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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Wijayanti, Diana Urbaya, Satriawansyah Sitompul, Taufiq Adrian, Verry BINUS Graduate Program Bina Nusantara University Department of Information Systems Jakarta11480 Indonesia Program USAID Indonesia Jakarta12930 Indonesia University of Oslo Department of Informatics Oslo0316 Norway Binus Graduate Program - Doctor Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Provincial Health Office Center of Data and Information Technology Jakarta10160 Indonesia

ISBN: (纸本)9798350390025

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users. © 2024 IEEE.

关键词： e-government

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A general sample size framework for developing or updating a clinical prediction model

arXiv

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arXiv 2025年

作者： Riley, Richard D. Whittle, Rebecca Sadatsafavi, Mohsen Martin, Glen P. Pate, Alexander Collins, Gary S. Ensor, Joie Department of Applied Health Sciences School of Health Sciences College of Medicine and Health University of Birmingham Birmingham United Kingdom Birmingham Biomedical Research Centre United Kingdom Respiratory Evaluation Sciences Program Faculty of Pharmaceutical Sciences The University of British Columbia Vancouver Canada Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford OxfordOX3 7LD United Kingdom

Background Existing sample size calculations for developing clinical prediction models rely on standard (unpenalised) regression-based formula. A general approach is needed for developing or updating a CPM using any statistical or machine learning method. Objectives To propose a general framework for sample size calculations applicable to any model development approach, based on drawing samples from anticipated sampling/posterior distributions and targeting minimal degradation in predictive performance compared to reference model. Methods Researchers must provide candidate predictors, their reference model (e.g., a regression equation matching outcome incidence, predictor weights and c-statistic of previous models), and a (synthetic) dataset reflecting the joint distribution of candidate predictors in the target population. Then, a fully simulation-based approach allows the impact of a chosen development sample size and modelling strategy to be examined. This generates thousands of models and, by applying each to the entire target population, leads to sampling/posterior distributions of individual predictions and model performance (degradation) metrics, to inform required sample sizes. To improve computational speed for penalised regression, we also propose a one-sample Bayesian analysis that combines shrinkage priors with a likelihood decomposed into sample size and Fisher’s unit information. Results The framework is illustrated when developing pre-eclampsia prediction models using logistic regression (unpenalised, uniform shrinkage, lasso or ridge) and random forests. It encompasses existing sample size calculation criteria whilst also providing model assurance probabilities, instability metrics, and degradation statistics about calibration, discrimination, clinical utility, prediction error and fairness. Crucially, the required sample size depends on the users’ key estimands of interest and planned model development or updating approach. Conclusions The new, flex

关键词： Size distribution

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Surgical Instrument Detection for Laparoscopic Surgery using Deep Learning 19

Surgical Instrument Detection for Laparoscopic Surgery using...

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19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Boonkong, Apiwat Hormdee, Daranee Sonsilphong, Suphachoke Khampitak, Kovit Khon Kaen University Embedded System RandD Group Computer Engineering Khon Kaen Thailand Khon Kaen University Data Science and Health Informatics Program Academic Affairs Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kaen Thailand

ISBN: (纸本)9781665485845

Laparoscopic surgery has transformed conventional open surgery. Robot-Assisted laparoscopic surgery which is minimally invasive is effective for operations in limited space. Nevertheless, the robotic system which is utilized in such surgery can inflict impair to the surgical instruments, organs, or tissues as a consequence of the narrow field of view and space available to operate, and insufficient tactile response. In laparoscopy, the surgeon observes what he operates via digital images which are displayed on a monitor. In addition to the surgeon's expertise, the image-guided surgery system could offer him significant support if that system is capable of understanding those images. Computer Vision has been able to proceed due to the emergence of Machine Learning, and especially Deep Learning. This paper applies Deep Learning for surgical instrument detection for laparoscopic surgery. The fastest model, based on results trained on the COCO 2017 dataset on TensorFlow Framework, for each of 4 algorithms-Faster-RCNN, SSD, CenterNet and EfficientDet-from Model Zoo, along with YOLOv4 on Darknet Framework, have been investigated in this work. The evaluation metrics used are Precision, Recall and F1-score. The results reveal that each model plays its part well;however, YOLOv4 seemed to breakthrough this statement by offering grate result for all three accuracy metrics. © 2022 IEEE.

关键词： Laparoscopy

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Yale at "Discharge Me!": Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information 23

Yale at "Discharge Me!": Evaluating Constrained Generation o...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Socrates, Vimig Huang, Thomas Ai, Xuguang Fereydooni, Soraya Chen, Qingyu Taylor, R. Andrew Chartash, David Department of Biomedical Informatics and Data Science Yale University School of Medicine United States Department of Emergency Medicine Yale School of Medicine United States Program of Computational Biology and Bioinformatics Yale University United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

ISBN: (纸本)9798891761308

In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: "Discharge Me!". We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.. ©2024 Association for Computational Linguistics.

