A likelihood-based convolution approach to estimate major health events in longitudinal health records data: an external validation study | Zendy

Lisiane Pruinelli | Zendy; Jiaqi Zhou | Zendy; Bethany Stai | Zendy; Jesse D. Schold | Zendy; Timothy L. Pruett | Zendy; Sisi Ma | Zendy; György Simon | Zendy

Open Access

A 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.

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