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Implementing a transmural model of early palliative care in advanced dementia: the use of a hybrid effectiveness-implementation study design

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BMC GERIATRICS 2025年第1期25卷 1-10页

作者： Hum, Allyn Kaur, Palvinder Goh, Wen Yang Tay, Riyin Neo, Han Yee Koh, Mervyn Yong Hwang Ali, Noorhazlina Binte Lim, Wee Shiong Tan, Yu-Ling Jackie Wu, Huei Yaw Kannapiran, Palvannan Tan, Hwee Teng Robyn Sun, Yan Ong, Chin Ee Sachdev, Ravinder Singh Low, Zhi Jun Tey, Lee Hung Tan, Woan Shin Ding, Yew Yoong Tan Tock Seng Hosp Dept Palliat Med 11 Jalan Tan Tock Seng Singapore 308433 Singapore Natl Healthcare Grp Hlth Serv & Outcomes Res 3 Fusionopolis Link Singapore 138543 Singapore Tan Tock Seng Hosp Dept Geriatr Med 11 Jalan Tan Tock Seng Singapore 308433 Singapore Palliat Care Ctr Excellence Res & Educ 1 Tan Tock Seng Link Singapore 307382 Singapore Dover Pk Hosp TTSH Integrated Care Hub 1 Tan Tock Seng Link Singapore 307382 Singapore Tan Tock Seng Hosp Dept Gen Med 11 Jalan Tan Tock Seng Singapore 308433 Singapore Natl Univ Singapore Inst Policy Studies 1C Cluny Rd House 5 Singapore 259599 Singapore Tan Tock Seng Hosp Dept Clin & Hlth Informat Off 11 Jalan Tan Tock Seng Singapore 308433 Singapore Geriatr Educ & Res Inst 2 Yishun Cent 2 Singapore 768024 Singapore Woodlands Hlth Campus Palliat & Support Care 17 Woodlands Dr 17 Singapore 737628 Singapore

BackgroundPeople with dementia receive differential access to palliative care services despite suffering from a significant burden of the disease in the advanced stage. Professional and familial caregivers may not view dementia as a terminal illness and are less likely to engage in end-of-life care discussions. Healthcare providers also face challenges coordinating palliative care in the community for dementia, demonstrating a lack of understanding of the needs of patients living with advanced dementia, and resources available to support them within the *** aim of this study is to implement a transmural model of care in a tertiary care setting where patients living with advanced dementia (PLAD) at risk of deterioration in one year are identified early to receive tailored palliative care interventions using a predictive ***/DesignThe updated medical research council (MRC) framework for complex interventions is used to guide the development and implementation of the transmural model which incorporates a predictive algorithm in clinical practice, with interventions tailored for at risk PLAD both within, and beyond the tertiary care setting. The PROgnostic Model for Advanced DEmentia (PRO-MADE) developed to predict one-year all-cause mortality in PLAD presenting to an acute care hospital was embedded into the electronic health records of the tertiary care setting as a mathematical equation. predictive modeling markup language in the digital records platform is used to calculate the risk score for PLAD by inputting the predictors. The clinical team is alerted of at risk PLAD through timed directive prompts, with advice on management given through tailored care bundles. The study will adopt a mixed methods approach in a Type 1 effectiveness-implementation study design to concurrently study the effectiveness of the transmural model in practice, and the barriers and facilitators to its *** implementation of a transmural mo

关键词： Advanced dementia predictive algorithm Transmural Early palliative care Implementation

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Bile Is a Selective Elevator for Mucosal Mechanics and Transport

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MOLECULAR PHARMACEUTICS 2023年第12期20卷 6151-6161页

