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RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning

BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第SUPPL 2期24卷 259页

作者： Song, Won Hoon Park, Meeyoung Pusan Natl Univ Sch Med Dept Urol Yangsan South Korea Pusan Natl Univ Yangsan Hosp Dept Urol Yangsan South Korea Kyungnam Univ Dept Comp Engn 7 Gyeongnamdaehak Ro Changwon Si 51767 Gyeongsangnam D South Korea

BackgroundThe population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatment options for renal cell carcinoma are diverse, depending on clinical stage and histologic characteristics. Hence, this study aims to develop a machine learning based clinical decision-support system that recommends personalized treatment tailored to the individual health condition of each *** reviewed the real-world medical data of 1,867 participants diagnosed with renal cell carcinoma between November 2008 and June 2021 at the Pusan National University Yangsan Hospital in South Korea. data were manually divided into a follow-up group where the patients did not undergo surgery or chemotherapy (Surveillance), a group where the patients underwent surgery (Surgery), and a group where the patients received chemotherapy before or after surgery (Chemotherapy). Feature selection was conducted to identify the significant clinical factors influencing renal cell carcinoma treatment decisions from 2,058 features. These features included subsets of 20, 50, 75, 100, and 150, as well as the complete set and an additional 50 expert-selected features. We applied representative machine learning algorithms, namely Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). We analyzed the performance of three applied machine learning algorithms, among which the GBM algorithm achieved an accuracy score of 95% (95% CI, 92-98%) for the 100 and 150 feature sets. The GBM algorithm using 100 and 150 features achieved better performance than the algorithm using features selected by clinical experts (93%, 95% CI 89-97%).ConclusionsWe developed a preliminary personalized treatment decision-support system (TDSS) called "RCC-Supporter" by applying machine learning (ML) algorithms to determine personalized treatment for the various clinical situations of RCC

关键词： Renal cell carcinoma Medical big data Clinical decision support system Machine learning common data model

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allofus: an R package to facilitate use of the All of Us Researcher Workbench

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2024年第12期31卷 3013-3021页

作者： Smith, Louisa H. Cavanaugh, Robert Northeastern Univ Roux Inst 100 Fore St Portland ME 04101 USA Northeastern Univ Bouve Coll Hlth Sci Dept Publ Hlth & Hlth Sci Boston MA 02115 USA

Objectives Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time *** audience All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership common data model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the *** We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.

关键词： All of Us R package common data model OMOP CDM OHDSI

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FOUNTAIN: a modular research platform for integrated real-world evidence generation

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 224页

作者： Oberprieler, Nikolaus G. Pladevall-Vila, Manel Johannes, Catherine Layton, J. Bradley Golozar, Asieh Lavallee, Martin Liu, Fangfang Kubin, Maria Vizcaya, David Bayer AS Oslo Norway RTI Hlth Solut Barcelona Spain Henry Ford Hlth Syst Ctr Hlth Policy & Hlth Serv Res Detroit MI USA RTI Hlth Solut Waltham MA USA RTI Hlth Solut Res Triangle Pk NC USA Odysseus Inc New York NY USA Bayer AG Wuppertal Germany Bayer Pharmaceut St Joan Despi Spain

BackgroundReal-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through *** paper's aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN;FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform's strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)-based program. FOUNTAIN's overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its ***, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase

关键词： Cohort study common data model FOUNTAIN Harmonization Methodology Real-world data

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Semantic Harmonization of Alzheimer's Disease datasets Using AD-Mapper

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JOURNAL OF ALZHEIMERS DISEASE 2024年第4期99卷 1409-1423页

作者： Wegner, Philipp Balabin, Helena Ay, Mehmet Can Bauermeister, Sarah Killin, Lewis Gallacher, John Hofmann-Apitius, Martin Salimi, Yasamin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 St Augustin Germany Rhein Friedrich Wilhelms Univ Bonn Bonn Germany German Ctr Neurodegenerat Dis DZNE Venusberg Campus 1 D-53121 Bonn Germany Katholieke Univ Leuven Lab Cognit Neurol Dept Neurosci Leuven Belgium Katholieke Univ Leuven Language Intelligence & Informat Retrieval Lab Dept Comp Sci Leuven Belgium Rhein Friedrich Wilhelms Univ Bonn Bonn Aachen Int Ctr IT Bonn Germany Univ Oxford Warneford Hosp Dept Psychiat Oxford England SYNAPSE Res Management Partners Barcelona Spain

Background: Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. Objective: As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. Methods: We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. Results: Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. Conclusion: AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.

