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Pervasive Artificial Intelligence (ICPAI), International Conference on

作者： Ping-Sung Cheng Meng-Hsiun Tsai Chung-Hao Hsueh Sheng Kuang Wu National Chung Hsing University Taichung City Taiwan AI and Data Science Program Institute of Genomic and Boioinformatics National Chung Hsing University Taichung City Taiwan National Taiwan University of Sport Taichung City Taiwan

ISBN: (数字)9781665404839

ISBN: (纸本)9781665404846

This study aims to provide a method to quantify the stress level with numerical EEG values, identify key features of brainwave and assess the level of stress for table tennis players. The data of College's Division 1 and Division 2 are collected and analyzed by the decision tree algorithms C4.5, CART, Random Forest and Random Tree. The result of Random Forest obtains the highest accuracy rate among each algorithm, which is 79.21% in all players, 79.3% in Division 1, and 80.68% in Division 2. According to the result of decision trees, the top attribute of the Division 1 players was Theta wave, which was different from the result of other data in the Division 2 players. Also reveal the deference of brainwaves between the Division 2 players and the Division 1 players while they are in high stressed competitions.

关键词： Stress Electroencephalography Physiology Sports Games Decision trees Vegetation

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2019年第4期46卷

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging 23rd

The Constrained Network-Based Statistic: A New Level of Infe...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Noble, Stephanie Scheinost, Dustin Department of Radiology and Biomedical Imaging Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States Interdepartmental Neuroscience Program Yale University New HavenCT United States Child Study Center Yale University New HavenCT United States

ISBN: (纸本)9783030597276

Neuroimaging research aimed at dissecting the network organization of the brain is poised to flourish under major initiatives, but converging evidence suggests more accurate inferential procedures are needed to promote discovery. Inference is typically performed at the cluster level with a network-based statistic (NBS) that boosts power by leveraging known dependence within the local neighborhood. However, existing NBS methods overlook another important form of dependence—shared membership in large-scale brain networks. Here, we propose a new level of inference that pools information within predefined large-scale networks: the Constrained Network-Based Statistic (cNBS). We evaluated sensitivity and specificity of cNBS against existing standard NBS and threshold-free NBS by resampling task data from the largest openly available fMRI database: the Human Connectome Project. cNBS was most sensitive to effect sizes below medium, which accounts for the majority of ground truth effects. In contrast, threshold-free NBS was most sensitive to higher effect sizes. Ground truth maps showed grouping of effects within large-scale networks, supporting the relevance of cNBS. All methods controlled FWER as intended. In summary, cNBS is a promising new level of inference for promoting more valid inference, a critical step towards more reproducible discovery in neuroscience. © 2020, Springer Nature Switzerland AG.

关键词： Statistics

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data Engineering Pipeline to Analyse Jakarta's Air Quality during COVID-19-Caused Lockdown Periods

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Reza Rahutomo Bens Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Jakarta lifted up lockdown after passing more than 50 days of large-scale social activity restriction and initiated phase opening to new normal. To analyse Jakarta's air quality after passing lockdown, a pipeline of data engineering is needed. By acquiring time series data from ***, a time-series database system is developed with Python programming language and its fundamental libraries namely Pandas, NumPy, SQLite. After PM 2.5 data are pre-processed into average per-hour and grouped by applicable periods (pre-lockdown, lockdown, and phase opening), a pattern of PM 2.5 in South Jakarta is revealed by using data visualization library Matplotlib. The apex of PM 2.5 occurs earlier during lockdown (04:00) and phase opening (02:00) rather than when it was normal or pre-lockdown (08:00) even though the nadir of PM2.5 still occurs at the same time (16:00 – 17:00).

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Patient-level Classification on Clinical Note Sequences Guided by Attributed Hierarchical Attention

Patient-level Classification on Clinical Note Sequences Guid...

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IEEE International Conference on Big data

作者： Cansu Sen Thomas Hartvigsen Xiangnan Kong Elke Rundensteiner Computer Science Department Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

In spite of clinical notes in Electronic Health Records (EHR) providing abundant information about patient health, effective modeling of clinical notes remains in its infancy. A patient's clinical notes correspond to a sequence of free-form texts generated by health care professionals over time; with each note in turn containing a sequence of words. Additionally, notes are accompanied by external attributes at multiple layers such as the time at which each note was created (note level) or the demographics of the patient (patient level). Thus, EHR notes correspond to a nested structure of text sequences augmented with external multi-layer attributes. To model this complex problem, we propose an Attributed Hierarchical Attention model, named HAC-RNN, that integrates multiple RNN layers that encode nested sequential notes with contextual and temporal attention layers that are conditioned on the external attributes. While the bottom layer of HAC-RNN is responsible for contextual summarization of the note content, the top layer combs through the entire timeline of notes to focus on those which are most relevant. These attention layers, which are each conditioned on layer-specific hierarchical attributes, allow personalized predictions through inferring patient *** evaluate HAC-RNN using three real-world medical tasks, detecting in-hospital acquired infections and predicting patient mortality using critical care database MIMIC-III. Our results demonstrate that our model significantly outperforms state-of-the-art techniques for all tasks.

