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arXiv 2018年

作者： Yang, Yilong Li, Xiaoshan Qamar, Nafees Ke, Wei Liu, Zhiming Faculty of Science and Technology University of Macau China Department of Computer Science State University of New York Biomedical and Health Informatics Program United States Macau Polytechnic Institute China School of Computer and Information Science SouthwestUniversity Chongqing China

Legacy Electronic Health Records (EHRs) systems were not developed with the level of connectivity expected from them nowadays. Therefore, interoperability weakness inherent in the legacy systems can result in poor patient care and waste of financial resources. Large hospitals are less likely to share their data with external hospitals due to economic and political reasons. Motivated by these facts, we aim to provide a set of software implementation guidelines, i.e., MedShare to deal with interoperability issues among disconnected healthcare systems. The proposed integrated architecture includes: 1) a data extractor to fetch legacy medical data from a hemodialysis center, 2) converting it to a common data model, 3) indexing patient information using the HashMap technique, and 4) a set of services and tools that can be installed as a coherent environment on top of stand-alone EHRs systems. Our work enabled three cooperating but autonomous hospitals to mutually exchange medical data and helped them develop a common reference architecture. It lets stakeholders retain control over their patient data, winning the trust and confidence much needed towards a successful deployment of MedShare. Security concerns were effectively addressed that also included patient consent in the data exchange process. Thereby, the implemented toolset offered a collaborative environment to share EHRs by the healthcare providers. Copyright © 2018, The Authors. All rights reserved.

关键词： Hospitals

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Shared secret key generation via carrier frequency offsets

arXiv

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arXiv 2019年

作者： Aman, Waqas Ijaz, Aneeqa Ur Rahman, M. Mahboob Jayakody, Dushanta Nalin K. Pervaiz, Haris Lahore Pakistan School of Computer Science & Robotics National Research Tomsk Polytechnic University Russia School of Postgraduate Studies and Research Sri Lanka Technological Campus Sri Lanka School of Computing and Communications Lancaster University United Kingdom

This work presents a novel method to generate secret keys shared between a legitimate node pair (Alice and Bob) to safeguard the communication between them from an unauthorized node (Eve). To this end, we exploit the reciprocal carrier frequency offset (CFO) between the legitimate node pair to extract common randomness out of it to generate shared secret keys. The proposed key generation algorithm involves standard steps: the legitimate nodes exchange binary phase-shift keying (BPSK) signals to perform blind CFO estimation on the received signals, and do equi-probable quantization of the noisy CFO estimates followed by information reconciliation-to distil a shared secret key. Furthermore, guided by the Allan deviation curve, we distinguish between the two frequency-stability regimes - when the randomly time-varying CFO process i) has memory, ii) is memoryless;thereafter, we compute the key generation rate for both regimes. Simulation results show that the key disagreement rate decreases exponentially with increase in the signal to noise ratio of the link between Alice and Bob. Additionally, the decipher probability of Eve decreases as soon as either of the two links observed by the Eve becomes more degraded compared to the link between Alice and Bob. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal to noise ratio

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A Mechanism for Verifying the Integrity and Immutability of Tuberculosis Data sing IOTA Distributed Ledger Technology 15

A Mechanism for Verifying the Integrity and Immutability of ...

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15th Annual Conference on Health Informatics meets Digital Health, dHealth 2021

作者： Lima, Vinícius Bernardi, Filipe Rijo, Rui Ueyama, Jó Alves, Domingos Ribeirão Preto Medical School University of São Paulo Ribeirão Preto Brazil Bioengineering Postgraduate Program São Carlos School of Engineering University of São Paulo São Carlos Brazil School of Technology and Management Polytechnic Institute of Leiria Leiria Portugal Institute of Mathematical and Computer Sciences University of São Paulo São Carlos Brazil Department of Social Medicine Ribeirão Preto Medical School University of São Paulo Ribeirão Preto Brazil

ISBN: (纸本)9781643681801

Background: Intensified research and innovation and rapid uptake of new tools, interventions, and strategies are crucial to fight Tuberculosis, the world's deadliest infectious disease. The sharing of health data remains a significant challenge. Data consumers must be able to verify the consistency and integrity of data. Solutions based on distributed ledger technologies may be adequate, where each member in a network holds a unique credential and stores an identical copy of the ledger and contributes to the collective process of validating and certifying digital transactions. Objectives: This work proposes a mechanism and presents a use case in Digital Health to allow the verification of integrity and immutability of TB electronic health records. Methods: IOTA was selected as a supporting tool due to its data immutability, traceability and tamper-proof characteristics. Results: A mechanism to verify the integrity of data through hash functions and the IOTA network is proposed. Then, a set of TB related information systems was integrated with the network. Conclusion: IOTA technology offers performance and flexibility to enable a reliable environment for electronic health records. © 2021 The authors, AIT Austrian Institute of Technology and IOS Press.

关键词： Disease control

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Predicting Protein Interaction Network Perturbation by Alternative Splicing with Semi-Supervised Learning

SSRN

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SSRN 2020年

作者： Narykov, Oleksandr Johnson, Nathan Korkin, Dmitry Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Harvard Program in Therapeutic Sciences Harvard Medical School Breast Tumor Immunology Laboratory Dana-Farber Cancer Institute BostonMA United States Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States

Alternative splicing introduces a new layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. In particular, recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions, yet the extent to which the macromolecular interactions are affected by alternative splicing has to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here we develop an accurate feature-based machine learning approach that predicts if a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method called ALTernatively Spliced INteractions prediction (ALT-IN) Tool was compared with the state-of-the-art PPI prediction tools and showed superior performance, achieving 0.92 in precision and recall values. © 2020, The Authors. All rights reserved.

