Mapping Table Research Articles

Broadband photonic frequency measurement based on the frequency-to-carrier phase-shifted mapping and finite extinction ratio

A novel frequency measurement is proposed based on frequency-to-carrier phase-shifted mapping and finite extinction ratio. Due to the frequency dependence of the dispersion phase shift, carrier phase-shifted double-sideband modulation and dispersive effect are combined for effective mapping. Additionally, the finite extinction ratio is introduced to mitigate the influence of unideal parameters on measurement error. To achieve this, a dual-parallel Mach-Zehnder modulator and a dispersion compensating fibre are combined, and a power minimization problem is formulated to establish a mapping table. Theoretical analysis and simulation results show the frequency measurement range over 30 GHz with errors below 180 MHz. Furthermore, the measurement error is reduced below 40 MHz after considering the finite extinction ratio.

Influence of peak exercise-induced blood pressure on basal to apical myocardial wall thickness in elite athletes: a sex-specific CMR study

Abstract Background Understanding the sex-specific physiological remodelling response of the myocardial walls to peak exercise-induced blood pressure (SBPpeak) is essential for distinguishing healthy adaptation from pathological remodelling in athletes. Purpose We investigated how SBPpeak relates to myocardial wall thickness measured using cardiac magnetic resonance imaging (CMR) in elite athletes. Methods Cross-sectional analysis of healthy elite athletes included in the ELITE study who underwent both cardiopulmonary exercise testing (CPET) and CMR. Imaging software’s built-in AI option determined average end-diastolic wall thicknesses of all sixteen AHA-defined segments on short-axis cine images with four-chamber long-axis reference. Our primary metrics of interest were Spearman's correlations between SBPpeak and left ventricular mass indexed to body surface area (LVM/BSA), as well as average wall thickness across the sixteen myocardial segments, stratified by sex and endurance/non-endurance sports status. Secondary analyses involved regression models to evaluate the influence of SBPpeak on myocardial wall thickness, adjusting for sex, body size, CPET variables and sport type. Results In 75 athletes (median age: 24; 71% female; 61% engaged in endurance sports), CPET and CMR were performed within a median interval of 5 months [interquartile range: 1-13 months]. SBPpeak was similar in female and male athletes (170 [160-180] vs. 174 [169-184] mmHg, p=0.121, respectively) and in endurance and non-endurance athletes (both 170 [160-180] mmHg, p=0.625). Female elite athletes showed weak to moderate positive correlations between SBPpeak and the majority of basal and mid-ventricular AHA segments (rho = 0.27 – 0.48, Figure). This was most pronounced in the mid-ventricular anterolateral segment (AHA06: rho = 0.48, p<0.001). LVM/BSA in females was positively correlated with SBPpeak (rho = 0.31, p=0.025). Male elite athletes demonstrated greater absolute wall thickness across all AHA segments compared to females, however, there was a moderate correlation with SBPpeak exclusive to the basal anterior segment (AHA01: rho = 0.48, p=0.028). Backward stepwise multivariate regression analyses (Table) revealed SBPpeak as an independent predictor of the thickness of all basal AHA segments except for the basal inferoseptal segment (AHA03), and the mid-anterior and anterolateral segments. Associationswere maintained after adjustment for maximum wattage, body surface area, and sex, with type of sports consistently excluded from the models. Conclusion In the extreme phenotypes of elite athletes, SBPpeak correlates especially with basal myocardial thickness, independent of sex, BSA and maximum work rate influences. We found stronger associations between SBPpeak and myocardial adaptation in female athletes. These findings suggest a paramount role of exercise-induced blood pressure on basal myocardial remodelling surpassing the effect of sports type.Figure:segmental polar plot (heat map)Table:GAM regression models

Texture-Image-Oriented Coverless Data Hiding Based on Two-Dimensional Fractional Brownian Motion