作者： Hanio, Simon Mollmert, Stephanie Mockel, Conrad Choudhury, Susobhan Hopfel, Andreas I. Zorn, Theresa Endres, Sebastian Schlauersbach, Jonas Scheller, Lena Kessler, Christoph Scherf-Clavel, Oliver Bellstedt, Peter Schubert, Ulrich S. Poppler, Ann-Christin Heinze, Katrin G. Guck, Jochen Meinel, Lorenz Univ Wurzburg Inst Pharm & Food Chem D-97074 Wurzburg Germany Max Planck Inst Sci Light D-91058 Erlangen Germany Max Planck Zentrum Phys & Med D-91058 Erlangen Germany Univ Wurzburg Rudolf Virchow Ctr Integrat & Translat Bioimaging D-97080 Wurzburg Germany Univ Wurzburg Inst Organ Chem D-97074 Wurzburg Germany Univ Jena Inst Organ & Macromol Chem D-07743 Jena Germany Univ Zurich Inst Clin Chem CH-8091 Zurich Switzerland Friedrich Schiller Univ Jena Jena Ctr Soft Matter JCSM D-07743 Jena Germany Helmholtz Inst RNA Based Infect Res HIRI Wurzburg 97080 Germany

Mucus mechanically protects the intestinal epithelium and impacts the absorption of drugs, with a largely unknown role for bile. We explored the impacts of bile on mucosal biomechanics and drug transport within mucus. Bile diffused with square-root-of-time kinetics and interplayed with mucus, leading to transient stiffening captured in Brillouin images and a concentration-dependent change from subdiffusive to Brownian-like diffusion kinetics within the mucus demonstrated by differential dynamic microscopy. Bile-interacting drugs, Fluphenazine and Perphenazine, diffused faster through mucus in the presence of bile, while Metoprolol, a drug with no bile interaction, displayed consistent diffusion. Our findings were corroborated by rat studies, where co-dosing of a bile acid sequestrant substantially reduced the bioavailability of Perphenazine but not Metoprolol. We clustered over 50 drugs based on their interactions with bile and mucin. Drugs that interacted with bile also interacted with mucin but not vice versa. This study detailed the dynamics of mucus biomechanics under bile exposure and linked the ability of a drug to interact with bile to its abbility to interact with mucus.

关键词： drug bioavailability predictive algorithm diffusion Brillouin microscopy NMR FCS differential dynamic microscopy

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A CNN_LSTM_KAN Based Genetic algorithm for Photovoltaic Power Generation Revenue Prediction 25th

A CNN_LSTM_KAN Based Genetic Algorithm for Photovoltaic Pow...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Ma, Ruikang Lin, Guanyu Dong, De Zhou, Rong Fan, Jianping Duan, Keliang Zhang, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China Southern University of Science and Technology Guangdong Shenzhen China National Supercomputing Center in Shenzhen Guangdong Shenzhen China Beijing JH Eco-Energy Technology Co. Ltd. Beijing100160 China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Ürümqi China

ISBN: (纸本)9789819642069

In order to improve the economic benefits of photovoltaic power generation systems, accurate prediction of photovoltaic power generation revenue has become particularly important. However, traditional prediction algorithms such as CNN and LSTM suffer from overfitting issues, while the latest emerging KAN neural network also has problems with parameter tuning difficulties and sensitivity to data quality. Based on these issues, this paper proposes a CNN_LSTM_KAN photovoltaic power generation revenue prediction algorithm based on genetic algorithm. The experimental results show that the algorithm outperforms traditional methods in both prediction accuracy and robustness, providing an effective solution for predicting the revenue of photovoltaic power generation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： genetic algorithm photovoltaics predictive algorithm

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A predictive approach for dynamic replication of operators in distributed stream processing systems 34

A predictive approach for dynamic replication of operators i...

