Abstract 14205: WAVES - The Lucile Packard Children's Hospital Open-Access Pediatric Physiological Waveforms Dataset

Abstract

Introduction: Research repositories of adult physiological waveform (PW) data facilitate clinical and methodological advances. Models trained on adult data do not generalize well to pediatric groups. No large, open repository of pediatric PW data is available for research. Methods: PW and vital sign data were downloaded from bedside patient monitoring systems at an academic pediatric hospital from 06/2008-01/2017, de-identified, cleaned, organized, and stored on a research platform managed by Stanford University. Algorithms to process data for deep learning applications were designed, validated, and uploaded to the research platform. Results: WAVES is a single-institution dataset comprising 9 years of high-frequency PW data. WAVES consists of 10.6 million hours of 1 to 20 concurrent types of high-frequency PWs (Table 1). Approximately 1.5 million PW samples were collected over 50,364 unique hospital encounters across various specialized and general units (Table 2). Initial work demonstrated the suitability of the data for training deep learning models by accurately detecting hypotension with data only from electrocardiogram, plethysmography, and respiration waveforms. Conclusions: WAVES is currently the largest pediatric-focused PW dataset available for open-access research and the second largest repository of correlated multi-channel PW data. The WAVES database can enable improvements in pediatric clinical care through machine learning research on PWs from a variety of hospitalized pediatric patients and could facilitate the development of methodological and clinical innovation in the field of pediatric care.