In an AI-immersing age, scholars look for new possibilities of employing AI technology to their fields, and how to strengthen security and protect privacy is no exception. In a coverless data hiding domain, the embedding capacity of an image generally depends on the size of a chosen database. Therefore, choosing a suitable database is a critical issue in coverless data hiding. A novel coverless data hiding approach is proposed by applying deep learning models to generate texture-like cover images or code images. These code images are then used to construct steganographic images to transmit covert messages. Effective mapping tables between code images in the database and hash sequences are established during the process. The cover images generated by a two-dimensional fractional Brownian motion (2D FBM) are simply called fractional Brownian images (FBIs). The only parameter, the Hurst exponent, of the 2D FBM determines the patterns of these cover images, and the seeds of a random number generator determine the various appearances of a pattern. Through the 2D FBM, we can easily generate as many FBIs of multifarious sizes, patterns, and appearances as possible whenever and wherever. In the paper, a deep learning model is treated as a secret key selecting qualified FBIs as code images to encode corresponding hash sequences. Both different seeds and different deep learning models can pick out diverse qualified FBIs. The proposed coverless data hiding scheme is effective when the amount of secret data is limited. The experimental results show that our proposed approach is more reliable, efficient, and of higher embedding capacity, compared to other coverless data hiding methods.

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics.

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

Static Scheduling of Weight Programming for DNN Acceleration with Resource Constrained PIM

Most existing architectural studies on ReRAM-based processing-in-memory (PIM) DNN accelerators assume that all weights of the DNN can be mapped to the crossbar at once. However, these studies are over-idealized. ReRAM crossbar resources for calculation are limited because of technological limitations, so multiple weight mapping procedures are required during the inference process. In this article, we propose a static scheduling framework which generates the mapping between DNN weights and ReRAM cells with minimum runtime weight programming cost. We first build a ReRAM crossbar programming latency model by simultaneously considering the DNN weight patterns, ReRAM programming operations, and PIM architecture characteristics. Then, the model is used in the searching process to obtain an optimized weight-to-OU mapping table with minimum online programming latency. Finally, an OU scheduler is used to coordinate the activation sequences of OUs in the crossbars to perform the inference computation correctly. Evaluation results show the proposed framework significantly reduces the weight programming overhead and the overall inference latency for various DNN models with different input datasets.

Towards Streamlined Transparent Data Linkage

Linked data is a powerful resource within data analytics and population-level research. However, methods for linkage vary and the choice of approach can impact downstream usage of data by introducing assumptions and biases in resulting links. Selecting stringent linkage methods helps strengthen identified links at risk of missing links; meanwhile, lenient rules or ill-considered comparisons may introduce false positive links. Therefore, the approach is non-trivial, requiring careful selection of preprocessing steps, model development and quality review to ensure suitable outputs, which can require significant human expertise and insight. Real-world population-scale linkage can benefit from automation and scalability offered within modern data centres, with many tasks eligible for pipelining, such as applying predefined cleaning routines, training defined models, and generating mapping tables. Despite this, there are still pinch points requiring human interaction, such as selecting appropriate linkage fields, blocking rules and comparison methods, and reviewing quality of predictions. We present an approach to provide scalable automation in linkage pipelines, whilst retaining transparency of the linkage process for downstream users, providing them with a dataset’s life history. The work output for a given dataset is a versioned catalogue documenting the dataset’s journey, with transparent reporting of data origin, linkage settings, routines, and privacy-preserving quality analysis for inspection. This gives researchers insight into how it may affect their data and provides confidence in data usage. These insights also work in both directions, allowing users to provide feedback and iteratively refine linkage approaches.

Terminal sequence consistency verification method for small diameter abreast optical fibers based on computer vision

In a kind of precision industrial equipment, small diameter abreast optical fibers are used for high-speed communication among functional nodes. The arrangement order at both terminals of the abreast optical fibers need to comply with communication protocols. In this paper, we propose an automatic terminal sequence consistency verification method based on computer vision. The Hue Saturation Value (HSV) color space is used for improving the image feature extraction capability. An abreast optical fiber sequence dictionary which converts the protocol logic into an input-output mapping table is provided to follow protocol confidentiality and improve inspecting speed. A light control baffle position adaptive algorithm is designed for improving the accuracy of optical fiber incident light control. The experimental results show that the method can achieve the conductivity inspection of 1 optical fiber every 50 seconds, and the inspection accuracy is over 96.5%, which generally improves the inspection efficiency by 45% compared with manual inspection.

