From episodes of care to diagnosis codes: automatic text categorization for medico-economic encoding.
We report on the design and evaluation of an original system to help assignment ICD (International Classification of Disease) codes to clinical narratives. The task is defined as a multi-class multi-document classification task. We combine a set of machine learning and data-poor methods to generate a single automatic text categorizer, which returns a ranked list of ICD codes. The combined ranking system currently obtains a precision of 75% at high ranks and a recall of about 63% for the top twenty returned codes for a theoretical upper bound of about 79% (inter-coder agreement). The performance of the data-poor classifier is weak, whereas the use of tempo-rally-typed contents such as anamnesis and prescription free text sections results in a statistically significant improvement.
Ruch
Patrick
P
Medical Informatics Service, University and University Hospitals of Geneva, Geneva, Switzerland. patrick.ruch@sim.hcuge.ch
Gobeilla
Julien
J
Tbahritia
Imad
I
Geissbühlera
Antoine
A
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D038801:International Classification of Diseases / Q000458:organization & administration*; D008487:Medical History Taking / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D013358:Subject Headings; D013557:Switzerland