关键词： Alzheimer's disease automatic data harmonization cohort study common data model data interoperability semantic mapping

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OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2024年第3期31卷 583-590页

作者： Reich, Christian Ostropolets, Anna Ryan, Patrick Rijnbeek, Peter Schuemie, Martijn Davydov, Alexander Dymshyts, Dmitry Hripcsak, George Observat Hlth Data Sci & Informat Coordinating Ctr New York NY 10032 USA Northeastern Univ OHDSI Ctr Roux Inst 100 Fore St Portland ME 04101 USA Erasmus MC Dept Med Informat NL-3015 GD Rotterdam Netherlands Columbia Univ Med Ctr Dept Biomed Informat New York NY 10032 USA Odysseus Data Serv Cambridge MA 02142 USA Janssen Res & Dev Observat Hlth Data Analyt Titusville NJ 08560 USA

Importance The Observational Health data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data *** and methods We created the OHDSI Standardized Vocabularies-a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent *** The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research-the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard *** and conclusion OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

关键词： OHDSI controlled vocabulary common data model observational data

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Association Between Ursodeoxycholic Acid and Clinical Outcomesin Patients With COVID-19 Infection:Population-Based CohortStudy

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JMIR PUBLIC HEALTH AND SURVEILLANCE 2024年 10卷 e59274页

作者： Lee, Hyunjun Kim, Min Gul Yeom, Sang Woo Noh, Sang Jae Jeong, Cho Yun Kim, Minji Kang, Min Gu Ko, Ji Hoon Park, Su Cheol Kweon, Hyeok Tae Sim, Sang Il Lee, Hyun You, Yeon Seok Kim, Jong Seung Jeonbuk Natl Univ Med Sch Dept Otorhinolaryngol Head & Neck Surg Jeonju South Korea Jeonbuk Natl Univ Med Sch Dept Pharmacol Jeonju South Korea Jeonbuk Natl Univ Med Sch Dept Med Informat Jeonju South Korea Jeonbuk Natl Univ Jeonbuk Natl Univ Hosp Res Inst Clin Med Biomed Res Inst Jeonju South Korea Jeonbuk Natl Univ Hosp Big Data Ctr Jeonju South Korea Hanyang Univ Coll Med Dept Internal Med Seoul South Korea Jeonbuk Natl Univ Sch Med & Hosp Dept Med Informat Dept Otorhinolaryngol 20 Geonji Ro Jeonju 54907 Jeonbuk Do South Korea

Background: Several studies have investigated the relationship between ursodeoxycholic acid (UDCA) and COVID-19 ***, complex and conflicting results have generated confusion in the application of these results. Objective: We aimed to investigate whether the association between UDCA and COVID-19 infection can also be demonstratedthrough the analysis of a large-scale cohort. Methods: This retrospective study used local and nationwide cohorts, namely, the Jeonbuk National University Hospital intothe Observational Medical Outcomes Partnership common data model cohort (JBUH CDM) and the Korean National HealthInsurance Service claim-based database (NHIS). We investigated UDCA intake and its relationship with COVID-19 susceptibilityand severity using validated propensity score ***: Regarding COVID-19 susceptibility, the adjusted hazard ratio (aHR) value of the UDCA intake was significantlylowered to 0.71 in the case of the JBUH CDM (95% CI 0.52-0.98) and was significantly lowered to 0.93 (95% CI 0.90-0.96) inthe case of the NHIS. Regarding COVID-19 severity, the UDCA intake was found to be significantly lowered to 0.21 (95% CI0.09-0.46) in the case of JBUH CDM. Furthermore, the aHR value was significantly lowered to 0.77 in the case of NHIS (95%CI 0.62-0.95). Conclusions: Using a large-scale local and nationwide cohort, we confirmed that UDCA intake was significantly associatedwith reductions in COVID-19 susceptibility and severity. These trends remained consistent regardless of the UDCA dosage. Thissuggests the potential of UDCA as a preventive and therapeutic agent for COVID-19 infection.

