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Integrated Electronic Health Record of Multidisciplinary Professionals Throughout the Cancer Care Pathway: A Pilot Study Exploring Patient-Centered Information in Breast Cancer Patients

SSRN

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SSRN 2023年

作者： Sugiyama, Atsuko Okumiya, Hayato Fujimoto, Katsuhiko Utsunomiya, Kazuki Shimomura, Yuka Sanuki, Masaru Kume, Keitaro Yano, Takahiro Kagawa, Rina Bando, Hiroko Canon Medical Systems Corporation Tochigi Otawara Japan Graduate School of Biomedical Engineering Tohoku University Miyagi Sendai Japan Laboratory of Mathematical Informatics in Medicine University of Tsukuba Ibaraki Tsukuba Japan Department of Biomedical Informatics and Management University of Tsukuba Hospital Ibaraki Tsukuba Japan Department of Breast-Thyroid-Endocrine Surgery Institute of Medicine University of Tsukuba Ibaraki Tsukuba Japan

Purpose: The aim of this pilot study was to first aggregate and then integrate the medical records of various healthcare professionals involved with breast cancer patients to reveal if and how patient-centered information is documented in multidisciplinary cancer care. Patients and methods: We aggregated 20 types of medical records from various healthcare professionals throughout 3 breast cancer patients’ care pathways in a department of breast surgery at a university hospital. These records consisted of 4,061 paragraphs comprising 14,195 lines. We integrated these data for each case. We manually annotated and analyzed them exploratively. First, provisional tags were given for all sentences. A breast cancer surgeon (26 years experience) checked the annotated results and revised the provisional tags. All records were then given revised tags and analyzed for how patient-centered information was documented. We defined Concern of Daily Life-Care-Treatment, State of Mind, Patient Views and Preferences, and Contextual Features as patient-centered information, whereas Medical Indication and Physical Status were designated biomedical information. Results: The number of patient-centered information related tags accounted for 30%, 30% and 20% of the total in case 1, case 2 and case 3, respectively. In all cases, patient-centered information was distributed across various medical records. The Progress Note tag provided the most patient-centered information, while other records contained information not described elsewhere in the progress notes. The records of Nurses and Allied Healthcare Professionals included more patient-centered information than the doctors’ notes. Each piece of information was documented in fragments providing each of the healthcare professionals’ viewpoints. Conclusion: The documented information throughout the breast cancer care pathway in the cases examined was dominated by biomedical information. However, our findings suggest integrating the EHR of var

关键词： Diseases

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Investigating Neural Network-Based Deep Learning Strategies for Real-Time Data Analysis in Machine Learning

Investigating Neural Network-Based Deep Learning Strategies ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Suchita Walke Mounika Nalluri Lavanya R Aruna Sri Rongali Hrishikesh Bhanudas Vanjari Ritu Computer Engineering pillai collage of Engg and Technology Mumbai University India Masters in Computer Information Systems Murray State University USA ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US E &TC Engineering Dept BVCOE Lavale SPPU Pune Maharashtra India Management Maharishi Markandeshwar(deemed to be university) Ambala Haryana

there are numerous distinct strategies and techniques that fall under the huge class of neural network-primarily based deep gaining knowledge of in device getting to know. those strategies revolve around developing and schooling synthetic neural networks, that are computational models that mimic the capabilities and structure of the human brain, to analyze huge datasets and make predictions or decisions based totally on the *** of the key challenges in actual-time information analysis is being capable of method incoming information fast and efficiently, as well as adapt to converting data styles or sudden occasions. traditional device learning algorithms are frequently restricted in their ability to deal with these demanding situations, that is in which deep gaining knowledge of techniques are available *** getting to know techniques contain education neural networks with a couple of layers, every of which strategies one-of-a-kind aspects of the data. This lets in for greater complex and accurate evaluation of the facts, in addition to the potential to handle larger and more numerous datasets. One famous method in deep studying is known as convolutional neural networks (CNNs). Those networks are specifically properly-suitable for studying visual information, including snap shots, by means of breaking down the data into smaller parts and studying them at one of a kind ranges of abstraction. This permits for the identification of patterns and features in the data which could then be used for category or prediction responsibilities.

