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  1. 2301
    “…External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. …”
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    Online Artículo Texto
    Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study
  2. 2302
    Publicado 2017
    “…This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. …”
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    Online Artículo Texto
    Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data
  3. 2303
    “…RESULTS: Fifteen critical RGs are summarised below: RG1: Lack of realistic models that recapitulate tumour/tumour micro/macroenvironment; RG2: Insufficient evidence on precise contributions of genetic/environmental/lifestyle factors to CRC risk; RG3: Pressing need for prevention trials; RG4: Lack of integration of different prevention approaches; RG5: Lack of optimal strategies for CRC screening; RG6: Lack of effective triage systems for invasive investigations; RG7: Imprecise pathological assessment of CRC; RG8: Lack of qualified personnel in genomics, data sciences and digital pathology; RG9: Inadequate assessment/communication of risk, benefit and uncertainty of treatment choices; RG10: Need for novel technologies/interventions to improve curative outcomes; RG11: Lack of approaches that recognise molecular interplay between metastasising tumours and their microenvironment; RG12: Lack of reliable biomarkers to guide stage IV treatment; RG13: Need to increase understanding of health related quality of life (HRQOL) and promote residual symptom resolution; RG14: Lack of coordination of CRC research/funding; RG15: Lack of effective communication between relevant stakeholders. …”
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    Critical research gaps and recommendations to inform research prioritisation for more effective prevention and improved outcomes in colorectal cancer
  4. 2304
    “…MATERIALS AND METHODS: Nine International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9-CM) concept sets were extracted from eMERGE network phenotypes, translated to Systematized Nomenclature of Medicine - Clinical Terms concept sets, and applied to patient data that were mapped from source ICD9-CM and ICD10-CM codes to Systematized Nomenclature of Medicine - Clinical Terms codes using Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) vocabulary mappings. …”
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    Online Artículo Texto
    Effect of vocabulary mapping for conditions on phenotype cohorts
  5. 2305
    “…METHODS: We used the Observational Health Data Sciences and Informatics network, an international collaborative implementing the Observational Medical Outcomes Partnership Common Data Model to standardize more than 2 billion patient records. …”
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    Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network
  6. 2306
    “…Recent revolutionary advances at the intersection of medicine, omics, data sciences, computing, epidemiology, and related technologies inspire us to ponder their impact on health. …”
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    Advancing human health in the decade ahead: pregnancy as a key window for discovery: A Burroughs Wellcome Fund Pregnancy Think Tank
  7. 2307
    “…Methods  Thyroid cancer diagnosis and stage-related modifiers were extracted with rule-based NLP from 63,795 thyroid cancer pathology reports and 56,239 Iodine whole-body scan reports from three medical institutions in the Observational Health Data Sciences and Informatics data network. The data were converted into the OMOP CDM v6.0 according to the OMOP CDM oncology extension module. …”
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    Online Artículo Texto
    Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model
  8. 2308
    “…DATA SOURCES: The American Academy of Ophthalmology Basic and Clinical Science Course (BCSC), SNOMED-CT, and ICD-10-CM terminologies from the Observational Health Data Sciences and Informatics Athena browser, and the ICD-11 terminology browser. …”
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    Online Artículo Texto
    Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11
  9. 2309
    “…Male BALB/cJ mice were surgically implanted with transmitters (DataSciences ETA10-F20) for recording EEG, activity and core body temperature by telemetry and a cannula for intracerebroventricular (ICV) microinjections. …”
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    Effects of corticotropin releasing factor (CRF) on sleep and temperature following predictable controllable and uncontrollable stress in mice
  10. 2310
    “…The Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM) organizes healthcare data into standard data structures using concepts that are explicitly and formally specified through standard vocabularies, thereby facilitating large-scale analysis. …”
