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Cautiously optimistic: paediatric critical care nurses' perspectives on data-driven algorithms in low-resource settings-a human-centred design study in Malawi

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BMC global and public health 2024年第1期2卷 80页

作者： Margot Rakers Daniel Mwale Lieke de Mare Lezzie Chirambo Bart Bierling Alice Likumbo Josephine Langton Niels Chavannes Hendrikus van Os Job Calis Kiran Dellimore María Villalobos-Quesada Department of Public Health and Primary Care Leiden University Medical Center Albinusdreef 2 Leiden 2333 ZA The Netherlands. National eHealth Living Lab (NeLL) Leiden University Medical Center Leiden The Netherlands. Kamuzu University of Health Sciences Blantyre Malawi. GOAL 3 Hertogenbosch The Netherlands. Training Research Unit of Excellence (TRUE) Blantyre Malawi. Paediatric Department College of Medicine Queen Elizabeth Central Hospital Blantyre Malawi. Department of Paediatric Intensive Care Emma Children's Hospital Academic Medical Center Amsterdam The Netherlands. Department of Public Health and Primary Care Leiden University Medical Center Albinusdreef 2 Leiden 2333 ZA The Netherlands. m.j.villalobos_quesada@lumc.nl. National eHealth Living Lab (NeLL) Leiden University Medical Center Leiden The Netherlands. m.j.villalobos_quesada@lumc.nl.

BACKGROUND:Paediatric critical care nurses face challenges in promptly detecting patient deterioration and delivering high-quality care, especially in low-resource settings (LRS). Patient monitors equipped with data-driven algorithms that monitor and integrate clinical data can optimise scarce resources (e.g. trained staff) offering solutions to these challenges. Poor algorithm output design and workflow integration, however, are important factors hindering successful implementation. This study aims to explore nurses' perspectives to inform the development of a data-driven algorithm and user-friendly interface for future integration into a continuous vital signs monitoring system for critical care in LRS. METHODS:Human-centred design methods, including contextual inquiry, semi-structured interviews, prototyping and co-design sessions, were carried out at the high-dependency units of Queen Elizabeth Central Hospital and Zomba Central Hospital in Malawi between March and July 2023. Triangulating these methods, we identified what algorithm could assist nurses and used co-creation methods to design a user interface prototype. data were analysed using qualitative content analysis. RESULTS:Workflow observations demonstrated the effects of personnel shortages and limited monitor equipment for vital signs monitoring. Interviews identified four themes: workload and workflow, patient prioritisation, interaction with guardians, and perspectives on data-driven algorithms. The interviews emphasised the advantages of predictive algorithms in anticipating patient deterioration, underlining the need to integrate the algorithm's output, the (constant) monitoring data, and the patient's present clinical condition. Nurses preferred a scoring system represented with familiar scales and colour codes. During co-design sessions, trust, usability and context specificity were emphasised as requirements for these algorithms. Four prototype components were examined, with nurses favouring scor

关键词： data-driven algorithms Low-resource settings Malawi Monitoring systems Nurses Paediatric critical care

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A Review of SOH Prediction of Li-Ion Batteries Based on data-driven algorithms

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ENERGIES 2023年第7期16卷 3167页

作者： Zhang, Ming Yang, Dongfang Du, Jiaxuan Sun, Hanlei Li, Liwei Wang, Licheng Wang, Kai Qingdao Univ Weihai Innovat Res Inst Sch Elect Engn Qingdao 266000 Peoples R China Xian Traff Engn Inst Xian 710300 Peoples R China Northeast Elect Power Univ Elect Engn & Automat Jilin 132012 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Zhejiang Univ Technol Sch Informat Engn Hangzhou 310000 Peoples R China

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery's state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.

