A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation

• Published in: JMIR medical informatics Vol. 12; p. e49997
• Main Authors: Wen, Andrew, Wang, Liwei, He, Huan, Fu, Sunyang, Liu, Sijia, Hanauer, David A, Harris, Daniel R, Kavuluru, Ramakanth, Zhang, Rui, Natarajan, Karthik, Pavinkurve, Nishanth P, Hajagos, Janos, Rajupet, Sritha, Lingam, Veena, Saltz, Mary, Elowsky, Corey, Moffitt, Richard A, Koraishy, Farrukh M, Palchuk, Matvey B, Donovan, Jordan, Lingrey, Lora, Stone-DerHagopian, Garo, Miller, Robert T, Williams, Andrew E, Leese, Peter J, Kovach, Paul I, Pfaff, Emily R, Zemmel, Mikhail, Pates, Robert D, Guthe, Nick, Haendel, Melissa A, Chute, Christopher G, Liu, Hongfang
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 09.09.2024
• Subjects: Algorithms; Annotations; Collaboration; COVID-19; Dictionaries; Electronic health records; Infections; Language; Machine learning; Narratives; Natural language processing; Neural networks; Ontology; Original Paper; Semantics; Software engineering; Subject specialists; phenotype; OHNLP; narratives; PASC; natural language processing; extraction; phenotyping; COVID; COVID-19; SARS-CoV-2; NLP; extract; unstructured; clinical information extraction; clinical phenotyping; Open Health Natural Language Processing

A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the d...