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    Preliminary exploration of survival analysis using the OHDSI common data model: a case study of intrahepatic cholangiocarcinoma
  11. 2311
    “…To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. …”
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    Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study
  12. 2312
    por Kostka, Kristin, Duarte-Salles, Talita, Prats-Uribe, Albert, Sena, Anthony G, Pistillo, Andrea, Khalid, Sara, Lai, Lana Y H, Golozar, Asieh, Alshammari, Thamir M, Dawoud, Dalia M, Nyberg, Fredrik, Wilcox, Adam B, Andryc, Alan, Williams, Andrew, Ostropolets, Anna, Areia, Carlos, Jung, Chi Young, Harle, Christopher A, Reich, Christian G, Blacketer, Clair, Morales, Daniel R, Dorr, David A, Burn, Edward, Roel, Elena, Tan, Eng Hooi, Minty, Evan, DeFalco, Frank, de Maeztu, Gabriel, Lipori, Gigi, Alghoul, Hiba, Zhu, Hong, Thomas, Jason A, Bian, Jiang, Park, Jimyung, Martínez Roldán, Jordi, Posada, Jose D, Banda, Juan M, Horcajada, Juan P, Kohler, Julianna, Shah, Karishma, Natarajan, Karthik, Lynch, Kristine E, Liu, Li, Schilling, Lisa M, Recalde, Martina, Spotnitz, Matthew, Gong, Mengchun, Matheny, Michael E, Valveny, Neus, Weiskopf, Nicole G, Shah, Nigam, Alser, Osaid, Casajust, Paula, Park, Rae Woong, Schuff, Robert, Seager, Sarah, DuVall, Scott L, You, Seng Chan, Song, Seokyoung, Fernández-Bertolín, Sergio, Fortin, Stephen, Magoc, Tanja, Falconer, Thomas, Subbian, Vignesh, Huser, Vojtech, Ahmed, Waheed-Ul-Rahman, Carter, William, Guan, Yin, Galvan, Yankuic, He, Xing, Rijnbeek, Peter R, Hripcsak, George, Ryan, Patrick B, Suchard, Marc A, Prieto-Alhambra, Daniel
    Publicado 2022
    “…Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. …”
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    Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS
  13. 2313
    “…OBJECTIVE: The adoption of electronic health records (EHRs) has produced enormous amounts of data, creating research opportunities in clinical data sciences. Several concept recognition systems have been developed to facilitate clinical information extraction from these data. …”
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    Online Artículo Texto
    Clinical concept recognition: Evaluation of existing systems on EHRs
  14. 2314
    “…OBJECTIVE: This study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the Observational Health Data Sciences and Informatics for internal and external validation. …”
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    Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study
  15. 2315
    “…Our data were transformed to a common data model and are part of the Observational Health Data Sciences and Informatics network. The cohort patients and outcomes were identified by a combination of procedure codes, condition codes, and medication exposures in billing and claims data. …”
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    Relative Risk of Cervical Neoplasms Among Copper and Levonorgestrel-Releasing Intrauterine System Users
  16. 2316
    “…BACKGROUND: The Observational Health Data Sciences and Informatics (OHDSI) network is an international collaboration established to apply open-source data analytics to a large network of health databases, including the Korean common data model (K-CDM) network. …”
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    Effect of Age on the Initiation of Biologic Agent Therapy in Patients With Inflammatory Bowel Disease: Korean Common Data Model Cohort Study
  17. 2317
    “…In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. …”
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    A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data
  18. 2318
    “…LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. …”
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    Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies
  19. 2319
    “…Class membership is a critical issue in health data sciences. Different types of statistical models have been widely applied to identify participants within a population with heterogeneous longitudinal trajectories. …”
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    Online Artículo Texto
    Maternal weight latent trajectories and associations with adverse pregnancy outcomes using a smoothing mixture model
  20. 2320
    “…METHODS: We conducted a retrospective, observational cohort study using electronic data from the enterprise data warehouse of the University of Colorado Anschutz Medical Campus and its affiliates, with data in the format of the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) common data model. …”
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    Prescribing Patterns of Oral Antibiotics and Isotretinoin for Acne in a Colorado Hospital System: Retrospective Cohort Study
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