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Your search keyword '"Kudo, Michiharu"' showing total 101 results
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101 results on '"Kudo, Michiharu"'

1. Data-Driven Disease Progression Modeling

Author

Ng, Kenney, Ghalwash, Mohamed, Chakraborty, Prithwish, Sow, Daby M., Koseki, Akira, Yanagisawa, Hiroki, Kudo, Michiharu, Kiel, Joan M., editor, Kim, George R., editor, and Ball, Marion J., editor

Published
2022
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2. Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps

Author

Katsuki, Takayuki, Osogami, Takayuki, Koseki, Akira, Ono, Masaki, Kudo, Michiharu, Makino, Masaki, and Suzuki, Atsushi

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. Unlike in ordinary time series, time intervals are not constant, small time-shifts have no significant effect, and inputting timestamps or time durations into a model is not effective. The criteria that we require for the modeling are providing robustness against time-shifts or timestamps uncertainty as well as maintaining the essential capabilities of time-series models, i.e., forgetting meaningless past information and handling infinite sequences. The proposed method handles them with a convolutional mechanism across time with specific parameterizations, which efficiently represents the event dependencies in a time-shift invariant manner while discounting the effect of past events, and a dynamic pooling mechanism, which provides robustness against the uncertainty in timestamps and enhances the time-discounting capability by dynamically changing the pooling window size. In our learning algorithm, the decaying and dynamic pooling mechanisms play critical roles in handling infinite and variable length sequences. Numerical experiments on real-world event sequences with ambiguous timestamps and ordinary time series demonstrated the advantages of our method., Comment: 18th IEEE International Conference on Data Mining (ICDM 2018)

Published
2018
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4. Checking Security Policy Compliance

Author

Gowadia, Vaibhav, Farkas, Csilla, and Kudo, Michiharu

Subjects
Computer Science - Cryptography and Security, H.1.1, H.2.0.a, I.2.2.e, I.2.4
Abstract

Ensuring compliance of organizations to federal regulations is a growing concern. This paper presents a framework and methods to verify whether an implemented low-level security policy is compliant to a high-level security policy. Our compliance checking framework is based on organizational and security metadata to support refinement of high-level concepts to implementation specific instances. Our work uses the results of refinement calculus to express valid refinement patterns and their properties. Intuitively, a low-level security policy is compliant to a high-level security policy if there is a valid refinement path from the high-level security policy to the low-level security policy. Our model is capable of detecting violations of security policies, failures to meet obligations, and capability and modal conflicts., Comment: 23 pages; submitted to TKDE; original submission 15 mar 2007; revised 20 jan 2008

Published
2008

5. Inter-organizational Business Processes Managed by Blockchain

Author

Nakamura, Hiroaki, Miyamoto, Kohtaroh, Kudo, Michiharu, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Hacid, Hakim, editor, Cellary, Wojciech, editor, Wang, Hua, editor, Paik, Hye-Young, editor, and Zhou, Rui, editor

Published
2018
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6. Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event Analysis

Author

Ghalwash, Mohamed, Koseki, Akira, Iwamori, Toshiya, Kudo, Michiharu, and Meyer, Pablo

Subjects
Articles
Abstract

In Chronic Kidney Disease (CKD), kidneys are damaged and lose their ability to filter blood, leading to a plethora of health consequences that end up in dialysis. Despite its prevalence, CKD goes often undetected at early stages. In order to better understand disease progression, we stratified patients with CKD by considering the time to dialysis from diagnosis of early CKD (stages 1 or 2). To achieve this, we first reduced the number of clinical features in a predictive time-to-dialysis model and identified the top important features on a cohort of ∼ 40, 000 CKD patients. The extracted features were used to stratify a subpopulation of 3, 522 patients that showed anemia and were prescribed for cardiovascular-related drugs and progressed faster to dialysis. On the other side, clustering patients using conventional clustering methods based on their clinical features did not allow such clear interpretation to identify the main factors for leading fast progression to dialysis. To our knowledge this is the first study extracting interpretable features for stratifying a cohort of early CKD patients using time-to-event analysis which could help prevention and the development of new treatments.

Published
2023

7. Business Monitoring Framework for Process Discovery with Real-Life Logs

Author

Abe, Mari, Kudo, Michiharu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sadiq, Shazia, editor, Soffer, Pnina, editor, and Völzer, Hagen, editor

Published
2014
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11. Fine-Grained Sticky Provenance Architecture for Office Documents

Author

Mishina, Takuya, Yoshihama, Sachiko, Kudo, Michiharu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Miyaji, Atsuko, editor, Kikuchi, Hiroaki, editor, and Rannenberg, Kai, editor

Published
2007
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12. Bridging the Gap Between Inter-communication Boundary and Internal Trusted Components

Author

Watanabe, Yuji, Yoshihama, Sachiko, Mishina, Takuya, Kudo, Michiharu, Maruyama, Hiroshi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gollmann, Dieter, editor, Meier, Jan, editor, and Sabelfeld, Andrei, editor

Published
2006
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13. XML Access Control with Policy Matching Tree

Author

Qi, Naizhen, Kudo, Michiharu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, di Vimercati, Sabrina de Capitani, editor, Syverson, Paul, editor, and Gollmann, Dieter, editor

Published
2005
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15. Access-Condition-Table-Driven Access Control for XML Databases

Author

(Naishin Seki), Naizhen Qi, Kudo, Michiharu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Samarati, Pierangela, editor, Ryan, Peter, editor, Gollmann, Dieter, editor, and Molva, Refik, editor

Published
2004
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17. Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

Author

Inaguma, Daijo, primary, Hayashi, Hiroki, additional, Yanagiya, Ryosuke, additional, Koseki, Akira, additional, Iwamori, Toshiya, additional, Kudo, Michiharu, additional, Fukuma, Shingo, additional, and Yuzawa, Yukio, additional

Published
2022
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33. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Author

Makino, Masaki, primary, Yoshimoto, Ryo, additional, Ono, Masaki, additional, Itoko, Toshinari, additional, Katsuki, Takayuki, additional, Koseki, Akira, additional, Kudo, Michiharu, additional, Haida, Kyoichi, additional, Kuroda, Jun, additional, Yanagiya, Ryosuke, additional, Saitoh, Eiichi, additional, Hoshinaga, Kiyotaka, additional, Yuzawa, Yukio, additional, and Suzuki, Atsushi, additional

Published
2019
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34. 924-P: Pattern Mining of Trajectories of Glucose Values of Continuous Glucose Monitoring System by Artificial Intelligence in Type 2 Diabetes Patients

Author

MAKINO, MASAKI, primary, YOSHIMOTO, RYO, additional, KONDO-ANDO, MIZUHO, additional, YOSHINO, YASUMASA, additional, HIRATSUKA, IZUMI, additional, MAKI, WAKAKO, additional, SEKIGUCHI-UEDA, SAHOKO, additional, KAKITA, AYAKO, additional, SHIBATA, MEGUMI, additional, SEINO, YUSUKE, additional, TAKAYANAGI, TAKESHI, additional, ONO, MASAKI, additional, KOSEKI, AKIRA, additional, KUDO, MICHIHARU, additional, HAIDA, KYOICHI, additional, YANAGIYA, RYOSUKE, additional, HAYAKAWA, NOBUKI, additional, and SUZUKI, ATSUSHI, additional

Published
2019
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