, do not reflect the growth potential of the technology cluster of the patent. Because even if the cluster of a patent has no hope of growing, we recognize the patent is important if PageRank or other ranking score is high. Therefore, there arises a necessity of developing citation network clustering and prediction of future citations. In our research, clustering of patent citation networks by Stochastic Block Model was done with the aim of enabling corporate managers and investors to evaluate the scale and life cycle of technology. As a result, we confirmed nested SBM is appropriate for graph clustering of patent citation networks. Also, a high MAPE value was obtained and the direction accuracy achieved a value greater than 50% when predicting growth potential for each cluster by using LSTM.Comment: arXiv admin note: substantial text overlap with arXiv:1904.1204

Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

回腸利用膀胱拡大術後に膀胱腺癌を発症した一例

京都府立医科大学附属北部医療センター泌尿器科Department of Urology, North Medical Center Kyoto Prefectural University of Medicine小腸癌は全消化管疾患の中でも稀な疾患である。また、回腸利用膀胱拡大術後に利用した回腸から腺癌を生じた報告も稀であり、その治療法は確立されていない。若年時に先天性二分脊椎による神経因性膀胱に対する回腸利用膀胱拡大術後に63 歳時に回腸膀胱吻合部に腺癌を生じた一例を経験した。経尿道的手術、免疫チェックポイント阻害薬など試みるも効果得られず、診断から約１２ヶ月後永眠された。若干の文献的考察を加え報告する

Sequential therapies after atezolizumab plus bevacizumab or lenvatinib first-line treatments in hepatocellular carcinoma patients

Introduction: The aim of this retrospective proof-of-concept study was to compare different second-line treatments for patients with hepatocellular carcinoma and progressive disease (PD) after first-line lenvatinib or atezolizumab plus bevacizumab.Materials and methods: A total of 1381 patients had PD at first-line therapy. 917 patients received lenvatinib as first-line treatment, and 464 patients atezolizumab plus bevacizumab as first-line.Results: 49.6% of PD patients received a second-line therapy without any statistical difference in overall survival (OS) between lenvatinib (20.6 months) and atezolizumab plus bev-acizumab first-line (15.7 months; p = 0.12; hazard ratio [HR] = 0.80). After lenvatinib first-line, there wasn't any statistical difference between second-line therapy subgroups (p = 0.27; sorafenib HR: 1; immunotherapy HR: 0.69; other therapies HR: 0.85). Patients who under-went trans-arterial chemo-embolization (TACE) had a significative longer OS than patients who received sorafenib (24.7 versus 15.8 months, p < 0.01; HR = 0.64). After atezolizumab plus bevacizumab first-line, there was a statistical difference between second-line therapy subgroups (p < 0.01; sorafenib HR: 1; lenvatinib HR: 0.50; cabozantinib HR: 1.29; other therapies HR: 0.54). Patients who received lenvatinib (17.0 months) and those who under-went TACE (15.9 months) had a significative longer OS than patients treated with sorafenib (14.2 months; respectively, p = 0.01; HR = 0.45, and p < 0.05; HR = 0.46).Conclusion: Approximately half of patients receiving first-line lenvatinib or atezolizumab plus bevacizumab access second-line treatment. Our data suggest that in patients progressed to atezolizumab plus bevacizumab, the systemic therapy able to achieve the longest survival is lenvatinib, while in patients progressed to lenvatinib, the systemic therapy able to achieve the longest survival is immunotherapy