Open AccessA likelihood-based convolution approach to estimate major health events in longitudinal health records data: an external validation study
Author(s) -
Lisiane Pruinelli,
Jiaqi Zhou,
Bethany Stai,
Jesse D. Schold,
Timothy L. Pruett,
Sisi Ma,
György Simon
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab087
Subject(s) - computer science , missing data , electronic health record , event (particle physics) , data mining , statistics , convolution (computer science) , data collection , health care , artificial intelligence , machine learning , mathematics , physics , quantum mechanics , artificial neural network , economics , economic growth
In electronic health record data, the exact time stamp of major health events, defined by significant physiologic or treatment changes, is often missing. We developed and externally validated a method that can accurately estimate these time stamps based on accurate time stamps of related data elements.