Development of a Mapping Table for Nursing Notes Based on Nurses' Concerns in ICU Patients.

This study aimed to develop a mapping table that connects nursing notes with standard terminology, focusing on nurses' concerns for ICU patients. After extracting nursing notes from a publicly accessible database, a research team, including a nursing informatics professor and researchers with ICU experience, developed a mapping table through a four-step process: initially reviewing literature on nurses' concerns, then extracting nursing notes from MIMIC IV and filtering the duplicate notes, subsequently defining and coding these concerns, and finally mapping them according to the CCC. Of 11,430,637 unstructured nursing notes from MIMIC IV, 265 unique notes remained after deduplication, with 208 notes reflecting nurses' concerns and categorized into 15 groups aligned with CCC. This mapping table will be a fundamental tool for predicting clinical deterioration in ICU patients by identifying important lexicons through natural language processing.

Factors Associated with Safe Sexual Behavior Among MSM Living with HIV: A Scoping Review

Background: Human Immunodeficiency Virus (HIV) is a virus that attacks lymphocytes, leading to a gradual decline in the body's immune system and increasing susceptibility to various diseases, including Acquired Immunodeficiency Syndrome (AIDS). Men who have sex with men (MSM) are at a higher risk of contracting HIV/AIDS due to unsafe sexual behavior. Objective: This scoping review aims to explore the factors associated with safe sexual behavior in MSM Living with HIV. Method: The scoping review is based on the framework by Arksey and O'Malley. A literature search involved various databases, such as EBSCO, PubMed, Science Direct, and CINAHL I, with the keywords: "Factors Associated" AND "Safe Sex Behavior" AND "Men Who Have Sex With Men OR MSM OR LSL". The selection process for research articles involved the use of inclusion criteria. The results were analyzed by extracting data into a data mapping table (PRISMA Flow Diagram). Results: 20 articles discussed factors related to safe sexual behavior among men with HIV/AIDS. These factors include individual perception, knowledge, and values. Discussion: It can be concluded that individual perception strongly influences safe sexual behavior among men, as individual perceptions can guide and motivate behavior.

Snake swarm optimization‐based deep reinforcement learning for resource allocation in edge computing environment

SummaryMobile edge computing has become popular in the past few years as a means of creating computing resources close to end‐user nodes at the network edge. Nodes—end users—demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor‐based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA).

Data Hiding Methods Using Voting Strategy and Mapping Table

<p>With advancements in technology, the study of data hiding (DH) in images has become more and more important. In this paper, we introduce a novel data hiding scheme that employs a voting strategy to predict pixels based on their neighbors, then embeds data into the predicted pixels according to a designed mapping table. To extract the information, it is only necessary to use the voting strategy to predict the pixels again, then to compare the predicted and hidden pixels to extract the secret data with the assistance of the mapping table. Our experimental results demonstrate that the proposed hiding scheme has a high embedding capacity, and preserves a satisfactory visual quality. Additionally, it attains a high peak signal-to-noise ratio (PSNR) even at high embedding capacity.</p> <p> </p>

An Environment-Aware Adaptive Data-Gathering Method for Packet-Level Index Modulation in LPWA.

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively.

Data Structure and Management Protocol to Enhance Name Resolving in Named Data Networking

Named Data Networking (NDN) is a future Internet architecture that requires an Inter-Domain Routing (IDR) to route its traffic globally. Address resolution is a vital component of any IDR system that relies on a Domain Name System (DNS) resolver to translate domain names into their IP addresses in TCP/IP networks. This paper presents a novel two-element solution to enhance name-to-delivery location resolution in NDN networks, consisting of (1) a mapping table data structure and a searching mechanism and (2) a management protocol to automatically populate and modify the mapping table. The proposed solution is implemented and tested on the Peer Name Provider Server (PNPS) mapping table, and its performance is compared with two other algorithms: component and character tries. The findings show a notable enhancement in the operational speed of the mapping table when utilizing the proposed data structure. For instance, the insertion process is 37 times faster compared to previous algorithms.

