The National Center for Advancing Translational Sciences (NCATS) has systematically compiled clinical, laboratory and diagnostic data from electronic health records to support COVID-19 research efforts via the National COVID Cohort Collaborative (N3C) Data Enclave. As of August 2, 2022, the repository contains information from over 15 million patients (including 5.8 million COVID-19 positive patients) across the United States.

The N3C Data Enclave is organized into 3 levels of data with varying access restrictions:

• Synthetic dataset: Contains no protected health information (PHI). This is a statistically-comparable artificial dataset derived from the original dataset.
  • Can be requested by: Researchers from US-based or foreign institutions, and citizen scientists
• De-identified dataset: Contains no PHI. This dataset consists of real patient data with shifted dates of service and truncated ZIP codes of patients residing in areas with populations above 20,000.
  • Can be requested by: Researchers from US-based or foreign institutions
• Limited Data Set (LDS): Contains 2 PHI elements (dates of service and patient ZIP code). This dataset consists of real patient data.
  • Can be requested by: Researchers from US-based institutions only

The N3C Data Enclave is a secure platform through which harmonized clinical data provided by our contributing members are stored. The Enclave includes demographic and clinical characteristics of patients who have been tested for or diagnosed with COVID-19, and further information about the strategies and outcomes of treatments for those suspected or confirmed to have the virus. Additional data from individuals infected with pathogens such as SARS 1, MERS, and H1N1 are also included to support comparative studies. Data can be accessed only within the N3C Data Enclave and cannot be downloaded or removed. Three tiers of access are available for users depending on the scope and nature of their research; however, all will require verification and approval by the Data Access Committee (DAC) before data can be accessed.

Portal for centralized national data to study COVID-19 and identify potential treatments.Centralized, secure analytics platform where patient privacy is protected. Enables collection and analysis of clinical, laboratory and diagnostic data from hospitals and health care plans. Data are provided after executing data transfer agreement with National Center for Advancing Translational Sciences. N3C is partnership among NCATS supported Clinical and Translational Science Awards Program hubs and National Center for Data to Health with overall stewardship by NCATS.

OMOP2OBO Mappings - N3C OMOP to OBO Working group

This repository stores OMOP2OBO mappings which have been processed for use within the National COVID Cohort Collaborative (N3C) Enclave. The version of the mappings stored in this repository have been specifically formatted for use within the N3C Enclave.

N3C OMOP to OBO Working Group: https://covid.cd2h.org/ontology

Accessing the N3C-Formatted Mappings

You can access the three OMOP2OBO HPO mapping files in the Enclave from the Knowledge store using the following link: https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567.

The mapping set includes three files, but you only need to merge the following two files with existing data in the Enclave in order to be able to create the concept sets:

• OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv
• OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_version.csv

The first file OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv, contains columns for the OMOP concept ids and codes as well as specifies information like whether or not the OMOP concept’s descendants should be included when deriving the concept sets (defaults to FALSE). The other file OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_version.csv, contains details on the mapping’s label (i.e., the HPO curie and label in the concept_set_id field) and its provenance/evidence (the specific column to access for this information is called intention).

Creating Concept Sets

Merge these files together on the column named codeset_id and then join them with existing Enclave tables like concept and condition_occurrence to populate the actual concept sets. The name of the concept set can be obtained from the OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_version.csv file and is stored as a string in the column called concept_set_id. Although not ideal (but is the best way to approach this currently given what fields are available in the Enclave), to get the HPO CURIE and label will require applying a regex to this column.

An example mapping is shown below (highlighting some of the most useful columns):

codeset_id: 900000000
concept_set_id: [OMOP2OBO] hp_0002031-abnormal_esophagus_morphology
concept: 23868
code: 69771008
codeSystem: SNOMED
includeDescendants: False
intention:

Mixed - This mapping was created using the OMOP2OBO mapping algorithm (https://github.com/callahantiff/OMOP2OBO).