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34th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Wladdimiro, Daniel Arantes, Luciana Sens, Pierre Hidalgo, Nicolas Sorbonne Univ LIP6 CNRS INRIA Paris France Univ Diego Portales Santiago Chile

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Stream Processing Systems (SPSs) can present significant fluctuation in input rate. To address this issue, some existing solutions propose reconfiguring the SPS by replicating its operators. However, such reconfiguration usually induces a high system downtime cost. Moreover, reconfiguration decisions are based only on resource utilization without balancing the load between replicas. We propose in this paper a predictive SPS that dynamically defines the necessary number of replicas of each operator based not only on the current resource utilization and input rate variation but also on the events that, due to the operator's overloading, could not be processed yet and are, thus, kept in the operator's queue. In addition, our SPS implements a load balancer that distributes incoming events more evenly among replicas of an operator. Our solution has been integrated into Storm. To avoid system reconfiguration downtime, our SPS preallocates a pool of replicas where each of them can be activated or deactivated based on per operator input load predictions. Using real traffic traces with different applications, we have conducted experiments on Google Cloud Platform (GCP), evaluating our SPS and comparing it with Storm and DABS-Storm.

关键词： Stream processing Adaptive SPS predictive algorithm Replication Google Cloud Platform

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Robust cascade-free predictive speed control for PMSM drives based on Extended State Observer

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IET ELECTRIC POWER APPLICATIONS 2021年第2期15卷 214-230页

作者： He, Chun Hu, Jianhui Li, Yong Harbin Inst Technol Coll Elect & Elect Engn Harbin Peoples R China

The direct predictive speed controller (DPSC) deals with the prediction of the motor speed based on precise mechanical parameters. However, the mechanical parameters are dependent on the practical applications and may not match with their actual values, which leads to inaccurate prediction of the motor behaviour and deteriorates the performance of the predictive algorithm. A three-order Extended State Observer is designed for a permanent magnet synchronous motor to observe the unknown overall disturbance of the system and compensate the speed prediction error, which reduces the usage of the mechanical constants and improves the robustness of the system against parameter uncertainties, accurate mechanical parameters become unessential. To improve the current dynamics, by using proper weighting of the position angle observation error, along with the overall disturbance, a new current controller is incorporated in the control law of developed PSC strategy. The proposed strategy is evaluated through experimental results with a two-level voltage source inverter. Finally, the proposed work is experimentally compared with a predictive speed control, by considering several performance indices.

关键词： predictive algorithm two-level voltage source inverter DPSC current controller motor behaviour prediction synchronous motors direct predictive speed controller Control of electric power systems Current control predictive control robust cascade-free predictive speed control electric current control PMSM drives Stability in control theory control system synthesis Control system analysis and synthesis methods invertors mechanical constants position angle observation error parameter uncertainties motor speed Small and special purpose electric machines three-order extended state observer control law system robustness robust control unknown overall disturbance permanent magnet motors Synchronous machines machine control Optimal control permanent magnet synchronous motor precise mechanical parameters PSC strategy speed prediction error observers Simulation, modelling and identification

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Fixed-Time Practical Consensus Tracking of Multi-Agent Systems With Communication Delay

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第3期9卷 1319-1334页

作者： Ni, Junkang Zhao, Yu Cao, Jinde Li, Weilin Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

This paper addresses the fixed-time consensus tracking problem for second-order multi-agent systems (MASs) subjected to communication delays under directed communication network. First, a predictive algorithm is designed to predict the neighbors' states at current time by using the delayed information. Next, based on the predicted neighbor's states, a fixed-time distributed observer is developed to estimate the leader's states. Finally, a predefined-time nonsingular terminal sliding mode control is proposed to stabilize the tracking error system and we can determine the controller gain as soon as the convergence time bound is prescribed. It is rigorously proved that the presented scheme achieves fixed-time consensus tracking of the studied MASs. This study contributes to providing a solution to fixed-time consensus tracking problem of second-order MASs subjected to communication delays under directed communication network, which is a challenging and important issue that has not been studied. The effectiveness of the presented strategy is validated through simulation results.