关键词： Task analysis Predictive models Medical services Medical diagnostic imaging Feature extraction Computer science Context modeling

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Evaluating a mobile app’s effects on depression and anxiety in medication-treated opioid use disorder

npj Mental Health Research

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npj Mental Health Research 2024年第1期3卷 1-14页

作者： Heinz, Michael V. Lekkas, Damien Abreu, Veronica Lee, Camilla Marsch, Lisa A. Jacobson, Nicholas C. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College Lebanon NH United States Department of Psychiatry Geisel School of Medicine Dartmouth College Hanover NH United States Quantitative Biomedical Sciences Program Dartmouth College Hanover NH United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College Lebanon NH United States

Depression and anxiety frequently co-occur with opioid use disorder (OUD) yet are often overlooked in standard OUD treatments. This study evaluated the feasibility, acceptability, and preliminary effectiveness of a mobile application designed to address these symptoms in individuals receiving medications for OUD (MOUD). A randomized controlled trial recruited N = 63 adults with OUD who received MOUD and screened positive for moderate depression or generalized anxiety. Participants were randomized to an app-based digital intervention or treatment-as-usual for 4 weeks, and completed follow-ups at 4 and 8 weeks. Primary outcomes were self-reported severity measures for depression and generalized anxiety, and urine drug screens (UDS). Secondary outcomes included self-reported OUD severity, craving intensity, and digital biomarkers derived from passive smartphone sensors. The application was well-received (median app rating = 4/5 stars). The intervention group showed significant reductions in depressive and generalized anxiety symptoms post-intervention and at 8 weeks follow-up (d > 0.70), with large (d = 0.78) and moderate (d = 0.38) effect sizes, respectively, compared to controls. Both groups exhibited substantial decreases in self-reported severity of opioid use symptoms (d > 2.50). UDS suggested similar between-group adherence to MOUD, with a marginal decrease in opioid (MOP) use in the intervention group and increase in controls, yielding medium between group effect sizes (d = 0.44). Passive sensor data suggested significant increases in social connectedness in the intervention group, evidenced by a significant rise in incoming and outgoing calls and text connections. Initial evidence supports the feasibility and acceptability of a digital intervention for treating anxiety and depressive symptoms in persons receiving MOUD. While underpowered to confidently determine statistical significance beyond directionality, the intervention showed promise in reducing depressi

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Learning Temporal Relevance in Longitudinal Medical Notes

Learning Temporal Relevance in Longitudinal Medical Notes

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IEEE International Conference on Big data

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Clinical notes correspond to sequences of reports about patients written over time by health-care professionals. With the availability of massive Electronic Health Record (EHR) datasets composed of such clinical notes, machine learning models on these rich text data series are being developed for patient outcome prediction from infection diagnosis to mortality. While current models focus on content in these unstructured clinical notes, we postulate that timing of the medical events that are explained in the notes are equally crucial. We thus propose a novel attention mechanism composed of dual-attention blocks based on a rich diversity of time representations. We then pair this mechanism with an LSTM, resulting in our proposed time-aware recurrent network TEND-LSTM. TEND-LSTM learns an integrated set of attention weights, with the first attention based on the content of the clinical notes and the second based on when the notes were taken. Together, they are combined using a deep-attention network layer. The proposed dual attention mechanism not only learns a function of time incorporating different aspects of the temporal nature of note instances but also automatically finds a balance between how much attention to put on content versus time. We evaluate our model on six medical tasks using data sets from the publicly-available MIMIC III database from the Beth Israel Deaconess Medical Center. Our results demonstrate that TEND-LSTM outperforms state-of-the-art methods by a large margin.

关键词： Task analysis Medical diagnostic imaging Predictive models Timing Indexes Neural networks Computer science

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Ecocommons Australia Virtual Laboratories with Cloud Computing: Meeting Diverse User Needs for Ecological Modeling and Decision-Making