The Mapping Category and Evidence supporting the mappings are provided below, by OMOP concept:

23868
*******

Mapping Category: Automatic Exact - Concept
------------------------------------------------

Mapping Provenance
------------------
OBO_DbXref-OMOP_ANCESTOR_SOURCE_CODE:snomed_69771008 | OBO_DbXref-OMOP_CONCEPT_SOURCE_CODE:snomed_69771008 | CONCEPT_SIMILARITY:HP_0002031_0.713

Release Notes - v1.0.0

Preparation

In order to import data into the Enclave, the following items are needed:

• Obtain API Token, which will be included in the authorization header (stored as GitHub Secret)
• Obtain username hash from the Enclave
• OMOP2OBO Mappings (v1.0.0)

Data

• Concept Set Container (concept_set_container): CreateNewConceptSet
• Concept Set Version (code_sets): CreateNewDraftOMOPConceptSetVersion
• Concept Set Expression Items (concept_set_version_item): addCodeAsVersionExpression

Script

• n3c_mapping_conversion.py: https://github.com/callahantiff/OMOP2OBO/tree/master/applications/N3C

Generated Output

Need to have the codeset_id filled from self-generation (ideally, from a conserved range) prior to beginning any of the API steps. The current list of assigned identifiers is stored in the file named omop2obo_enclave_codeset_id_dict_v1.0.0.json.

To be consistent with OMOP tools, specifically Atlas, we have also created Atlas-formatted json files for each mapping, which are stored in the zipped directory named atlas_json_files_v1.0.0.zip.

File 1: concept_set_container

• Generated Data: OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_container.csv
• Columns:
  • concept_set_id
  • concept_set_name
  • intention
  • assigned_informatician
  • assigned_sme
  • project_id
  • status
  • stage
  • n3c_reviewer
  • alias
  • archived
  • created_by
  • created_at

File 2: concept_set_expression_items

• Generated Data: OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv
• Columns:
  • codeset_id
  • concept_id
  • code
  • codeSystem
  • isExcluded
  • includeDescendants
  • includeMapped
  • item_id
  • annotation
  • created_by
  • created_at

File 3: concept_set_version

• Generated Data: OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_version.csv
• Columns:
  • codeset_id
  • concept_set_id
  • concept_set_version_title
  • project
  • source_application
  • source_application_version
  • created_at
  • atlas_json
  • most_recent_version
  • comments
  • intention
  • limitations
  • issues
  • update_message
  • status
  • has_review
  • reviewed_by
  • created_by
  • provenance
  • atlas_json_resource_url
  • parent_version_id
  • is_draft

Generated Output:

• OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_container.csv
• OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv
• OMOP2OBO_v1.0.0_N3C_Enclave_CSV_concept_set_version.csv
• atlas_json_files_v1.0.0.zip
• omop2obo_enclave_codeset_id_dict_v1.0.0.json

Please see the most recent version at 10.5281/zenodo.3992394 The N3C is more than simply a data enclave; it is also a collaborative research community committed to the rapid generation and dissemination of scientific knowledge for the public good, and to the advancement of COVID-19 science. Purpose: This document provides guidelines and approaches that all users within the N3C research community uphold, and it addresses attribution and publication principles with regard to N3C Community dissemination of research. Here, we define publication and attribution principles that apply to N3C analysis reports, data, resources, abstracts, presentations, preprints, and publications arising from the use of content in N3C Enclave. This document will be reviewed and updated regularly as N3C evolves, but at least annually by the N3C Governance Workstream and modified or recertified as deemed necessary. Overview: These guidelines are intended to enable the following: A transparent and collaborative environment within the N3C where all contributions are acknowledged; Promote provenance and reproducibility of COVID-19 research; Sharing research results with N3C users as soon as they are mature enough; Support opportunities to publish in high-impact journals; and Clarify attribution expectations for all N3C artifacts, specifically publications. Attribution Guidelines: The N3C community is dedicated to the goal of collaboration and rapid knowledge generation and dissemination to help combat COVID-19. This version is deprecated; please see most recent at 10.5281/zenodo.3992394

Representative examples of false negatives for positive mentions of “fever” in N3C COVID corpus, “diarrhea” in UMN PASC corpus and “chest pain” in N3C corpus as returned by BioMedICUS and both LLMs along with explanations.

Objectives: Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data.

Results: The data set used in this analysis consists of 2,880,456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period.

Discussion: An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on progression of COVID-19 disease severity over time.

Conclusion: The OS provides a framework which can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.

The main entity of this document is a taxonomy with accession number 1776758

Purpose: This document provides community-driven guidelines and approaches that all users within the N3C research community uphold, and it addresses attribution and publication principles regarding N3C Community dissemination of research. These publication and attribution principles apply to N3C analysis reports, data, resources, abstracts, presentations, preprints, manuscripts, and other publications arising from the use of content in N3C Data Enclave. This document will be reviewed and updated regularly as N3C evolves, but at least annually by the N3C Governance Workstream and modified or recertified as deemed necessary. Note that prior versions of this policy were under doi: 10.5281/zenodo.3992394; those older versions have been deprecated.

