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arXiv 2024年

作者： Suzuki, Takashi Tsuchiya, Takuya Center for Mathematical Modeling and Data Science Osaka University Japan

We study Hadamard's variational formula for simple eigenvalues under dynamical and conformal deformations. Particularly, harmonic convexity of the first eigenvalue of the Laplacian under the mixed boundary condition is established for two-dimensional domain, which implies several new *** Codes 35J25, 35R35 © 2024, CC BY-NC-ND.

关键词： Perturbation techniques

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Methods of mathematical oncology—Study of medicine using mathematics

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Sugaku Expositions 2024年第2期37卷 227-244页

作者： Suzuki, Takashi Center for Mathematical Modeling and Data Science Osaka University Machikaneyamacyo 1-3 Toyonakashi 560-8531 Japan

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Hadamard variation of eigenvalues with respect to general domain perturbations

arXiv

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arXiv 2023年

作者： Suzuki, Takashi Tsuchiya, Takuya Center of Mathematical Modeling and Data Science Osaka University Toyonaka560-8531 Japan Center of Mathematical Modeling and Data Science Osaka University Toyonaka560-8531 Japan

We study Hadamard variation of eigenvalues of Laplacian with respect to general domain perturbations. We show their existence up to the second order rigorously and characterize the derivatives, using associated eigenvalue problems in finite dimensional spaces. Then smooth rearrangement of multiple eigenvalues is explicitly given. This result follows from an abstract theory, applicable to general perturbations of symmetric bilinear *** Codes 35J25, 25R35 © 2023, CC BY-NC-ND.

关键词： Eigenvalues and eigenfunctions

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Pre-trained language models in Spanish for health insurance coverage 5

Pre-trained language models in Spanish for health insurance ...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Aracena, Claudio Rodríguez, Nicolás Rocco, Victor Dunstan, Jocelyn Faculty of Physical and Mathematical Sciences University of Chile Chile Millennium Institute Foundational Research on Data Chile Chilean Safety Association Chile Department of Computer Science Catholic University of Chile Chile Institute for Mathematical and Computational Engineering Catholic University of Chile Chile Center for Mathematical Modeling University of Chile Chile

ISBN: (纸本)9781959429883

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pretrained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language. © 2023 Association for Computational Linguistics.

关键词： Health insurance

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LinkMed: Entity Recognition and Relation Extraction from Clinical Notes in Spanish

LinkMed: Entity Recognition and Relation Extraction from Cli...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Center for Mathematical Modeling Universidad de Chile Chile Millenium Institute Foundational Research on Data Chile

Relation extraction is an essential component of Natural Language Processing (NLP) and significantly influences information retrieval and structured information extraction. Within clinical notes, the task is needed to establish connections among illnesses, therapies, indications, and other medical concepts. Motivated by the above, in this work, we propose a two-step model approach for entity linking;in the first step, we solve entity recognition, and in the second, a relation classification approach. We evaluated our approach in a Spanish corpus of the TESTLINK challenge in IberLEF2023 (Iberian Languages Evaluation Forum), comprising 81 clinical notes to train and 80 clinical notes to test. Our results show competitive performance with a precision of 0.47, recall of 0.43, and F1-score of 0.45, presenting an effective strategy for relation extraction from clinical notes in Spanish. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Information retrieval

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CMM PLN at MentalRiskES: A Traditional Machine Learning Approach for Detection of Eating Disorders and Depression in Chat Messages

CMM PLN at MentalRiskES: A Traditional Machine Learning Appr...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Guerra, Rodrigo Pizarro, Benjamín Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Dunstan, Jocelyn Center for Mathematical Modeling Universidad de Chile Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile National Center for Artificial Intelligence Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Millenium Institute Foundational Research on Data Chile

This paper describes our approaches to solving the MentalRiskES task, which belongs to the IberLEF (Iberian Languages Evaluation Forum) shared task. The task aims to identify the eating disorders and depression of a user using a series of Telegram messages. Our proposed system uses the traditional TFiDF method to represent the messages and then utilizes these representations as input for machine learning models. The best results for classification were obtained using the Naive Bayes classifier, while in the regression task, the best models were Gradient Boots Regressor and Linear Regressor. Despite its simplicity, we demonstrated that our traditional approaches can still achieve competitive results in recent NLP tasks, obtaining the best results in the case of detecting depression and eating disorders. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Natural language processing systems

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Automatic Section Classification in Spanish Clinical Narratives Using Chunked Named Entity Recognition