SSRN

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SSRN 2024年

作者： Bayraktarov, Elisa Low-Choy, Samantha Singh, Abhimanyu Raj Beaumont, Linda J. Williams, Kristen J. Baumgartner, John Laffan, Shawn W. Vasco, Daniela Cosgrove, Robert Wraith, Jenna Antunes, Jessica Fenker Mackey, Brendan EcoCommons Australia Research Specialized and Data Foundations Griffith University NathanQLD Australia Centre for Planetary Health and Food Security Griffith University Nathan Campus Brisbane Australia Griffith Institute of Educational Research Griffith University Mount Gravatt Campus Brisbane Australia School of Natural Sciences Macquarie University SydneyNSW Australia Commonwealth Scientific and Industrial Research Organisation Environment Australian Capital Territory Canberra Australia Centre of Excellences for Biosecurity Risk Analysis University of Melbourne MelbourneVIC Australia Earth and Sustainability Science Research Center School of Biological Earth and Environmental Sciences University of New South Wales Sydney Australia Biosecurity Commons Research Specialized and Data Foundations Griffith University Gold CoastQLD Australia Australian Rivers Institute Griffith University NathanQLD Australia Museums Victoria MelbourneVIC Australia Griffith Climate Change Response Program Griffith University Gold Coast Campus Parklands Drive SouthportQLD Australia Climate Action Beacon Griffith University Gold Coast Campus Parklands Drive SouthportQLD Australia

Biodiversity decline and climate change are among the most important environmental issues society faces. Information to address these issues has benefited from increasing big data, advances in cloud computing, and subsequent new tools for analytics. Accessing such tools is streamlined by virtual laboratories for ecological analysis, like the ‘Biodiversity and Climate Change Virtual Laboratory’ (BCCVL) and ‘ecocloud’. These platforms help reduce time and effort spent on developing programming skills, data acquisition and curation, plus model building. Recently this functionality was extended, producing EcoCommons Australia—a web-based ecological modeling platform for environmental problem-solving—with upgraded infrastructure and improved ensemble modeling, post-model analysis, workflow transparency and reproducibility. We outline our user-centered approach to systems design, from initial surveys of stakeholder needs to user involvement in testing, and collaboration with specialists. We illustrate EcoCommons through four use cases, highlighting how the modular platform meets users’ needs. © 2024, The Authors. All rights reserved.

关键词： Climate change

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Waiting-time paradox in 1922

arXiv

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arXiv 2020年

作者： Masuda, Naoki Hiraoka, Takayuki Department of Mathematics University at Buffalo State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States Department of Computer Science Aalto University Espoo00076 Finland

We present an English translation and discussion of an essay that a Japanese physicist, Torahiko Terada, wrote in 1922. In the essay, he described the waiting-time paradox, also called the bus paradox, which is a known mathematical phenomenon in queuing theory, stochastic processes, and modern temporal network analysis. He also observed and analyzed data on Tokyo City trams to verify the relevance of the waiting-time paradox to busy passengers in Tokyo at the time. This essay seems to be one of the earliest documentations of the waiting-time paradox in a sufficiently scientific manner. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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Correction: Spatial organization of B lymphocytes and prognosis prediction in patients with gastric cancer

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Gastric Cancer 2025年第4期28卷 729-730页

作者： Tay, Ryan Yong Kiat Sachdeva, Manavi Ma, Haoran Kim, Young-Woo Kook, Myeong-Cherl Kim, Hyunki Cheong, Jae-Ho Hewitt, Lindsay C. Nekolla, Katharina Schmidt, Günter Yoshikawa, Takaki Oshima, Takashi Arai, Tomio Srivastava, Supriya Teh, Ming Ong, Xuewen Tay, Su Ting Sheng, Taotao Zhao, Joseph J. Tan, Patrick Grabsch, Heike I. Sundar, Raghav Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Haematology-Oncology National University Cancer Institute National University Hospital Singapore Singapore Cancer and Stem Cell Biology Program Duke-NUS Medical School Singapore Singapore Department of Cancer Policy and Population Health National Cancer Center Graduate School of Cancer Science and Policy and Center for Gastric Cancer and Department of Surgery National Cancer Center Goyang Republic of Korea Center for Gastric Cancer Department of Pathology National Cancer Center Goyang Republic of Korea Department of Pathology Yonsei University College of Medicine Seoul Republic of Korea Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea Department of Pathology GROW Research Institute for Oncology and Reproduction Maastricht University Medical Center+ Maastricht The Netherlands Department of Precision Medicine GROW School for Oncology and Reproduction Maastricht University Center+ Maastricht The Netherlands Computational Pathology Oncology R&D AstraZeneca Munich Germany Department of Surgery Kanagawa Cancer Center Yokohama Japan Department of Surgery Yokohama City University Yokohama Japan Department of Surgery Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital Tokyo Japan Department of Pathology Tokyo Metropolitan Institute for Geriatrics and Gerontology Tokyo Japan Department of Medicine National University of Singapore Singapore Singapore Department of Pathology National University Hospital Singapore Singapore Genome Institute of Singapore Agency for Science Technology and Research Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Cellular and Molecular Research National Cancer Centre Singapore Singapore Pathology and Data Analytics Leeds Institute of Medical Research at St. James’S University of Leeds Leeds UK

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