A high-throughput phenotyping algorithm is portable from adult to pediatric populations. (17th February 2021)

• Record Type: Journal Article
• Authors: Geva, Alon; Liu, Molei; Panickan, Vidul A; Avillach, Paul; Cai, Tianxi; Mandl, Kenneth D
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 6(2021)
• Page Start: 1265
• Page End: 1269
• Publication Date: 2021-02-17
• Subjects: phenotype -- electronic health records -- data mining -- biobank, high-throughput; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa343
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Multimodal automated phenotyping (MAP) is a scalable, high-throughput phenotyping method, developed using electronic health record (EHR) data from an adult population. We tested transportability of MAP to a pediatric population. Materials and Methods: Without additional feature engineering or supervised training, we applied MAP to a pediatric population enrolled in a biobank and evaluated performance against physician-reviewed medical records. We also compared performance of MAP at the pediatric institution and the original adult institution where MAP was developed, including for 6 phenotypes validated at both institutions against physician-reviewed medical records. Results: MAP performed equally well in the pediatric setting (average AUC 0.98) as it did at the general adult hospital system (average AUC 0.96). MAP's performance in the pediatric sample was similar across the 6 specific phenotypes also validated against gold-standard labels in the adult biobank. Conclusions: MAP is highly transportable across diverse populations and has potential for wide-scale use.