关键词： Delays Communication networks Observers Consensus protocol Upper bound Task analysis Switches Fixed-time consensus tracking multi-agent system communication delay distributed observer predictive algorithm

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Adaptive Digital predictive Peak Current Control algorithm for Buck Converters

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JOURNAL OF POWER ELECTRONICS 2019年第3期19卷 613-624页

作者： Zhang, Yu Zhang, Yiming Wang, Xuhong Zhu, Wenhao Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

Digital current control techniques are an attractive option for DC-DC converters. In this paper, a digital predictive peak current control algorithm is presented for buck converters that allows the inductor current to track the reference current in two switching cycles. This control algorithm predicts the inductor current in a future period by sampling the input voltage, output voltage and inductor current of the current period, which overcomes the problem of hardware periodic delay. Under the premise of ensuring the stability of the system, the response speed is greatly improved. A real-time parameter identification method is also proposed to obtain the precision coefficient of the control algorithm when the inductance is changed. The combination of the two algorithms achieves adaptive tracking of the peak inductor current. The performance of the proposed algorithms is verified using simulations and experimental results. In addition, its performance is compared with that of a conventional proportional-integral (PI) algorithm.

关键词： Adaptive control Buck converter Parameter identification Peak current control predictive algorithm

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Baseline Values of the Compensatory Reserve Index in a Healthy Pediatric Population

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PEDIATRIC CARDIOLOGY 2022年第2期43卷 344-349页

作者： Rodriguez, Salvador A. Morgan, Gareth J. Lara, Claudia Zablah, Jenny E. Childrens Hosp Colorado Heart Inst Box 10013123 East 16th Ave Aurora CO 80045 USA Univ Colorado Sch Med Anschutz Med Campus Aurora CO 80045 USA Univ Colorado Hosp Dept Cardiol Aurora CO USA Univ Dr Jose Matias Delgado Dept Pediat Antiguo Cuscatlan El Salvador

The objective of this study is to describe the compensatory reserve index (CRI) baseline values in a healthy cohort of healthy pediatric patients and evaluate the existing correlation with other physiological parameters that influence compensatory hemodynamic mechanisms. CRI is a computational algorithm that has been broadly applied to non-invasively estimate hemodynamic vascular adaptations during acute blood loss. So far, there is a lack of baseline values from healthy individuals, which complicates accurately estimating the severity of the hemodynamic compromise. Additionally, the application of this technology in pediatric populations is limited to a few reports, highlighting a marked variability by age, weight, and other physiological parameters. The CRI of 205 healthy subjects from 0 to 60 years of age were prospectively evaluated from January to February 2020 at several public outpatient clinics in El Salvador;vital signs and sociodemographic data were also collected during this period. After data collection, baseline values were classified for each age group. Multiple correlation models were tested between the CRI and the other physiological parameters. CRI value varies significantly for each age group, finding for patients under 18 years old a mean value lower than 0.6, which is currently considered the lower normal limit for adults. CRI presents strong correlations with other physiological variables such as age, weight, estimated blood volume, and heart rate (R > 0.8, R2 > 0.6, p < 0.0001). There is significant variability in the CRI normal values observed in healthy patients based on age, weight, estimated blood volume, and heart rate.

关键词： Compensatory reserve index Machine learning Normal values predictive algorithm

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Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

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ARTHRITIS RESEARCH & THERAPY 2022年第1期24卷 74-74页

作者： van der Leeuw, Matthijs S. Messelink, Marianne A. Tekstra, Janneke Medina, Ojay van Laar, Jaap M. Haitjema, Saskia Lafeber, Floris Veris-van Dieren, Josien J. van der Goes, Marlies C. den Broeder, Alfons A. Welsing, Paco M. J. Univ Utrecht Univ Med Ctr Utrecht Dept Rheumatol & Clin Immunol Heidelberglaan 100 NL-3584 CX Utrecht Netherlands Univ Utrecht Univ Med Ctr Utrecht Dept Digital Hlth Heidelberglaan 100 NL-3584 CX Utrecht Netherlands Univ Utrecht Univ Med Ctr Utrecht Cent Diagnost Lab Heidelberglaan 100 NL-3584 CX Utrecht Netherlands Reumazorg Zuid West Nederland Van Hertumweg 17-19 NL-4462 EV Goes Netherlands Meander Med Ctr Dept Rheumatol Maatweg 3 NL-3813 TZ Amersfoort Netherlands Sint Maartensklin Dept Rheumatol Hengstdal 3 NL-6574 NA Ubbergen Netherlands