关键词： Covid 19 COVID-19 ursodeoxycholic acid population-based cohort study SARS-CoV-2 Coronavirus pandemic population-based retrospective cohort study propensity score UDCA public health common data model clinical severity

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INSIGHT: A Tool for Fit-for-Purpose Evaluation and Quality Assessment of Standardized Observational data Sources for Real World Evidence on Medicine and Vaccine Safety

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2025年第1期34卷 e70089页

作者： Hoxhaj, Vjola Navarro, Constanza L. Andaur Riera-Arnau, Judit Elbers, Roel J. H. J. Alsina, Ema Dodd, Caitlin Sturkenboom, Miriam C. J. M. Univ Utrecht Univ Med Ctr Utrecht Julius Ctr Hlth Sci & Primary Care Utrecht Netherlands Univ Autonoma Barcelona Vall dHebron Hosp Univ Clin Pharmacol Serv Vall dHebron Barcelona Hosp Campus Barcelona Spain

PurposeTo describe the development of INSIGHT, a real-world data quality tool to assess completeness, consistency, and fitness-for-purpose of observational health data *** designed a three-level pipeline with data quality assessments (DQAs) to be performed in ConcePTION common data model (CDM) instances. The pipeline has been coded using *** is an open-source tool that identifies potential data quality issues in CDM-standardized instances through the systematic execution and summary of over 588 configurable DQAs. Level 1 focuses on conformance to the ConcePTION CDM specifications. Level 2 evaluates the temporal plausibility of events and uniqueness of records. Level 3 provides an overview of distributions, outliers, and trends over time to facilitate fit-for-purpose evaluation. Therefore, level 1 and 2 assure a proper data standardization, while level 3 provides information regarding the study population, and potential sub-populations. The DQAs are run locally and assessed centrally by a data quality revisor together with the data access provider's *** quality is the sum of several internal and external features of the data. While DQAs can provide reassurance about fitness-for-purpose for secondary-use data sources, improvements in data collection are essential to reduce errors and enhance overall data quality for Real World *** aims to support clinical and regulatory decision-making for medicines and vaccines by evaluating the quality of observational health data sources to support fit for purpose assessment. Assessing and improving data quality will enhance the reliability and quality of the generated *** RegistrationThis research was registered in EU PAS registration with number EU50142.

关键词： common data model data quality drug safety multi-database studies pregnancy quality checks real-world data

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Healthcare burden changes by restricted physical activities in lumbar spinal stenosis and spondylolisthesis: a retrospective large cohort study during the COVID-19 pandemic

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BMC MUSCULOSKELETAL DISORDERS 2024年第1期25卷 411页

作者： Kim, Jun-Hoe Chegal, Yebin Kim, Suhyun Park, Hangeul Kim, Young Rak Kim, Sum Kim, Kwangsoo Lee, Chang-Hyun Kim, Chi Heon Chung, Chun Kee Seoul Natl Univ Hosp Dept Neurosurg Seoul South Korea Korea Univ Dept Stat Seoul South Korea Seoul Natl Univ Hosp Transdisciplinary Dept Med & Adv Technol Seoul South Korea Seoul Natl Univ Coll Med Dept Neurosurg 101 Daehak Ro Seoul 03080 South Korea Seoul Natl Univ Dept Brain & Cognit Sci Seoul 03080 South Korea

Background Lumbar spinal stenosis (LSS) and spondylolisthesis (SPL) are characterized as degenerative spinal pathologies and share considerable similarities. However, opinions vary on whether to recommend exercise or restrict it for these diseases. Few studies have objectively compared the effects of daily physical activity on LSS and SPL because it is impossible to restrict activities ethnically and practically. We investigated the effect of restricting physical activity due to social distancing (SoD) on LSS and SPL, focusing on the aspect of healthcare burden changes during the pandemic period. Methods We included first-visit patients diagnosed exclusively with LSS and SPL in 2017 and followed them up for two years before and after the implementation of the SoD policy. As controls, patients who first visited in 2015 and were followed for four years without SoD were analyzed. The common data model was employed to analyze each patient's diagnostic codes and treatments. Hospital visits and medical costs were analyzed by regression discontinuity in time to control for temporal effects on dependent variables. Results Among 33,484 patients, 2,615 with LSS and 446 with SPL were included. A significant decrease in hospital visits was observed in the LSS (difference, -3.94 times/month100 patients;p = 0.023) and SPL (difference, -3.44 times/month100 patients;p = 0.026) groups after SoD. This decrease was not observed in the data from the control group. Concerning medical costs, the LSS group showed a statistically significant reduction in median copayment (difference, -$45/monthpatient;p < 0.001) after SoD, whereas a significant change was not observed in the SPL group (difference, -$19/monthpatient;p = 0.160). Conclusion Restricted physical activity during the SoD period decreased the healthcare burden for patients with LSS or, conversely, it did not significantly affect patients with SPL. Under circumstances of physical inac