Note: very small numbers are rounded to 0 in the HuggingFace dataset viewer. These data represent medical concept probabilities for 300 topics generated via Latent Direchlet Allocation applied to 387M electronic health record conditions for 7.9M patients as described in Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs . Data were quality filtered and cleaned prior to modeling, including removal of COVID-19 and MIS-C as confounders… See the full description on the dataset page: https://huggingface.co/datasets/oneilsh/lda_pasc.

BackgroundPatient symptoms, crucial for disease progression and diagnosis, are often captured in unstructured clinical notes. Large language models (LLMs) offer potential advantages in extracting patient symptoms compared to traditional rule-based information extraction (IE) systems.MethodsThis study compared fine-tuned LLMs (LLaMA2-13B and LLaMA3-8B) against BioMedICUS, a rule-based IE system, for extracting symptoms related to acute and post-acute sequelae of SARS-CoV-2 from clinical notes. The study utilized three corpora: UMN-COVID, UMN-PASC, and N3C-COVID. Prevalence, keyword and fairness analyses were conducted to assess symptom distribution and model equity across demographics.ResultsBioMedICUS outperformed fine-tuned LLMs in most cases. On the UMN PASC dataset, BioMedICUS achieved a macro-averaged F1-score of 0.70 for positive mention detection, compared to 0.66 for LLaMA2-13B and 0.62 for LLaMA3-8B. For the N3C COVID dataset, BioMedICUS scored 0.75, while LLaMA2-13B and LLaMA3-8B scored 0.53 and 0.68, respectively for positive mention detection. However, LLMs performed better in specific instances, such as detecting positive mentions of change in sleep in the UMN PASC dataset, where LLaMA2-13B (0.79) and LLaMA3-8B (0.65) outperformed BioMedICUS (0.60). For fairness analysis, BioMedICUS generally showed stronger performance across patient demographics. Keyword analysis using ANOVA on symptom distributions across all three corpora showed that both corpus (df = 2, p 

This page, "anti-N3C(O)N", is part of the NIST Chemistry WebBook. This site and its contents are part of the NIST Standard Reference Data Program.

The main entity of this document is a proteome with accession number UP000184869

Macro-averaged metrics with 95% confidence intervals for evaluation of BioMedICUS’, LLaMA2-13B, and LLaMA3-8B extraction performance in positive and negative symptom mentions for N3C COVID.

Table of Contents Background 2 N3C Privacy Preserving Record Linkage Principles 2 PPRL Participation Options 4 Deduplication 4 Linking Multiple Datasets 5 Cohort Discovery for Research Studies 7 Technical and Data Governance Architecture for N3C PPRL Linked Data Infrastructure 9 Appendix A Glossary 12 Appendix B Linkage Honest Broker Agreement 13 Appendix C Regulatory Considerations for Privacy-Preserving Record Linkage 27

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SARS-CoV-2 infection has been associated with increased autoimmune disease risk. Past studies have not aligned regarding the most prevalent autoimmune diseases after infection, however. Furthermore, the relationship between infection severity and new autoimmune disease risk has not been well examined. We used RECOVER’s electronic health record (EHR) networks, N3C, PCORnet, and PEDSnet, to estimate types and frequency of autoimmune diseases arising after SARS-CoV-2 infection and assessed how infection severity related to autoimmune disease risk. We identified patients of any age with SARS-CoV-2 infection between April 1, 2020 and April 1, 2021, and assigned them to a World Health Organization COVID-19 severity category for adults or the PEDSnet acute COVID-19 illness severity classification system for children (30 days after SARS-CoV-2 infection index date and occurring ≥1 day apart. We calculated overall and infection severity-stratified incidence ratesper 1000 person-years for all autoimmune diseases. With least severe COVID-19 severity as reference, survival analyses examined incident autoimmune disease risk. The most common new-onset autoimmune diseases in all networks were thyroid disease, psoriasis/psoriatic arthritis, and inflammatory bowel disease. Among adults, inflammatory arthritis was the most common, and Sjögren’s disease also had high incidence. Incident type 1 diabetes and hematological autoimmune diseases were specifically found in children. Across networks, after adjustment, patients with highest COVID-19 severity had highest risk for new autoimmune disease vs. those with least severe disease (N3C: adjusted Hazard Ratio, (aHR) 1.47 (95%CI 1.33–1.66); PCORnet aHR 1.14 (95%CI 1.02–1.26); PEDSnet: aHR 3.14 (95%CI 2.42–4.07)]. Overall, severe acute COVID-19 was most strongly associated with autoimmune disease risk in three EHR networks.

The pseudocode for the algorithm: Estimating the gestational age.

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