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Health Care Science 2023年第3期2卷 153-163页

作者： John Abisheganaden Kheng Hock Lee Lian Leng Low Eugene Shum Han Leong Goh Christine Gia Lee Ang Andy Wee An Ta Steven M.Miller Department of Respiratory and Critical Care Medicine Tan Tock Seng HospitalNational Healthcare GroupSingaporeSingapore National Working Group for the Hospital to Home Program SingaporeSingapore Department of Family Medicine and Continuing Care Singapore General HospitalSingHealth GroupSingaporeSingapore SingHealth Community Hospitals SingHealth GroupSingaporeSingapore Population Health and Integrated Care Office SingHealth GroupSingaporeSingapore Office of Community Development Changi General HospitalSingHealth GroupSingaporeSingapore Data Analytics and AI Department Integrated Health Information SystemsSingaporeSingapore School of Computing and Information Systems Singapore Management UniversitySingaporeSingapore

In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since *** this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 *** the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

关键词： hospital to home community care hospital to home lessons learned transitional care integrated care multiple readmissions AI prediction model machine learning in healthcare healthcare technology

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Development and Evaluation of a Process to Link Cancer Patients in the SEER Registries to National Medicaid Enrollment data

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Journal of the National Cancer Institute - Monographs 2020年第55期2020卷 89-95页

作者： Warren, Joan L. Benner, Suzie Stevens, Jennifer Enewold, Lindsey Huang, Bin Zhao, Lirong Tilahun, Negussie Bradley, Cathy J. Healthcare Assessment Research Branch Healthcare Delivery Research Program Division of Cancer Control and Population Science National Cancer Institute Bethesda MD United States Fu Associates Ltd Arlington VA United States Information Management Services Calverton MD United States Department of Biostatistics College of Public Health Markey Cancer Center University of Kentucky Lexington KY United States Division of Data Research and Analytic Methods Center for Medicare and Medicaid Innovation Centers for Medicare and Medicaid Services Baltimore MD United States Department of Health Systems Management and Policy School of Public Health University of Colorado Aurora CO United States

Cancer patients receiving Medicaid have worse prognosis. Patients in 14 Surveillance, Epidemiology, and End Results (SEER) cancer registries were linked to national Medicaid enrollment files, 2006-2013, to determine enrollment status during the year before and after diagnosis. A deterministic algorithm based on Social Security number, Medicare Health Insurance Claim number, sex, and date of birth was utilized. Results were compared with an independent linkage of Kentucky-based SEER and Medicaid data. A total 559 484 cancer cases were linked to national Medicaid enrollment files, representing 15-17% of persons with cancer yearly. About 60% of these cases were a complete match on all variables. There was 99% agreement on enrollment status compared with the Kentucky linked data. SEER data were successfully linked to national Medicaid enrollment data. NCI will make the linked data available to researchers, allowing for more detailed assessments of the impact Medicaid enrollment has on cancer diagnosis and outcomes. © 2020 Published by Oxford University Press. This work is written by US Government employees and is in the public domain in the US.

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AUTOMATED MAINTENANCE management FOR SMALL CRAFT

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NAVAL ENGINEERS JOURNAL 1992年第5期104卷 52-56页

作者： SETEROFF, SS VANHOOK, KC Sviatoslav Steve Seteroffholds a master of business administration degree from the University of Puget Sound and served in the United States Navy for more than 29 years. The last nine years were involved with service and small craft including operational maintenance administrative and training responsibilities within the United States and overseas. After leaving active duty Mr. Seteroff was involved in defining requirements to apply the U.S. Navy SNAP II program to Trident submarines incorporating SNAP I Intermediate Maintenance Management System—Real Time (IMMS-RT) processes into the OpNav 4790 procedures and supervised SNAP II hardware installations in the Pacific Fleet. He has conducted work estimating and cost estimating training at three naval shipyards and availability re-estimating tasks for the Navy Industrial Improvement Program and served as a project manager for information systems and logistics. Mr. Seteroff is currently the principal of Management & Logistics Associates providing professional services and analys to government and industry in the areas of management information systems logistics support analysis and integrated logistics support and is developing a logistics support analysis record data base application called LDS2B. Keith C. Van Hookserved for 27 years in the Naval Nuclear Propulsion Program specializing in submarine repair and quality assurance. He acted as a technical representative for the Department of Energy Division of Naval Reactors at Newport News Shipbuilding and Norfolk Naval Shipyard. Other assignments included production management assistant on board USSEmory S. Land (AS-39) and quality assurance officer at Trident Refit Facility Bangor Washington. After leaving active duty as a limited duty officer in 1989 he was a senior operations analyst for Merit Systems Inc. of Bremerton Washington consulting on marine maintenance management and shipyard management issues. He has conducted planning and estimating training and availability re-estimating services at

This paper deals with the need for a coordinated effort to develop a uniform, readily available PC-based maintenance management program for commands employing small craft in the execution of their mission. Included are: A discussion of the maintenance management problem as it applies to small craft. An evaluation of necessary capabilities of such a program by comparison with two existing programs which can currently satisfy some of these requirements. A review of the hardware, software and support requirements necessary to implement and maintain such a system. The importance of a coordinated and deliberate effort to standardize the essential elements of the program.

