Research and Resources Hub

Explore our dedicated repository for groundbreaking studies and resources that inform and bolster the development of EpyDiagnosis. Here, we provide streamlined access to a curated selection of research papers, clinical trial findings, and ongoing investigations that are pivotal to our product’s innovation. This hub serves as a testament to our commitment to evidence-based advancements and transparent communication with the medical community and stakeholders.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Representing and utilizing clinical textual data for real world studies: An OHDSI approach Keloth et al., 2023Abstract Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical…

Continuar leyendoRepresenting and utilizing clinical textual data for real world studies: An OHDSI approach

Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer

Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer Ryu et al., 2020Abstract Background: Common data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research.…

Continuar leyendoTransformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review Ahmadi et al., 2022Abstract The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a…

Continuar leyendoOMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

Validation of a Deep Learning–Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data

Validation of a Deep Learning–Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data Raghu et al., 2022Abstract Importance: Lung cancer screening with chest computed tomography (CT) prevents lung cancer death; however, fewer than 5% of…

Continuar leyendoValidation of a Deep Learning–Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data