SPC-Indexed Indirect Branch Hardware Cache Redirecting Technique in Binary Translation

In the domain of process virtual machine (PVM) binary translation, the difference in address space layout between the guest program and the translated program requires the recalculation of jump instruction targets, resulting in suboptimal execution efficiency. This paper presents a novel method called SPC-Indexed Indirect Branch Hardware Cache Redirecting (SPCIC) technique. SPCIC utilizes specialized branch instruction to represent indirect branches from guest programs while frequently-used target addresses are cached in a customized hardware mapping table. When translating an indirect branch, SPCIC queries the jump target cache first to achieve a fast redirection unless the destination address is not cached. Besides, SPCIC merely falls back to the software-based remapping approach when the query fails, improving the translation efficiency to the greatest extent. SPCIC is implemented on the QEMU platform to accelerate the translation of ARM payloads into RISC-V. Experiments are carried on SPEC2006 to demonstrate the effectiveness of SPCIC for reducing the runtime overhead of indirect branch translation. The experimental results indicate up to 11% average improvement and 35% maximum improvement are obtained on the selected benchmark.

Abstract 4866: To weight or not to weight? Studying the effect of selection bias in three EHR-linked biobanks with applications to colorectal cancer

Abstract The use electronic health record (EHR)-linked biobanks, including All of Us (AOU), the Michigan Genomics Initiative (MGI), and the UK Biobank (UKB), has become increasingly common in cancer research. However, each has a different participant recruitment strategy resulting in non-probability samples, which can lead to selection bias. An unanswered question is whether researchers should use selections weights when analyzing these data. Using colorectal cancer as the test case, we investigate the impact of using such weights on descriptive and analytic tasks in these three biobanks. We curated sociodemographic and clinical diagnosis data for 726,841 individuals (n = 244,071, 81,243, and 401,167, in AOU, MGI, and UKB, respectively). EHR ICD (diagnosis) code data were mapped to broader 2,042 codes, called phecodes, using the new phecode X mapping table developed by researchers at Vanderbilt University. Selection weights were constructed in AOU and MGI to make them more representative of the US adult population, using data from the 2019 National Health Interview Survey; previously described weights for the UKB were used. We estimated phenomewide prevalences, pairwise correlations, and phenome dimensionality (via principal components analysis). To investigate the role of weighting on conclusions from hypothesis testing and association estimation, we conducted a colorectal cancer phenomewide association study and estimated the sex-colorectal cancer log-odds ratio, respectively. We found that phecode prevalences in AOU and MGI decreased following weighting (median prevalence ratio [MPR]: 0.82 and 0.61, respectively) while those in UKB increased (MPR: 1.06). MGI is enriched for phecodes compared to AOU (MPR: 1.15) and UKB (MPR: 6.28). Weighting PCA had minimal impact on phenome dimensionality (e.g., 732 PCs explaining 95% of cumulative variation in AOU vs. 711 after weighting). Weighted PheWAS for colorectal cancer identified 21 hits not identified in unweighted PheWAS, but only one was from a new disease category. Weighted estimates of the female-colorectal cancer log-odds ratio overlapped with the benchmark range in MGI and UKB though resulted in a null association in AOU. Weighting had limited impact on dimensionality estimation and hypothesis testing but were important to consider for prevalence and association estimation. Results from untargeted analyses should be followed up with targeted analyses using curated weights. The importance of weights depends on the estimates obtained and inference goals and can improve the representativeness of results for cancer-related outcomes based on EHR-linked biobank data. Importantly, EHR-linked biobanks should be explicit in reporting recruitment and selection mechanisms and when possible, supply selection weights to researchers for population-based inference along with a clear definition of the target population. Citation Format: Maxwell Salvatore, Ritoban Kundu, Xu Shi, Christopher R. Friese, Seunggeun Lee, Lars G. Fritsche, Alison M. Mondul, David Hanauer, Celeste L. Pearce, Bhramar Mukherjee. To weight or not to weight? Studying the effect of selection bias in three EHR-linked biobanks with applications to colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4866.

