Automatic construction of rule-based ICD-9-CM coding systems

Our results demonstrate that hand-crafted systems - which proved to be successful in ICD-9-CM coding - can be reproduced by replacing several laborious steps in their construction with machine learning models. These hybrid systems preserve the favourable aspects of rule-based classifiers like good performance, and their development can be achieved rapidly and requires less human effort. Hence the construction of such hybrid systems can be feasible for a set of labels one magnitude bigger, and with more labeled data.