关键词： Lumbar spinal stenosis Spondylolisthesis COVID-19 common data model

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Time Series AI model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study

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JMIR MEDICAL INFORMATICS 2024年 12卷 e47693页

作者： Heo, Suncheol Kang, Eun-Ae Yu, Jae Yong Kim, Hae Reong Lee, Suehyun Kim, Kwangsoo Hwangbo, Yul Park, Rae Woong Shin, Hyunah Ryu, Kyeongmin Kim, Chungsoo Jung, Hyojung Chegal, Yebin Lee, Jae-Hyun Park, Yu Rang Yonsei Univ Coll Med Dept Biomed Syst Informat 50-1 Yonsei Ro Seoul 03722 South Korea Yonsei Univ Dept Res Affairs Med Informat Collaborat Unit Coll Med Seoul South Korea Gachon Univ Dept Comp Engn Seongnam South Korea Seoul Natl Univ Hosp Transdisciplinary Dept Med & Adv Technol Seoul South Korea Natl Canc Ctr Healthcare AI Team Goyang South Korea Ajou Univ Sch Med Dept Biomed Informat Suwon South Korea Konyang Univ Hosp Healthcare Data Sci Ctr Daejeon South Korea Ajou Univ Grad Sch Med Dept Biomed Sci Suwon South Korea Korea Univ Dept Stat Seoul South Korea Yonsei Univ Coll Med Dept Internal Med Div Allergy & Immunol Seoul 120752 South Korea Yonsei Univ Inst Allergy Coll Med Seoul South Korea

Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN. Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMVLSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin

关键词： adverse drug reaction real world data multicenter study distributed research network common data model time series AI time series artificial intelligence machine learning adverse reaction adverse reactions detect detection toxic toxicity renal kidney nephrology pharmaceutical pharmacology pharmacy pharmaceutics

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Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population

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PEDIATRIC PULMONOLOGY 2023年第2期58卷 433-440页

作者： Kanbar, Lara J. Dexheimer, Judith W. Zahner, Janet Burrows, Evanette K. Chatburn, Robert Messinger, Amanda Baker, Christopher D. Schuler, Christine L. Benscoter, Dan Amin, Raouf Pajor, Nathan Cincinnati Childrens Hosp Med Ctr Div Pulm Med 3333 Burnet Ave Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Div Biomed Informat Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Div Emergency Med Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Dept Informat Serv Cincinnati OH 45229 USA Childrens Hosp Philadelphia Dept Biomed & Hlth Informat Philadelphia PA 19104 USA Cleveland Clin Program Manager Enterprise Res Resp Care Cleveland OH 44106 USA Univ Colorado Dept Pediat Sect Pulm & Sleep Med Denver CO 80202 USA Univ Cincinnati Coll Med Div Hosp Med Cincinnati OH USA Univ Cincinnati Coll Med Dept Pediat Cincinnati OH USA

Background Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing. Objective We aimed to describe current ventilator data in the electronic health record (EHR) and propose a framework for standardizing these data using a common data model (CDM) across multiple populations and sites. Methods We focused on a cohort of patients with LTMV dependence who were weaned from mechanical ventilation (MV). We extracted and described relevant EHR ventilation data. We identified the minimum necessary components, termed "Clinical Ideas," to describe MV from time of initiation to liberation. We then utilized existing resources and partnered with informatics collaborators to develop a framework for incorporating Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP). Results We identified 78 children with LTMV dependence who weaned from ventilator support. There were 25 unique device names and 28 unique ventilation mode names used in the cohort. We identified multiple Clinical Ideas necessary to describe ventilator support over time: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. We used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration. Conclusion The proposed framework standardizes mechanical ventilation terminology and may facilitate efficient data exchange in a multisite network. Rapid data sharing is necessary to improve research and clinical care for children with LTMV dependence.

关键词： common data model data sharing electronic health record informatics mechanical ventilation pediatrics

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