GIS TOOLS IN THE CONSERVATION AND SUSTAINABLE DEVELOPMENT NATIONAL PARKS, FORESTS AND RURAL AREAS

The problem of optimising the location of crops/plots in protected areas, national parks, farmland or forests involves trying to reconcile their proper functioning with nature conservation, the preservation of unique ecosystems or sustainable development. Such areas are crucial for the preservation of biodiversity and wildlife. This paper examines and discusses the impact of land consolidation in rural areas using GIS (Geographic Information System) tools. It performs a series of network analyses to determine the real distances (following the road network) and linear distances (rectilinear distances) between habitats and farmland. A tool was also developed to automate this process. The results obtained were visualised using map compositions, tables and graphs. The implemented project had a significant beneficial effect on the change in the land structure in the analysed village. The number of registered plots decreased by 40% (from 1,189 to 711) due to the consolidation process.

Improvements in Neoplasm Classification in the International Classification of Diseases, Eleventh Revision: Systematic Comparative Study With the Chinese Clinical Modification of the International Classification of Diseases, Tenth Revision.

The International Classification of Diseases, Eleventh Revision (ICD-11) improved neoplasm classification. We aimed to study the alterations in the ICD-11 compared to the Chinese Clinical Modification of the International Classification of Diseases, Tenth Revision (ICD-10-CCM) for neoplasm classification and to provide evidence supporting the transition to the ICD-11. We downloaded public data files from the World Health Organization and the National Health Commission of the People's Republic of China. The ICD-10-CCM neoplasm codes were manually recoded with the ICD-11 coding tool, and an ICD-10-CCM/ICD-11 mapping table was generated. The existing files and the ICD-10-CCM/ICD-11 mapping table were used to compare the coding, classification, and expression features of neoplasms between the ICD-10-CCM and ICD-11. The ICD-11 coding structure for neoplasms has dramatically changed. It provides advantages in coding granularity, coding capacity, and expression flexibility. In total, 27.4% (207/755) of ICD-10 codes and 38% (1359/3576) of ICD-10-CCM codes underwent grouping changes, which was a significantly different change (χ21=30.3; P<.001). Notably, 67.8% (2424/3576) of ICD-10-CCM codes could be fully represented by ICD-11 codes. Another 7% (252/3576) could be fully described by uniform resource identifiers. The ICD-11 had a significant difference in expression ability among the 4 ICD-10-CCM groups (χ23=93.7; P<.001), as well as a considerable difference between the changed and unchanged groups (χ21=74.7; P<.001). Expression ability negatively correlated with grouping changes (r=-.144; P<.001). In the ICD-10-CCM/ICD-11 mapping table, 60.5% (2164/3576) of codes were postcoordinated. The top 3 postcoordinated results were specific anatomy (1907/3576, 53.3%), histopathology (201/3576, 5.6%), and alternative severity 2 (70/3576, 2%). The expression ability of postcoordination was not fully reflected. The ICD-11 includes many improvements in neoplasm classification, especially the new coding system, improved expression ability, and good semantic interoperability. The transition to the ICD-11 will inevitably bring challenges for clinicians, coders, policy makers and IT technicians, and many preparations will be necessary.

Impact mapping tool for interdisciplinary research institutes

The identification of societal impacts resulting from interdisciplinary research is a complex task. This complexity is particularly evident for agricultural research, which strives to mitigate trade-offs between agricultural production and multiple societal targets, including food provision, ecosystem services, soil and biodiversity conservation, climate action and rural development, all contributing to the Sustainable Development Goals (SDGs). To address this issue, this article presents an Impact Mapping Framework for systematically assessing such societal impacts. The use case on soil health shows how ZALF, an interdisciplinary agricultural landscape research institute in Germany, uses the framework to map its research activities and their contributions towards the improvement of ecosystem services and biodiversity, and the SDGs. The Impact Mapping Framework is presented in a circular format, with research activities mapped in the outermost circle and their contributions to SDGs in the center. The article provides step-by-step instructions for applying the impact mapping framework and a supplementary Impact Map Table. It concludes by emphasizing the importance of using a systemic approach to formatively assess the societal impact of interdisciplinary research, to visualize it for external communication, to identify research gaps and collaboration opportunities, and to build impact literacy.

한글과 태국어간의 유사 폰트 매칭을 위한 매핑 테이블 작성 및 타이포그래피 분석

한글과 태국어간의 유사 폰트 매칭을 위한 매핑 테이블 작성 및 타이포그래피 분석

Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.

Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability. Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes). The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names. This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.