Richard L Zhu

TL;DR: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics.

TL;DR: The National COVID Cohort Collaborative (N3C) as discussed by the authors is the largest, most representative U.S. cohort of COVID-19 cases and controls to date.

TL;DR: In this article, the authors developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using four federated Common Data Models, and discovered 15 heuristics relating to source CDM conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness and fitness for use.

TL;DR: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections as unique properties of centralized data are harnessed to enable unit inference.

TL;DR: In this article , a method for predicting electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning is presented, called rapid approximate subset-based spectra prediction (RASSP).