[HTML][HTML] Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database
Modern predictive models require large amounts of data for training and evaluation,
absence of which may result in models that are specific to certain locations, populations in …
absence of which may result in models that are specific to certain locations, populations in …
[HTML][HTML] The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: A review
Abstract Coronavirus disease 2019 (COVID-19) is an infectious disease with acute intense
respiratory syndrome which spread around the world for the very first time impacting the way …
respiratory syndrome which spread around the world for the very first time impacting the way …
[HTML][HTML] Development and validation of the radiology common data model (R-CDM) for the international standardization of medical imaging data
Purpose Digital Imaging and Communications in Medicine (DICOM), a standard file format
for medical imaging data, contains metadata describing each file. However, metadata are …
for medical imaging data, contains metadata describing each file. However, metadata are …
[HTML][HTML] A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data
Background and objective As a response to the ongoing COVID-19 pandemic, several
prediction models in the existing literature were rapidly developed, with the aim of providing …
prediction models in the existing literature were rapidly developed, with the aim of providing …
[HTML][HTML] The evolution of clinical knowledge during COVID-19: towards a global learning health system
K Verspoor - Yearbook of medical informatics, 2021 - thieme-connect.com
Objectives: We examine the knowledge ecosystem of COVID-19, focusing on clinical
knowledge and the role of health informatics as enabling technology. We argue for …
knowledge and the role of health informatics as enabling technology. We argue for …
[HTML][HTML] Phenotyping in distributed data networks: selecting the right codes for the right patients
Observational data can be used to conduct drug surveillance and effectiveness studies,
investigate treatment pathways, and predict patient outcomes. Such studies require …
investigate treatment pathways, and predict patient outcomes. Such studies require …
Integrating real-world data from Brazil and Pakistan into the OMOP common data model and standardized health analytics framework to characterize COVID-19 in the …
EPP Junior, P Normando, R Flores-Ortiz… - Journal of the …, 2023 - academic.oup.com
Objectives The aim of this work is to demonstrate the use of a standardized health
informatics framework to generate reliable and reproducible real-world evidence from Latin …
informatics framework to generate reliable and reproducible real-world evidence from Latin …
[PDF][PDF] PHOEBE 2.0: selecting the right concept sets for the right patients using lexical, semantic, and data-driven recommendations
Background Reliable evidence generated from observational data can impact decision-
making and improve patient outcomes but requires accurate phenotype algorithms (1). Our …
making and improve patient outcomes but requires accurate phenotype algorithms (1). Our …