关键词： LIB SOH data-driven algorithms data processing

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data-driven Learning Control algorithms for Unachievable Tracking Problems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 205-218页

作者： Zeyi Zhang Hao Jiang Dong Shen Samer S.Saab the School of Mathematics Renmin University of ChinaBeijing 100872China IEEE the School of Engineering Lebanese American UniversityByblos 2038Lebanon

For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best ***, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete *** address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain *** input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

关键词： data-driven algorithms incomplete information iterative learning control gradient information unachievable problems

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A data-driven Model to Predict Mutual Inductance Between Planar Coils With Arbitrary Specifications and Positions

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IET ELECTRIC POWER APPLICATIONS 2025年第1期19卷

作者： Asadi, Mahdi Abazari, Amir Musa Iran Univ Sci & Technol Sch Adv Technol Dept Energy Syst Engn Tehran Iran Urmia Univ Fac Engn Dept Mech Engn Orumiyeh Iran

The accurate prediction of mutual inductance in inductive planar coils is a critical challenge in advancing wireless power transfer (WPT) systems, particularly as traditional analytical methods struggle to balance precision and computational speed in complex, real-world scenarios. This study addresses these limitations by exploring data-driven algorithms for predicting mutual inductance. Additionally, the study offers a robust solution to handle the nonlinearities and dynamic requirements of three-dimensional coil configurations. Seven regression algorithms-linear, polynomial, kernel ridge, decision tree, random forest, support vector and neural network-are evaluated to identify the most effective approach. Key results reveal the superior performance of kernel ridge, support vector and neural network regression models, achieving R2 scores of 0.995, 0.987 and 0.992, respectively. Kernel ridge regression demonstrated the lowest error metrics, with an MAE of 49.624 nH and an RMSE of 86.174 nH, whereas support vector and neural network regression followed closely with slightly higher errors. Conversely, traditional models such as linear regression and decision tree showed significantly higher MAEs and RMSEs, highlighting their inadequacy for handling the complexities of WPT datasets. This research establishes a scalable and accurate framework for mutual inductance prediction, paving the way for improved efficiency in WPT systems.

关键词： data-driven algorithms machine learning mutual inductance planar coils wireless power transfer

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Physics-based battery SOC estimation: A joint data-driven and window-varying adaptive extended Kalman filter approach

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JOURNAL OF ENERGY STORAGE 2025年 121卷

作者： Gu, Chunfei Ni, Lianghua Tian, Xinxiang Fan, Chuanxin Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China Nanjing Inst Technol Sch Automat Nanjing 211167 Peoples R China

Accurate state of charge (SOC) estimation is critical to the safe and efficient operation of lithium-ion (Li-ion) batteries. In this work, a novel method that integrates a Random Forest-Least Absolute Shrinkage and Selection Operator (RF-LASSO) with a window-varying adaptive extended Kalman filter (WVAEKF) is applied to a physics-based battery model to estimate SOC. The algorithm integrates the strengths of data-driven methods and adaptive filtering to enhance the precision of SOC forecasting. The proposed physics-based battery model is a novel equivalent circuit model with diffusion dynamics. This model integrates the diffusion dynamics principle to provide a detailed account of the voltage loss encountered by Li-ion batteries during the diffusion phase. The WVAEKF algorithm's estimation error due to inaccurately capturing the distribution changes of the error innovation sequence and inherent instability of battery model, particularly under dynamic load conditions, is compensated by the introduction of a data-driven RF-LASSO model to improve the accuracy of SOC estimation. The experimental results show that the maximum SOC error of the SOC estimation method proposed in this study is less than 1% under the Dynamic Stress Test (DST) and Federal Urban Driving Schedule (FUDS) conditions. Compared with the conventional AEKF, this method has higher SOC estimation accuracy. Under DST and FUDS conditions at 25 degrees Celsius , the peak error of SOC estimation is significantly reduced by 58.73% and 57.53%, respectively. This improvement highlights the reliability and accuracy of the method in SOC estimation.

关键词： Lithium-ion battery State of charge Window-varying adaptive extended Kalman filter data-driven algorithms

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Enhancing the Prediction of Dam Deformations: A Novel data-driven Approach

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REMOTE SENSING 2025年第6期17卷 1026-1026页

作者： Ziemer, Jonas Stein, Gideon Wicker, Carolin Jaenichen, Jannik Kloepper, Daniel Last, Katja Denzler, Joachim Schmullius, Christiane Shadaydeh, Maha Dubois, Clemence Friedrich Schiller Univ Jena Dept Earth Observat Leutragraben 1 D-07743 Jena Germany Friedrich Schiller Univ Jena Comp Vis Grp Ernst Abbe Pl 2 D-07743 Jena Germany Ruhrverband Dept Water Econ Kronprinzenstr 29 D-45128 Essen Germany Inst Data Sci German Aerosp Ctr Malzerstr 3 D-07743 Jena Germany

