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Your search keyword '"Iwamori, Toshiya"' showing total 6 results
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6 results on '"Iwamori, Toshiya"'

1. Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction

Author

Katsuki, Takayuki, Miyaguchi, Kohei, Koseki, Akira, Iwamori, Toshiya, Yanagiya, Ryosuke, and Suzuki, Atsushi

Subjects
Computer Science - Machine Learning
Abstract

We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them., Comment: To be published in IJCAI-22

Published
2022
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3. Deep convolutional neural network-based algorithm for muscle biopsy diagnosis

Author

Kabeya, Yoshinori, Okubo, Mariko, Yonezawa, Sho, Nakano, Hiroki, Inoue, Michio, Ogasawara, Masashi, Saito, Yoshihiko, Tanboon, Jantima, Indrawati, Luh Ari, Kumutpongpanich, Theerawat, Chen, Yen-Lin, Yoshioka, Wakako, Hayashi, Shinichiro, Iwamori, Toshiya, Takeuchi, Yusuke, Tokumasu, Reitaro, Takano, Atsushi, Matsuda, Fumihiko, and Nishino, Ichizo

Published
2022
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4. 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

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

Author

Ghalwash M, Koseki A, Iwamori T, Kudo M, and Meyer P

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., (©2023 AMIA - All rights reserved.)

Published
2023

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