Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2506.10269

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Publishing type：Research paper (scientific journal)  

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Other Link： https://dblp.uni-trier.de/db/journals/tmlr/tmlr2025.html#SannaiHKTH25

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.5220/0013118300003893

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Other Link： https://dblp.uni-trier.de/db/conf/icores/icores2025.html#KoguchiNKKNK25

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1021/jacsau.4c01189

Web of Science

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Publishing type：Research paper (international conference proceedings)  

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Other Link： https://dblp.uni-trier.de/rec/conf/aistats/2025

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/BigData62323.2024.10825549

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2024.html#NagaiIOAMMKN24

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-981-96-0125-7_35

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Other Link： https://dblp.uni-trier.de/db/conf/pricai/pricai2024-4.html#NishimuraKN24

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-981-96-0116-5_20

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Other Link： https://dblp.uni-trier.de/db/conf/pricai/pricai2024-1.html#YamaoKKTIN24

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：OpenReview.net  

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Other Link： https://dblp.uni-trier.de/rec/conf/icml/2024

Publishing type：Research paper (international conference proceedings)  

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Other Link： https://dblp.uni-trier.de/rec/conf/iclr/2024

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-031-58113-7_14

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Other Link： https://dblp.uni-trier.de/db/conf/iciea/iciea2024.html#MizutaniUUOHHKN24

Language：English   Publishing type：Research paper (international conference proceedings)  

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Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.ejor.2023.01.037

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Other Link： https://dblp.uni-trier.de/db/journals/corr/corr2112.html#abs-2112-12454

Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2304.14606

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Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2307.10204

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Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2203.15292

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Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3512290.3528778

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Other Link： https://dblp.uni-trier.de/db/conf/gecco/gecco2022.html#TanabeAKUSH22

Publishing type：Research paper (international conference proceedings)   Publisher：PMLR  

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Other Link： https://dblp.uni-trier.de/rec/conf/aistats/2022

Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2205.11099

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<title>Abstract</title>Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious. Among the 206 subjects from the second cohort, 42 were categorized as delirious. The areas under the curve (AUCs) based on BSEEG score were 0.72 (1st cohort, Fp1-A1), 0.76 (1st cohort, Fp2-A2), and 0.67 (2nd cohort). AUCs from TDA were much higher at 0.82 (1st cohort, Fp1-A1), 0.84 (1st cohort, Fp2-A2), and 0.78 (2nd cohort). When sensitivity was set to be 0.80, the TDA drastically improved specificity to 0.66 (1st cohort, Fp1-A1), 0.72 (1st cohort, Fp2-A2), and 0.62 (2nd cohort), compared to 0.48 (1st cohort, Fp1-A1), 0.54 (1st cohort, Fp2-A2), and 0.46 (2nd cohort) with BSEEG. BSEEG has the potential to detect delirium, and TDA is helpful to improve the performance.

DOI： 10.1038/s41598-020-79391-y

PubMed

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Other Link： http://www.nature.com/articles/s41598-020-79391-y

Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s10898-021-01048-5

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Other Link： https://link.springer.com/article/10.1007/s10898-021-01048-5/fulltext.html

Publishing type：Research paper (scientific journal)   Publisher：Royal College of Psychiatrists  

<sec id="S000712502100101X_sec_a1">
<title>Background</title>
We have developed the bispectral electroencephalography (BSEEG) method for detection of delirium and prediction of poor outcomes.


</sec>
<sec id="S000712502100101X_sec_a2">
<title>Aims</title>
To improve the BSEEG method by introducing a new EEG device.


</sec>
<sec id="S000712502100101X_sec_a3" sec-type="methods">
<title>Method</title>
In a prospective cohort study, EEG data were obtained and BSEEG scores were calculated. BSEEG scores were filtered on the basis of standard deviation (s.d.) values to exclude signals with high noise. Both non-filtered and s.d.-filtered BSEEG scores were analysed. BSEEG scores were compared with the results of three delirium screening scales: the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the Delirium Rating Scale-Revised-98 (DRS) and the Delirium Observation Screening Scale (DOSS). Additionally, the 365-day mortalities and the length of stay (LOS) in the hospital were analysed.


</sec>
<sec id="S000712502100101X_sec_a4" sec-type="results">
<title>Results</title>
We enrolled 279 elderly participants and obtained 620 BSEEG recordings; 142 participants were categorised as BSEEG-positive, reflecting slower EEG activity. BSEEG scores were higher in the CAM-ICU-positive group than in the CAM-ICU-negative group. There were significant correlations between BSEEG scores and scores on the DRS and the DOSS. The mortality rate of the BSEEG-positive group was significantly higher than that of the BSEEG-negative group. The LOS of the BSEEG-positive group was longer compared with that of the BSEEG-negative group. BSEEG scores after s.d. filtering showed stronger correlations with delirium screening scores and more significant prediction of mortality.