Deformation monitoring is a critical task for dam operators to guarantee safe operation. Given an increasing number of extreme weather events caused by climate change, the precise prediction of dam deformations has become increasingly important. Traditionally, multiple linear regression models have been employed, utilizing in situ data from pendulum systems or trigonometric measurements. These methods sometimes suffer from sparse data, which typically represent deformations only at specific points on the dam, if such data are available at all. Technical advances in multi-temporal synthetic aperture radar interferometry (MT-InSAR), particularly Persistent Scatterer Interferometry (PSI), address these limitations by enabling monitoring in high spatial and temporal resolution, capturing dam deformations with millimeter precision, and providing extensive spatial coverage. This study advances traditional methods of dam monitoring by employing data-driven techniques and integrating Sentinel-1 C-band Persistent Scatterer (PS) time series alongside in situ data. Through a comprehensive evaluation of advanced data-driven approaches, we demonstrated considerable improvements in predicting dam deformations and evaluating their drivers. The analysis provided evidence for the following insights: First, the accuracy of current modeling approaches can be greatly improved by utilizing advanced feature engineering and data-driven model selection. The prediction performance of the pendulum data was improved by utilizing data-driven algorithms, reducing the mean absolute error from 0.51 mm in the baseline model (R2 = 0.92) to as low as 0.05 mm using the full model search space (R2 = 0.99). Although the model accuracy for the PS datasets (MAEmax: 0.81 mm) was about one order of magnitude lower than that for pendulum data, the mean absolute errors could be reduced by up to 0.25 mm. Second, by incorporating freely available PS time series into deformation prediction, dams can be monitore

关键词： dam monitoring data-driven algorithms deformation prediction PSI Sentinel-1

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Anomaly detection in multivariate time series of drilling data

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 237卷

作者： Altindal, Mehmet Cagri Nivlet, Philippe Tabib, Mandar Rasheed, Adil Kristiansen, Tron Golder Khosravanian, Rasool Norwegian Univ Sci & Technol Dept Engn Cybernet Trondheim Norway Sintef Ind Dept Appl Geosci Trondheim Norway Sintef Digital Dept Math & Cybernet Trondheim Norway Aker BP Stavanger Norway Equinor ASA Stavanger Norway Halliburton Tananger Norway

Different clusters of abnormal activities often arise within same temporal domain of drilling operations. This contrasts with employing simplified scenarios, such as anomaly detection models for specific issues like stuck pipe or loss circulation. The dynamic nature of drilling environments demands a holistic framework that can adapt to evolving conditions and detect anomalies beyond predefined categories. There exists a need to evaluate the performance of various data -driven models and achieve a broader applicability of anomaly detection instead of focusing on a single abnormal activity type. This study presents a generalized framework designed to detect anomalous events in drilling operations. Three different unsupervised learning algorithms, principal component analysis (PCA), isolation forest (IF) and lstm-autoencoder (LSTM-AE), are used to determine abnormal patterns and irregularities in the drilling data. PCA is chosen for its interpretability and efficiency in handling high -dimensional data by reducing the dimensionality. IF is selected for its effectiveness in isolating anomalies, making it robust for scenarios where anomalies exhibit distinct patterns. LSTM-AE is employed due to its capability to capture temporal dependencies and nonlinear patterns in time series data. A multivariate time series of drilling data from an offshore well is collected to use in this work. The dataset comprises approximately six months of drilling operations, including drilling and non -drilling periods. The input variables are chosen from the common controllable parameters continuously monitored by the drilling crew. The pre-processed drilling data with input features is organized into groups based on each drilling section. These unlabeled data is split into train, validation and test sets. Cross -validation (CV) is applied into train and validation sets to prevent overfitting and tune the hyperparameters. The test data is used to evaluate the generalizability and robustness

关键词： Drilling Multivariate time series data analysis Anomaly detection data-driven algorithms Machine learning

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TS-RTPM-Net: data-driven Tensor Sketching for Efficient CP Decomposition

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IEEE TRANSACTIONS ON BIG data 2024年第1期10卷 1-11页

作者： Cao, Xingyu Zhang, Xiangtao Zhu, Ce Liu, Jiani Liu, Yipeng Univ Elect Sci & Technol China UESTC Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Tensor decomposition is widely used in feature extraction, data analysis, and other fields. As a means of tensor decomposition, the robust tensor power method based on tensor sketch (TS-RTPM) can quickly mine the potential features of tensor, but in some cases, its approximation performance is limited. In this paper, we propose a data-driven framework called TS-RTPM-Net, which improves the estimation accuracy of TS-RTPM by jointly training the TS value matrices with the RTPM initial matrices. It also uses two greedy initialization algorithms to optimize the TS location matrices. In addition, TS-RTPM-Net accelerates TS-RTPM by using fast power iteration modules. Comparative experiments on real-world datasets verify that TS-RTPM-Net outperforms TS-RTPM in terms of estimation accuracy, running speed, and memory consumption.