Automatic Section Classification in Spanish Clinical Narrati...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Carvallo, Andrés Rojas, Matías Muñoz-Castro, Carlos Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Santiago Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Santiago Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile Institute for Mathematical Computing Pontificia Universidad Católica de Chile Santiago Chile Center for Mathematical Modeling Santiago Chile Millenium Institute Foundational Research on Data Santiago Chile

The extraction and classification of important information from Spanish Electronic Clinical Narratives (ECNs) can be challenging due to the complexity of the clinical text and the limited availability of labeled data. In this paper, we introduce a chunked Named Entity Recognition model designed to parse and classify sections of ECNs into predefined categories. The model aims to improve section identification and classification accuracy within ECNs in the context of the IberLEF ClinAIS Task. Our system achieves a promising performance, obtaining a weighted B2 score of.6958, demonstrating its capability to accurately distinguish borders and boundaries between sections. The paper concludes with a comprehensive analysis of the results, discussing potential implications and suggesting directions for further improvements in clinical text analysis. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Classification (of information)

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Distance-Matrix-Based Extraction of Motility Features from Functionally Heterogeneous Cell Populations

Distance-Matrix-Based Extraction of Motility Features from F...

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International Symposium on Fusion of Mathematics and Biology, 2020

作者： Nakamura, Naotoshi Yamada, Ryo Center for Mathematical Modeling and Data Science Osaka University Toyonaka Japan Department of Statistical Genetics Center for Genomic Medicine Kyoto University Kyoto Japan

ISBN: (纸本)9789811648656

It has recently been recognized that seemingly identical cell populations can exhibit functional heterogeneity in vivo. However, the unsupervised extraction of features to understand such heterogeneous cell behaviors has been a challenging task. Here, we present a novel, data-driven method to visualize cell heterogeneity as a set of points in a low-dimensional Euclidean space, based on a distance matrix between individual cells. The axes of this space serve as a guide for finding the characteristic features in the population. By using cell motility as an example, we show that our visualization can distinguish three types of simulated cell movements as separate clusters, without knowing a priori the mathematical models they follow. By applying our method to time-lapse two-photon imaging data of neutrophils, we successfully extract critical features that characterize different types of cell motility. We expect that our method would be applicable to other cellular phenotypes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Extraction

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Differences of communication activity and mobility patterns between urban and rural people

arXiv

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arXiv 2023年

作者： Ogushi, Fumiko Roy, Chandreyee Kaski, Kimmo Center for Mathematical Modeling and Data Science Osaka University Japan Department of Computer Science Aalto University School of Science Espoo Finland The Alan Turing Institute London United Kingdom

Human mobility and other social activity patterns influence various aspects of society such as urban planning, traffic predictions, crisis resilience, and epidemic prevention. The behaviour of individuals, like their communication frequencies and movements, are shaped by societal and socio-economic factors. In addition, the differences in the geolocation of people as well as their gender and age cast effects on their activity patterns. In this study we focus on investigating these patterns by using mobile phone data, specifically the call detail records (CDRs), to analyze the social communication and mobility patterns of people. This dataset can provide us insight into the individual and population-level behaviours in rural and urban environments on a daily, weekly and seasonal basis. The results of our analyses show that in the urban areas people have high calling activity but low mobility, while in the rural areas they show the opposite behaviour, i.e. low calling activity combined with high mobility. Overall, there is a decreasing trend in people’s mobility through the year even though their calling activity remained consistent except for the holidays during which time the communication frequency drops markedly. We have also observed that there are significant differences in the mobility between the work days and free days. Finally, the age and gender of individuals have also been observed to play a role in the seasonal patterns differently in urban and rural areas. Copyright © 2023, The Authors. All rights reserved.

关键词： Rural areas

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Omics data Analysis Tools for Biomarker Discovery and the Tutorial

Omics Data Analysis Tools for Biomarker Discovery and the Tu...

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International Symposium on Fusion of Mathematics and Biology, 2020

作者： Nojima, Yosui Takeda, Yoshito Center for Mathematical Modeling and Data Science Osaka University Toyonaka Japan Department of Respiratory Medicine and Clinical Immunology Osaka University Graduate School of Medicine Suita Japan

ISBN: (纸本)9789811648656

Given the current progress in next-generation sequencing and mass spectrometry, considerable attention has been given to omics approaches and biomarker discovery to understand heterogeneous diseases. However, it is difficult to analyze them in the biological experimental community because the analysis processes are complicated. To address this problem, we have introduced and explained in this chapter the tools used for omics data analysis and how they are used. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Biomarkers

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