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Structural Metamorphosis of Graphitic Derivatives Produced from Fractionated Bamboo Lignin

ACS Sustainable Resource Management

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ACS Sustainable Resource management 2023年第1期1卷 97-113页

作者： Mohammed Abdillah Ahmad Farid Jacqueline Lease Alvin Lim Teik Zheng Toshiki Tsubota Yoshito Andou Department of Biological Functions Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology 2-4 Hibikino Wakamatsu Kitakyushu Fukuoka 808-0196 Japan Department of Science and Technology Faculty of Humanities Management and Science Universiti Putra Malaysia Bintulu Sarawak 97008 Bintulu Sarawak Malaysia Department of Applied Chemistry Faculty of Engineering Kyushu Institute of Technology 1-1 Sensuicho Tobata Kitakyushu Fukuoka 804-8550 Japan Collaborative Research Centre for Green Materials on Environmental Technology Kyushu Institute of Technology 2-4 Hibikino Wakamatsu Kitakyushu Fukuoka 808-0196 Japan

The demand for sustainable and renewable materials has prompted extensive research on lignocellulosic biomass as a potential feedstock for advanced nanomaterials. This article describes the sequential processes of iron-catalyzed graphitization, improved Hummer’s method, and low-temperature thermal annealing designed to transform ethanosolv lignin into bio-based graphene products. The lignin undergoes a metamorphosis during graphitization, transforming into turbostratic crystallite with a unique flake-like configuration at high temperatures in a tightly sealed muffle furnace. Following oxidation, the structure of the resultant graphite undergoes dynamic changes, leading to exfoliation-induced distancing of the sheets of graphene layers. Notwithstanding the reduction process, the geometrical morphology of its oxidized state remains unaltered, preserving the lamellar configuration. Notably, the resulting graphitized lignin showcases a substantial carbon content exceeding 75 wt % and a relatively low oxygen content below 16 wt %. In contrast, graphene oxides (GOs) exhibit elevated oxygen levels resulting from the introduction of oxygen moieties during the oxidation step, which subsequently diminishes following thermal annealing. Diffraction patterns confirm the presence of (001), (002), and (100) planes in all graphene-based materials, indicating an aromatic ring orientation. Oxidation affects interlayer spacing with GOs exhibiting larger distances than graphitized lignin and reduced graphene oxides (rGOs). Graphitization increases the sp2carbon composition by removing volatiles and mineral matter. Oxidation increases the sp3carbon composition, but it decreases after thermal annealing due to the partial removal of oxygen species, leading to a reorientation of the sp2carbon *** research explores converting bamboo lignin into customizable graphene materials for sustainable use. The study details processes of metamorphosis, oxidation, and reduction, reveali

关键词： Anode materials Biopolymers Carbon Organic polymers Oxygen

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Wargaming Commodity Course of Action Automated Analysis Method

Wargaming Commodity Course of Action Automated Analysis Meth...

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作者： DeBerry, W. T AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT Air Force Research Laboratory Plans and Programs Directorate Wright-Patterson Air Force Base OH Air Force Institute of Technology Plans and Programs Directorate Wright-Patterson Air Force Base OH

This research presents the Wargaming Commodity Course of Action Automated Analysis Method (WCCAAM), a novel approach to assist wargame commanders in developing and analyzing courses of action (COAs) through semi-automation of the Military Decision Making Process (MDMP). MDMP is a seven-step iterative method that commanders and mission partners follow to build an operational course of action to achieve strategic objectives. MDMP requires time, resources, and coordination all competing items the commander weighs to make the optimal decision. WCCAAM receives the MDMP's Mission Analysis phase as input, converts the wargame into a directed graph, processes a multi-commodity flow algorithm on the nodes and edges, where the commodities represent units, and the nodes represent blue bases and red threats, and then programmatically processes the MDMP steps to output the recommended COA. To demonstrate WCCAAM effectiveness, a wargame scenario compares COA outcomes within the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) and statistical analysis. The AFSIM results demonstrate a 71 objective completion improvement with the WCCAAM COA versus a human-generated COA. Statistical analysis reveals that over a 300 run test matrix, WCCAAM produces the optimal, minimal risk COA.