Background Biological disease-modifying antirheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis. However, as bDMARDs may also lead to adverse events and are expensive, tapering them is of great clinical interest. Tapering according to disease activity-guided dose optimization (DGDO) does not seem to affect long term remission rates, but flares are frequent during this process. Our objective was to develop a model for the prediction of flares during bDMARD tapering using data from routine care and to evaluate its potential clinical impact. Methods We used a joint latent class model to repeatedly predict the probability of a flare occurring within the next 3 months. The model was developed using longitudinal data on disease activity (DAS28) and other routine care data from two clinics. predictive accuracy was assessed in cross-validation and external validation was performed with data from the DRESS (Dose REduction Strategy of Subcutaneous tumor necrosis factor inhibitors) trial. Additionally, we simulated the reduction in number of flares and bDMARD dose when implementing the model as a decision aid during bDMARD tapering in the DRESS trial. Results Data from 279 bDMARD courses were used for model development. The final model included two latent DAS28-trajectories, bDMARD type and dose, disease duration, and seropositivity. The area under the curve of the final model was 0.76 (0.69-0.83) in cross-validation and 0.68 (0.62-0.73) in external validation. In simulation of prediction-aided decisions, the mean number of flares over 18 months decreased from 1.21 (0.99-1.43) to 0.75 (0.54-0.96). The reduction in he bDMARD dose was mostly maintained, increasing from 54 to 64% of full dose. Conclusions We developed a dynamic flare prediction model, exclusively based on data typically available in routine care. Our results show that using this model to aid decisions during bDMARD tapering may significantly reduce the number of flares while maintaini

关键词： Rheumatoid arthritis predictive algorithm Tapering bDMARD therapy Applied data analytics in medicine Biologicals

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Classification of Diabetes Types using Machine Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第9期13卷 152-161页

作者： Adigun, Oyeranmi Oyeranm, Folasade Yekini, Nureni Babatunde, Ronke Yaba Coll Technol Dept Comp Sci Lagos Nigeria Yaba Coll Technol Dept Comp Engn Lagos Nigeria Kwara State Univ Dept Comp Sci Malete Kwara Nigeria

learning algorithms have aided health workers (including doctors) in the processing, analysis, and diagnosis of medical problems, as well as the detection of disease patterns and other patient data. Diabetes mellitus (DM), commonly referred to as diabetes, is a gathering of a syndrome issue that is portrayed by high glucose levels in the blood over a drawn-out period. It is a long-term illness that is a great threat to humanity and causes death. Most of the existing machine learning algorithms used for the classification and prediction of diabetes suffer from embodying redundant or inessential medical procedures that cause complications and wastage of time and resources. The absence of a correct diagnosis scheme, deficiency of economic means, and a general lack of awareness represent the main reasons for these negative effects. Hence, preventing the sickness altogether through early detection may doubtless cut back a considerable burden on the economy and aid the patient in diabetes management. This study developed diabetes classification using machine learning techniques that will minimize the aforementioned drawbacks in the prediction of diabetes systems. Decision tree classifiers, logistic regression, random forest, and support vector machines are all examples of predictive algorithms that were tested in this paper. 1009 records of data set were obtained from the Diabetes dataset of Abelvikas, Data World. We used a confusion matrix to visualize the performance evaluation of the classifiers. The experimental result shows that the four machine learning algorithms perform well. However, Random Forest outperforms the other three, with a prediction accuracy of 100% and has a better prediction level when compared with others and existing work.

关键词： -Machine learning diabetes mellitus predictive algorithm correlation map confusion matrix

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