关键词： Accelerated tensor decomposition data-driven algorithms dimensionality reduction greedy algorithms low-rank tensor approximation

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From intensive care monitors to cloud environments: a structured data pipeline for advanced clinical decision support

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EBIOMEDICINE 2025年 111卷 105529-105529页

作者： Noteboom, Sijm H. Kho, Eline Galanty, Maria ten Bookum, Frans C. P. Veelo, Denise P. Vlaar, Alexander P. J. van der Ster, Bjoern J. P. Univ Amsterdam Dept Anaesthesiol Amsterdam UMC Meibergdreef 9 NL-1105 AZ Amsterdam Netherlands Univ Amsterdam Dept Intens Care Amsterdam UMC Meibergdreef 9 NL-1105 AZ Amsterdam Netherlands Univ Amsterdam Informat Inst Meibergdreef 9 NL-1105 AZ Amsterdam Netherlands Amsterdam UMC Data Management Meibergdreef 9 NL-1105 AZ Amsterdam Netherlands Univ Amsterdam Lab Expt Intens Care & Anaesthesiol Amsterdam UMC Meibergdreef 9 NL-1105 AZ Amsterdam Netherlands

Background Clinical decision-making is increasingly shifting towards data-driven approaches and requires large databases to develop state-of-the-art algorithms for diagnosing, detecting and predicting diseases. The intensive care unit (ICU), a data-rich setting, faces challenges with high-frequency, unstructured monitor data. Here, we showcase a successful example of a data pipeline to efficiently move patient data to the cloud environment for structured storage. This supports individual patient analysis, enables largescale retrospective research, and the development of data-driven algorithms. Methods Since June 2021, ICU data of the Amsterdam UMC have been collected and stored in a third-party cloud environment which is hosted on large virtual servers. The feasibility of the pipeline will be demonstrated with the available data through research and clinical use cases. Furthermore, privacy, safety, data quality, and environmental impact are carefully considered in the cloud storage transition. Findings Over two years, data from over 9000 patients have been stored in the cloud. The availability, agility, computational power, high uptime, and streaming data pipelines allow for large retrospective analyses as well as the opportunity to implement real-time prediction of critical events with machine learning algorithms. Critical events can be accessed by applying keyword search in the natural language data, annotated by the treating team. Besides, the cloud environment offers storage of institutional data enabling evaluation of healthcare. Interpretation The combined data and features of cloud environments offer support for predictive algorithm development and implementation, healthcare evaluation, and improved individual patient care. Funding University of Amsterdam Research Priority Agenda Program AI for Heath Decision-Making. Copyright (c) 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/

关键词： Cloud environments data-driven algorithms data management Real-time decision-making

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Optimal perfect corrective maintenance policy for a system with multiple components using data-driven decision-making methods

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第1期40卷 472-498页

作者： Misaii, Hasan Fouladirad, Mitra Haghighi, Firoozeh Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran Univ Technol Troyes LISTEN Troyes France Aix Marseille Univ CNRS Cent Marseille Marseille France

This paper considers a multicomponent series system to be inspected periodically. It is assumed that the exact cause of system failure might be unknown, and we only know that it belongs to a subset of all components, called masked set. In this case, it is said that the exact cause of failure is masked. A perfect corrective maintenance policy is applied to repair the system, such that a failed component is replaced by a new one at the first inspection time after failure. The distance between each two inspections is considered a decision parameter that should be optimized. Three levels of information availability are considered, including full, partial, and noninformation availability, in which the maintenance policy should be optimized. The former is considered a benchmark, and the statistical and machine learning data-driven algorithms are used to estimate the cost for the other two information levels. Eventually, using Monte Carlo simulation studies, the proposed method application is analyzed.

关键词： data-driven algorithms masked data perfect corrective repair statistical decision-making methods

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