</sec>
<sec id="S000712502100101X_sec_a5" sec-type="conclusions">
<title>Conclusions</title>
We confirmed the usefulness of the BSEEG method for detection of delirium and of delirium severity, and prediction of patient outcomes with a new EEG device.


</sec>

DOI： 10.1192/bjp.2021.101

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Artificial Intelligence  

DOI： 10.1527/tjsai.36-4_b-l13

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Publishing type：Research paper (scientific journal)  

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Other Link： https://dblp.uni-trier.de/db/journals/corr/corr2012.html#abs-2012-11782

Publishing type：Research paper (international conference proceedings)   Publisher：AAAI Press  

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Other Link： https://dblp.uni-trier.de/rec/conf/aaai/2021

Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science ({PLoS})  

This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of forecasts for corresponding lower-level time series. Previous methods for making coherent forecasts consist of two phases: first computing base (incoherent) forecasts and then reconciling those forecasts based on their inherent hierarchical structure. To improve time series predictions, we propose a structured regularization method for completing both phases simultaneously. The proposed method is based on a prediction model for bottom-level time series and uses a structured regularization term to incorporate upper-level forecasts into the prediction model. We also develop a backpropagation algorithm specialized for applying our method to artificial neural networks for time series prediction. Experimental results using synthetic and real-world datasets demonstrate that our method is comparable in terms of prediction accuracy and computational efficiency to other methods for time series prediction.

DOI： 10.1371/journal.pone.0242099

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Publishing type：Research paper (international conference proceedings)   Publisher：International Joint Conferences on Artificial Intelligence Organization  

DOI： 10.24963/ijcai.2020/395

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ijcai/ijcai2020.html#KanamoriTKA20

Publishing type：Research paper (scientific journal)   Publisher：Association for the Advancement of Artificial Intelligence (AAAI)  

The B&amp;apos;ezier simplex fitting is a novel data modeling technique which utilizes geometric structures of data to approximate the Pareto set of multi-objective optimization problems. There are two fitting methods based on different sampling strategies. The inductive skeleton fitting employs a stratified subsampling from skeletons of a simplex, whereas the all-at-once fitting uses a non-stratified sampling which treats a simplex as a single object. In this paper, we analyze the asymptotic risks of those B&amp;apos;ezier simplex fitting methods and derive the optimal subsample ratio for the inductive skeleton fitting. It is shown that the inductive skeleton fitting with the optimal ratio has a smaller risk when the degree of a B&amp;apos;ezier simplex is less than three. Those results are verified numerically under small to moderate sample sizes. In addition, we provide two complementary applications of our theory: a generalized location problem and a multi-objective hyper-parameter tuning of the group lasso. The former can be represented by a B&amp;apos;ezier simplex of degree two where the inductive skeleton fitting outperforms. The latter can be represented by a B&amp;apos;ezier simplex of degree three where the all-at-once fitting gets an advantage.

DOI： 10.1609/aaai.v34i03.5622

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10589-019-00153-2

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/icip.2019.8803605

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Publishing type：Research paper (scientific journal)   Publisher：Association for the Advancement of Artificial Intelligence (AAAI)  

Multi-objective optimization problems require simultaneously optimizing two or more objective functions. Many studies have reported that the solution set of an M-objective optimization problem often forms an (M − 1)-dimensional topological simplex (a curved line for M = 2, a curved triangle for M = 3, a curved tetrahedron for M = 4, etc.). Since the dimensionality of the solution set increases as the number of objectives grows, an exponentially large sample size is needed to cover the solution set. To reduce the required sample size, this paper proposes a Bézier simplex model and its fitting algorithm. These techniques can exploit the simplex structure of the solution set and decompose a high-dimensional surface fitting task into a sequence of low-dimensional ones. An approximation theorem of Bézier simplices is proven. Numerical experiments with synthetic and real-world optimization problems demonstrate that the proposed method achieves an accurate approximation of high-dimensional solution sets with small samples. In practice, such an approximation will be conducted in the postoptimization process and enable a better trade-off analysis.

DOI： 10.1609/aaai.v33i01.33012304

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Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s10898-018-0713-3

Scopus

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Other Link： http://link.springer.com/content/pdf/10.1007/s10898-018-0713-3.pdf

Publishing type：Research paper (scientific journal)   Publisher：The Operations Research Society of Japan  

DOI： 10.15807/jorsj.60.321

Scopus

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