Many hospitals have introduced the Clinical Path (Path) to improve medical procedures. A Path is a way to manage care and check lists for a certain disease, providing a useful tool for hospital management. Paths can help hospitals reduce the duration of hospitalization and variations in care of patients while increasing hospital revenue. Nowadays, Paths are made by each hospital and there is no standard format. Benchmark testing between Paths used by different hospitals is important for evaluating medical practices, in order to develop and improve more effective practices. However, as the formats used in Paths are not standardized, benchmark testing of Paths is no easy task. To start benchmark testing of Paths, we compare medication in Paths and introduce description rules of medication in XML. Based on these, we developed a prototype system that enables us to compare the difference of medications in Paths prescribed between multiple of hospitals.
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http://dx.doi.org/10.1007/s10916-005-6110-8 | DOI Listing |
Water Res X
May 2025
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Pumps in Water Distribution Networks (WDNs) adequately provide effective pressure where low elevation or high head losses are detected within the system. One of the most effective strategies to ensure economic sustainability is Pump Scheduling (PS), assuring the optimization of pump management and enabling significant energy cost saving. Meta-heuristic algorithms can be applied to Pump Scheduling, given their ability to provide reliable global solutions, further complemented by limited computational efforts.
View Article and Find Full Text PDFBackground: The development and approval of novel drugs are typically time-intensive and expensive. Leveraging a computational drug repurposing framework that integrates disease-relevant genetically regulated gene expression (GReX) and large longitudinal electronic medical record (EMR) databases can expedite the repositioning of existing medications. However, validating computational predictions of the drug repurposing framework remains a challenge.
View Article and Find Full Text PDFF1000Res
January 2025
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Baden-Württemberg, Germany.
Background: Synthetic data's utility in benchmark studies depends on its ability to closely mimic real-world conditions and reproduce results obtained from experimental data. Building on Nearing et al.'s study (1), who assessed 14 differential abundance tests using 38 experimental 16S rRNA datasets in a case-control design, we are generating synthetic datasets that mimic the experimental data to verify their findings.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory for Information Science of Electromagnetic Waves and the Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Addressing the shortcomings of the Sparrow Search Algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search Algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer's position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the Coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India.
This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimization to facilitate efficient model selection for lane segmentation. The benchmarking dataset namely TuSimple is used for training, validating and testing the proposed and existing lane detection techniques.
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