关键词： Decoding

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Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes

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Journal of Medical Internet Research 2025年第1期27卷 e71873页

作者： Huang, Feiqing Hou, Jue Zhou, Ningxuan Greco, Kimberly Lin, Chenyu Sweet, Sara Morini Wen, Jun Shen, Lechen Gonzalez, Nicolas Zhang, Sinian Liao, Katherine P. Cai, Tianrun Xia, Zongqi Bourgeois, Florence T. Cai, Tianxi Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Harvard-MIT Center for Regulatory Science Harvard Medical School Boston MA United States Division of Biostatistics and Health Data Science School of Public Health University of Minnesota Minneapolis MN United States Department of Biomedical Informatics Harvard Medical School Boston MA United States Department of Engineering University of Toronto Toronto ON Canada Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Boston MA United States Department of Neurology University of Pittsburgh Pittsburgh PA United States Computational Health Informatics Program (CHIP) Boston Children's Hospital Boston MA United States Department of Pediatrics Harvard Medical School Boston MA United States

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction. ©Feiqing Huang, Jue Hou, Ningxuan Zhou, Kimberly Greco, Chenyu Lin, Sara Morini Sweet, Jun Wen, Lechen Shen, Nicolas Gonzalez, Sinian Zhang, Katherine P Liao, Tianrun Cai, Zongqi Xia, Florence T Bourgeois, Tianxi Cai.

关键词： calibration causal inference chronic disease outcomes data imputation electronic health records longitudinal disease activity machine learning real-world evidence

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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M3D: Dual-Stream Selective State Spaces and Depth-Driven Framework for High-Fidelity Single-View 3D Reconstruction

arXiv

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arXiv 2024年

作者： Zhang, Luoxi Shrestha, Pragyan Zhou, Yu Xie, Chun Kitahara, Itaru Doctoral Program in Empowerment Informatics University of Tsukuba Japan Center for Computational Science University of Tsukuba Japan Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University China

The precise reconstruction of 3D objects from a single RGB image in complex scenes presents a critical challenge in virtual reality, autonomous driving, and robotics. Existing neural implicit 3D representation methods face significant difficulties in balancing the extraction of global and local features, particularly in diverse and complex environments, leading to insufficient reconstruction precision and quality. We propose M3D, a novel single-view 3D reconstruction framework, to tackle these challenges. This framework adopts a dual-stream feature extraction strategy based on Selective State Spaces to effectively balance the extraction of global and local features, thereby improving scene comprehension and representation precision. Additionally, a parallel branch extracts depth information, effectively integrating visual and geometric features to enhance reconstruction quality and preserve intricate details. Experimental results indicate that the fusion of multi-scale features with depth information via the dual-branch feature extraction significantly boosts geometric consistency and fidelity, achieving state-of-the-art reconstruction performance. Our code and dataset are publicly available at this URL. © 2024, CC BY-NC-ND.

关键词： 3D reconstruction

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Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces

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Journal of Genetics and Genomics 2023年第4期50卷 241-252页

作者： June-Sik Kim Kotaro Takahagi Komaki Inoue Minami Shimizu Yukiko Uehara-Yamaguchi Asaka Kanatani Daisuke Saisho Ryuei Nishii Alexander ELipka Takashi Hirayama Kazuhiro Sato Keiichi Mochida Bioproductivity Informatics Research Team RIKEN Center for Sustainable Resource ScienceYokohama 230-0045Japan Institute of Plant Science and Resources Okayama UniversityKurashiki 710-0046Japan Kihara Institute for Biological Research Yokohama City UniversityYokohama 244-0813Japan Graduate School of Nanobioscience Yokohama City UniversityYokohama 236-0027Japan School of Information and Data Sciences Nagasaki UniversityNagasaki 852-8521Japan Department of Crop Sciences University of IllinoisUrbanaIL 61801USA Microalgae Production Control Technology Laboratory RIKEN Baton Zone ProgramRIKEN Cluster for ScienceTechnology and Innovation HubYokohama 230-0045Japan

Barley(Hordeum vulgare ***)is one of the first crops to be domesticated and is adapted to a wide range of *** barley germplasm collections possess valuable allelic variations that could further improve barley *** barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits,polymorphisms from East Asian varieties remain *** this study,we analyze exome polymorphisms in a panel of 274 barley varieties collected worldwide,including 137 varieties from East Asian countries and *** reveal the underlying population structure and conduct genome-wide association studies for 10 agronomic ***,we examin genome-wide associations for traits related to grain size such as awn length and glume *** results demonstrate the value of diverse barley germplasm panels containing Eastern varieties,highlighting their distinct genomic signatures relative to Western subpopulations.

关键词： Barley Exome-seq Genome-wide association study(GWAS) Population structure Allelic variation

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Whole slide images are 2D point clouds: Context-aware survival prediction using patch-based graph convolutional networks

arXiv

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arXiv 2021年

作者： Chen, Richard J. Lu, Ming Y. Shaban, Muhammad Chen, Chengkuan Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Department of Biomedical Informatics Harvard Medical School Cancer Data Science Program Dana-Farber Cancer Institute Cancer Program Broad Institute of Harvard MIT

Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Despite the advancements made in weakly-supervised deep learning, many approaches are not context-aware and are unable to model important morphological feature interactions between cell identities and tissue types that are prognostic for patient survival. In this work, we present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features to model local- and global-level topological structures in the tumor microenvironment. We validate Patch-GCN with 4,370 gigapixel WSIs across five different cancer types from the Cancer Genome Atlas (TCGA), and demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%. Our code and corresponding models are publicly available at https://***/mahmoodlab/Patch-GCN. Copyright © 2021, The Authors. All rights reserved.

关键词： Pathology

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