关键词： Air force Statistical analysis United states Algorithms engineering Simulations Air force research laboratories Commerce Commodities Governments Military research Information science Materials Military operations Operations research Standards Statistics

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Scenario Assessment of Neodymium and Cobalt Recycling in ASEAN Countries Based on a Dynamic Material Flow Analysis

SSRN

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SSRN 2023年

作者： Rustandi, Rendi Ahmad Morimoto, Shinichirou Borrirukwisitsak, Siriporn Khwamsawat, Kannika Chaipurimas, Supaluk Leewattananukul, Surachai Khaodhiar, Sutha Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Ibaraki Tsukuba305-8573 Japan Department of Energy and Environment National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8569 Japan Department of Environmental Science Faculty of Science Silpakorn University Nakhon Pathom73000 Thailand Center of Excellence on Hazardous Substance Management Chulalongkorn University Bangkok10330 Thailand

Tackling global greenhouse gas (GHG) emissions warrants a radical shift to low-carbon technologies, including batteries and permanent magnets. This shift will increase demand for critical raw materials (CRMs). This study offers dynamic material flow analysis (MFA) to address uncertainties associated with the sustainable supply of neodymium (Nd) and cobalt (Co): CRMs used in NdFeB magnets and lithium-ion batteries in ASEAN. Annual maximum and minimum scenario estimates were conducted for demand and waste of CRMs and CO2 emissions of recycled versus virgin materials. Based on the results, in 2050, 3,209 ton/y of Nd will be discarded, of which 25.3% will be accumulated as hibernating stock. Moreover, 33.79% of Co will be kept as hibernating stock. Recycling NdFeB magnets and batteries could reduce CO2 emissions up to 1,536 kton CO2-Eq/y and 313.5 kton CO2-Eq/y, respectively, by 2050. The proposed dynamic MFA is a useful foundation for the creation of effective global standards. © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

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Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

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ENHANCING SPEAKING STYLES IN CONVERSATIONAL TEXT-TO-SPEECH SYNTHESIS WITH GRAPH-BASED MULTI-MODAL CONTEXT MODELING

arXiv

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arXiv 2021年

作者： Li, Jingbei Meng, Yi Li, Chenyi Wu, Zhiyong Meng, Helen Weng, Chao Su, Dan Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong AI Lab Tencent Shenzhen China

Comparing with traditional text-to-speech (TTS) systems, conversational TTS systems are required to synthesize speeches with proper speaking style confirming to the conversational context. However, state-of-the-art context modeling methods in conversational TTS only model the textual information in context with a recurrent neural network (RNN). Such methods have limited ability in modeling the inter-speaker influence in conversations, and also neglect the speaking styles and the intra-speaker inertia inside each speaker. Inspired by DialogueGCN and its superiority in modeling such conversational influences than RNN based approaches, we propose a graph-based multi-modal context modeling method and adopt it to conversational TTS to enhance the speaking styles of synthesized speeches. Both the textual and speaking style information in the context are extracted and processed by DialogueGCN to model the inter- and intra-speaker influence in conversations. The outputs of DialogueGCN are then summarized by attention mechanism, and converted to the enhanced speaking style for current utterance. An English conversation corpus is collected and annotated for our research and released to public. Experiment results on this corpus demonstrate the effectiveness of our proposed approach, which outperforms the state-of-the-art context modeling method in conversational TTS in both MOS and ABX preference rate. © 2021, CC BY.

