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Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Schulz, S; Seddig, T; Hanser, S; Zaiß, A; Daumke, P.
Checking Coding Completeness by Mining Discharge Summaries.
Stud Health Technol Inform. 2011; 169: 594-598.


Autor/innen der Med Uni Graz:
Schulz Stefan

Dimensions Citations:

Plum Analytics:
Incomplete coding is a known problem in hospital information systems. In order to detect non-coded secondary diseases we developed a text classification system which scans discharge summaries for drug names. Using a drug knowledge base in which drug names are linked to sets of ICD-10 codes, the system selects those documents in which a drug name occurs that is not justified by any ICD-10 code within the corresponding record in the patient database. Treatment episodes with missing codes for diabetes mellitus, Parkinson's disease, and asthma/COPD were subject to investigation in a large German university hospital. The precision of the method was 79%, 14%, and 45% respectively, roughly estimated recall values amounted to 43%, 70%, and 36%. Based on these data we predict roughly 716 non-coded diabetes cases, 13 non-coded Parkinson cases, and 420 non-coded asthma/COPD cases among 34,865 treatment episodes.
Find related publications in this database (using NLM MeSH Indexing)
Algorithms -
Asthma - classification
Clinical Coding - methods
Data Mining - methods
Databases, Factual -
Diabetes Mellitus - classification
Diagnosis-Related Groups -
Electronic Health Records -
Hospitals -
Humans -
Information Systems - organization and administration
Parkinson Disease - classification
Patient Discharge -
Pulmonary Disease, Chronic Obstructive - classification
Terminology as Topic -

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