关键词： Forecasting

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LLM-based Hierarchical Label Annotation for Foodborne Illness Detection on Social Media

LLM-based Hierarchical Label Annotation for Foodborne Illnes...

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IEEE International Conference on Big Data

作者： Dongyu Zhang Ruofan Hu Dandan Tao Hao Feng Elke Rundensteiner AML ByteDance San Jose USA Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Foodborne illnesses pose a threat to public health, leading to morbidity, mortality, and economic burden annually. Social media, while providing a rich timely source for training AI models for surveillance, requires effective tools for annotation. While Large Language Models (LLMs) have shown promise for generating simple labels, here hierarchical labels composed of entity types like food type and symptom (at individual word level) and the foodborne illness event (at complete post level) are required. For this, we introduce ICL2FID, the first LLM-based hierarchical labeling framework designed to annotate social media posts for foodborne illness detection at two levels using only a few demonstration examples. To utilize the interconnection between post and word levels, ICL2FID instructs the LLM to leverage information from one level when predicting the other level. To combat model hallucination and cyclic dependencies, a verification step improves evidence propagation between interconnected word and post-level labeling tasks. Strategies for custom selection of demonstration examples are designed reducing biases and increasing representation. We compare ICL2FID against traditional supervised learning and other LLM methods, demonstrating that it not only achieves superior accuracy but does so at a fraction of the cost and time. These findings highlight ICL2FID’s potential as a viable alternative for hierarchical label generation in scenarios with limited resources and huge data sets. Code is available at https://***/zdy93/ICL2FID.

关键词： Training Social networking (online) Annotations Biological system modeling Surveillance Large language models Supervised learning Mortality Labeling Public healthcare

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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IEEE International Conference on Big Data

作者： Ruofan Hu Dongyu Zhang Dandan Tao Huayi Zhang Hao Feng Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China ByteDance San Jose USA College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Deden Ardiansyah Mustafid Teddy Mantoro Doctoral Program of Information Systems School of Postgraduate Universitas Diponegoro Semarang Central Java Indonesia Computer Engineering Department Vocational School Universitas Pakuan Bogor West Java Indonesia Media-Tech-Lab Computer Science Department Sampoerna University DKI Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm.

关键词： Wireless communication Wireless sensor networks Energy consumption Q-learning Machine learning algorithms Energy resources Classification algorithms

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Improving the user experience on mobile apps through data mining

Improving the user experience on mobile apps through data mi...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Tássio de O. S. Auad Luiz Felipe C. Mendes Victor Ströele José Maria N. David Center of Graduate School - CES/JF Juiz de Fora Brazil Center of Graduate School — CES/JF Juiz de Fora Brazil Postgraduate Program in Computer Science — PGCC Federal University of Juiz de Fora Brazil

ISBN: (纸本)9781509019168

With the advance of mobile applications market, there is an increasing concern about the challenges when developing products that meet the many types of users and to harmonize each product with the various usage environments, thus, providing a good user experience. This situation leads to the need to develop a framework that makes applications become aware of the context and provides a self-learning of the user navigation. This article presents a theoretical framework and the TURAP operation process, a framework for Android applications that helps to tackle these challenges.

关键词： Context Data mining Mobile applications Navigation Visualization Usability Friction

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Diet as an epigenetic factor in inflammatory bowel disease

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World Journal of Gastroenterology 2023年第41期29卷 5618-5629页

作者： Karina Marangoni Gilson Dorneles Daniella Miranda da Silva Letícia Pereira Pinto Carina Rossoni Sabrina Alves Fernandes Egas Moniz School of Health and Science Caparica-AlmadaPortugalCaparica 2820-062Portugal National Institute of Sciences and Technology-Theranostics and Nanobiotechnology Federal University of Uberlandia-MGBrazilUberlândia 38400-902Brazil Corporate Social Responsibility Hospital Moinhos de VentoPorto Alegre 90035-004Brazil Postgraduate Program in Gastroenterology Universidade Federal do Rio Grande do SulPorto Alegre 91540-000Brazil Department of Nutrition Uniasselvi-Group VitruSanta Catarina 89082-262Brazil Postgraduate Program in Hepatology Universidade Federal de Ciências da Saúde de Porto AlegrePorto Alegre 90050-170Brazil Master in Physical Activity and Health Polytechnic Institute of BejaBeja 7800-000Portugal Degree in Nutrition Sciences Lusófona UniversityLisboa 1749-024Portugal

Inflammatory bowel disease(IBD)has as a main characteristic the exacerbation of the immune system against enterocytes,compromising the individual’s intestinal *** inflammatory cascade causes several nutritional deficiencies,which further compromise immunological functioning and,as a result,worsen the *** vicious cycle can be interrupted as the patient’s dietary pattern meets their needs according to their clinical condition,acting directly on the inflammatory process of IBD through the interaction of food,intestinal microbiota,and *** nutritional intervention for IBD has a crucial role in preventing and managing disease *** review addresses epigenetic modifications through dietary compounds as a mechanism for modulating the intestinal microbiota of patients with IBD.

关键词： Inflammatory bowel disease Epigenetics Nutrition Nutrigenetics

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