关键词： Maintenance

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Market Basket Analysis Using Apriori and FP-Growth for Analysis Consumer Expenditure Patterns at Berkah Mart in Pekanbaru Riau

Market Basket Analysis Using Apriori and FP-Growth for Analy...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, information Systems, and information Technology, WMA-Mathcomtech 2018

作者： Mustakim Herianda, Della Maulina Ilham, Ahmad Daeng Gs, Achmad Laumal, Folkes E. Kurniasih, Nuning Iskandar, Akbar Manulangga, Gloria Indra Iswara, Ida Bagus Ary Rahim, Robbi Puzzle Research Data Technology UIN Sultan Syarif Kasim Riau Indonesia Department of Information System UIN Sultan Syarif Kasim Riau Indonesia Informatics Department Universitas Muhammadiyah Semarang Semarang Indonesia Department of Management University of 45 Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Kupang Kupang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Informatics Department STMIK STIKOM Indonesia Denpasar Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

Berkah Mart is one of the new minimarkets in Pekanbaru city which began to develop as a retail business to meet the needs of the community. In his operation, Berkah Mart still uses the traditional store management perception that the placement of product layout on sale shelves has not been through sales analysis. Existing products have not been reviewed from the consumer's point of view, whereas the marketing economics concept provides a variety of product and consumer management methods for business management in Berkah Mart, including market bucket analysis techniques and various itemset search algorithms. In this research, Market Basket Basket Analysis with FP-Growth algorithm is proposed to determine the layout and planning of goods availability. The application of FP-Growth algorithm proved to be useful in generating many and informative association rules to find out the consumer spending pattern at Berkah Mart in Pekanbaru. Moreover, from the rules of the customer association can be segmented separately to meet the specific needs of customers with cost-effective by using some special promotions for the general group. The experimental results show that FP-Growth algorithm can analyze quickly and efficiently informing consumer shopping pattern at Berkah Mart and can increase Berkah Mart revenue. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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A NEW HOPE: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation

arXiv

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arXiv 2024年

作者： Pan, Shidong Tao, Zhen Hoang, Thong Zhang, Dawen Li, Tianshi Xing, Zhenchang Xu, Xiwei Staples, Mark Rakotoarivelo, Thierry Lo, David School of Computing Australian National University Australia Software and Computational Systems Research Program CSIRO’s Data61 Australia Khoury College of Computer Sciences Northeastern University United States School of Computing and Information Systems Singapore Management University Singapore

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application’s graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SEEPRIVACY could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://***/SeePrivacy/ © 2024, CC BY.

关键词： Graphical user interfaces

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Sensory Perception of Haptic Rendering in Surgical Simulation

Sensory Perception of Haptic Rendering in Surgical Simulatio...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rohit Kaushik Rachit Sachdeva Chirag Vashisht Krishan Kant Choudhary CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA Electronics and Communication Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Information Technology and Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India

The essential ideas behind haptic technology, including haptic interface and haptic rendering methods, as well as its application to surgical simulation, medical education, and other diverse topics, are presented in this paper. In empirical psychology and physiology, this refers to the sense of contact. The process of analyzing and developing capabilities in response to user interactions with virtual entities is elaborated as haptic rendering. Haptic may indicate haptic transmission, haptic perception, and haptic technology. Humans and other animals can respond to one another by using their feelings through haptic technology. Haptic perception is the process of recognizing objects through *** implies tactile response, which means it delivers feedback in the state of sensation as we may sense the material properties and motions of virtual objects devised by a computer. The use of haptic technology in the medical profession has proven extremely beneficial, and it has a wide range of applications in many different industries. Providing effective exchange via virtual reality helps to ease the surgeon’s obligations. Amplify a person’s experience in the virtual environment. The haptics ability ranges from computer graphics to psychophysics, cognitive science, and neurosciences.

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Scalable bayesian nonparametric clustering and classification

arXiv

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arXiv 2018年

作者： Ni, Yang Müller, Peter Diesendruck, Maurice Williamson, Sinead Zhu, Yitan Ji, Yuan Department of Statistics and Data Sciences University of Texas at Austin Department of Mathematics University of Texas at Austin Department of Information Risk and Operations Management University of Texas at Austin Program for Computational Genomics and Medicine NorthShore University HealthSystem Department of Public Health Sciences University of Chicago

We develop a scalable multi-step Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is "embarrassingly parallel" and can be implemented using the same Markov chain Monte Carlo sampler. The simplicity and generality of our approach makes inference for a wide range of Bayesian nonparametric mixture models applicable to large datasets. Specifically, we apply the approach to inference under a product partition model with regression on covariates. We show results for inference with two motivating data sets: A large set of electronic health records (EHR) and a bank telemarketing dataset. We find interesting clusters and favorable classification performance relative to other widely used competing classifiers. Copyright © 2018, The Authors. All rights reserved.

关键词： Records management

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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

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A deep learning approach to Lung Nodule Growth Prediction using CT image combined with Demographic and image features 23

A deep learning approach to Lung Nodule Growth Prediction us...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

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FincGAN: A Gan Framework of Imbalanced Node Classification on Heterogeneous Graph Neural Network

FincGAN: A Gan Framework of Imbalanced Node Classification o...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hung Chun Hsu Ting-Le Lin Bo-Jun Wu Ming-Yi Hong Che Lin Chih-Yu Wang Graduate Program of Data Science National Taiwan University and Academia Sinica Taiwan Department of Accounting and Statistics National ChengChi University Taiwan Department of Risk Management and Insurance National ChengChi University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imbalanced data can lead to sub-optimal performance. Traditional oversampling methods, while effective, risk overfitting and face difficulties in reintegrating synthetic samples into the original graph. In this study, we introduce Framework of Imbalanced Node Classification on heterogeneous graph neural network with GAN (FincGAN), a new framework that utilizes oversampling techniques to address class imbalance in heterogeneous graphs. Instead of duplicating existing samples, FincGAN employs a Generative Adversarial Network (GAN) to create synthetic samples and uses deep learning-based edge generators to connect them back to the original graph. Our evaluations on spam user detection in the Amazon and Yelp Review datasets show that FincGAN outperforms baseline models in all essential metrics, including F-score and AUC-PRC score, showing its effectiveness in addressing class imbalance.

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