Data integration of structured and unstructured sources for assigning clinical codes to patient stays | Zendy

Elyne Scheurwegs | Zendy; Kim Luyckx | Zendy; Léon Luyten | Zendy; Walter Daelemans | Zendy; Tim Van den Bulcke | Zendy

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Data integration of structured and unstructured sources for assigning clinical codes to patient stays

Author(s) -

Elyne Scheurwegs,

Kim Luyckx,

Léon Luyten,

Walter Daelemans,

Tim Van den Bulcke

Publication year - 2015

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/ocv115

Subject(s) - computer science , diagnosis code , data integration , unstructured data , data mining , big data , data science , predictive power , set (abstract data type) , information retrieval , machine learning , medicine , population , environmental health , programming language , philosophy , epistemology

Enormous amounts of healthcare data are becoming increasingly accessible through the large-scale adoption of electronic health records. In this work, structured and unstructured (textual) data are combined to assign clinical diagnostic and procedural codes (specifically ICD-9-CM) to patient stays. We investigate whether integrating these heterogeneous data types improves prediction strength compared to using the data types in isolation.

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