关键词： Recurrent neural networks

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A Novel Approach for Unsupervised Sensor Error Classification

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Procedia CIRP 2024年 130卷 206-213页

作者： Samiha Durnagöz Tobias Eberhardt Felix Fuchs Peter Reimann Thomas Ackermann Maik Fiedler Mathias Mayer Marco F. Huber Alexander Verl Graduate School of Excellence advanced Manufacturing Engineering GSaME University of Stuttgart Nobelstraße 12 70569 Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Allmandring 35 70569 Stuttgart Germany AUDI AG NSU-Straße 1 74172 Neckarsulm Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 12 70569 Stuttgart Germany Institute for Control Engineering of Machine Tools and Manufacturing Units ISW University of Stuttgart Seidenstraße 36 70174 Stuttgart Germany J. Schmalz GmbH Johannes-Schmalz-Straße 1 72293 Glatten Germany Institute of Parallel and Distributed Systems (IPVS) Universitätsstraße 38 70569 Stuttgart Germany

The precise detection and classification of sensor errors is crucial for the optimal operation of smart manufacturing systems. Sensor errors, which can manifest in various forms, such as drifts or constant values, significantly challenge data integrity. This work introduces a novel approach that combines unsupervised learning and system simulations to detect and classify relevant sensor errors. Our approach avoids extensive manual labeling and uses only error-free simulated time series data to create initial embeddings of sensor data. This establishes a baseline for normal sensor behavior. To facilitate a precise identification of error types through a classifier, simulated sensor errors are introduced to create context-aware embeddings. We further analyze and compare conventional to advanced embedding methods such as TS2Vec. We apply our approach to a synthetic dataset obtained from a vacuum handling system and accurately identify and classify relevant sensor error types. Our work contributes to advancing operational efficiency and reliability in automated manufacturing processes within the Industry 4.0 framework by improving the detection capabilities for sensor errors.

关键词： Unsupervised learning Sensor errors TS2Vec System simulation

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IIOT Within The Architecture Of The Manufacturing Company 20

IIOT Within The Architecture Of The Manufacturing Company

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Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service

作者： Anastasia Levina Sofia Kalyazina Alena Ershova Peter Cornelis Schuur Graduate school of Business and Management Peter the Great St.Petersburg Polytechnic University St. Petersburg Russia Industrial Engineering and Business Information Systems University of Twente Enschede Overijssel Netherlands

ISBN: (纸本)9781450388900

The introduction of the Internet of Things (IoT) in industry can increase the level of production management, increase the speed of managerial decision-making, and provide higher quality products. The article discusses the use of the Industrial Internet of Things (IIoT) in the architecture of a manufacturing enterprise. The technical solutions currently used and the main effects of the introduction of the Industrial Internet of Things are analyzed.

关键词： Manufacturing Internet of Things Industrial Internet of Things ERP

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Cloud Application For Sheet Materials Cutting Optimization 20

Cloud Application For Sheet Materials Cutting Optimization

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Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service

作者： Igor Ilin Aleksander Kubarskii Peter Cornelis Schuur Aleksander Lepekhin Alissa Dubgorn Graduate school of Business and Management Peter the Great St.Petersburg Polytechnic University St. Petersburg Russia Industrial Engineering and Business Information Systems University of Twente Enschede Overijssel Netherlands

ISBN: (纸本)9781450388900

The purpose of this study is to provide a working SaaS application that can be used for sheet materials cutting optimization using heuristics. For the research, literature and open sources in the internet were analyzed. The list of the most common and well-known applications for cutting optimization was formed. The cloud and on premises applications were briefly compared and cloud solutions advantages were highlighted. Few algorithms were chosen and implemented using JavaScript on server-side and deployed, using Amazon Web Services. However, there is a great room for improvement for methods used and implementation of algorithm for automatic cutting method choosing.

关键词： Cloud computing Cutting optimization SaaS

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