Alon Geva1,2,3, Molei Liu4, Vidul A Panickan5, Paul Avillach1,5,6, Tianxi Cai4,5, Kenneth D Mandl1,5,6. 1. Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA. 2. Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA. 3. Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA. 4. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA. 5. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA. 6. Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
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.
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.
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