Open AccessHigh-throughput phenotyping with temporal sequences
Author(s) -
Hossein Estiri,
Zachary H. Strasser,
Shawn N. Murphy
Publication year - 2020
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/ocaa288
Subject(s) - interpretability , computer science , throughput , data mining , transitive relation , representation (politics) , machine learning , artificial intelligence , identification (biology) , raw data , biology , telecommunications , mathematics , combinatorics , politics , political science , law , wireless , botany , programming language
High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.