Exploiting Structured Data, Negation Detection and SNOMED CT Terms in a Random Indexing Approach to Clinical Coding

The problem of providing effective computer support for clinical coding has been the target of many research efforts. A recently introduced approach, based on statistical data on co-occurrences of words in clinical notes and assigned diagnosis codes, is here developed further and improved upon. The ability of the word space model to detect and appropriately handle the function of negations is demonstrated to be important in accurately correlating words with diagnosis codes, although the data on which the model is trained needs to be sufficiently large. Moreover, weighting can be performed in various ways, for instance by giving additional weight to ‘clinically significant’ words or by filtering code candidates based on structured patient records data. The results demonstrate the usefulness of both weighting techniques, particularly the latter, yielding 27% exact matches for a general model (across clinic types); 43% and 82% for two domain-specific models (ear-nosethroat and rheumatology clinics).