title: An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records.
abstract: BACKGROUND
  Diagnosis codes are assigned to medical records in healthcare facilities by trained coders by reviewing all physician authored documents associated with a patient's visit. This is a necessary and complex task involving coders adhering to coding guidelines and coding all assignable codes. With the popularity of electronic medical records (EMRs), computational approaches to code assignment have been proposed in the recent years. However, most efforts have focused on single and often short clinical narratives, while realistic scenarios warrant full EMR level analysis for code assignment.
  
  
  OBJECTIVE
  We evaluate supervised learning approaches to automatically assign international classification of diseases (ninth revision) - clinical modification (ICD-9-CM) codes to EMRs by experimenting with a large realistic EMR dataset. The overall goal is to identify methods that offer superior performance in this task when considering such datasets.
  
  
  METHODS
  We use a dataset of 71,463 EMRs corresponding to in-patient visits with discharge date falling in a two year period (2011-2012) from the University of Kentucky (UKY) Medical Center. We curate a smaller subset of this dataset and also use a third gold standard dataset of radiology reports. We conduct experiments using different problem transformation approaches with feature and data selection components and employing suitable label calibration and ranking methods with novel features involving code co-occurrence frequencies and latent code associations.
  
  
  RESULTS
  Over all codes with at least 50 training examples we obtain a micro F-score of 0.48. On the set of codes that occur at least in 1% of the two year dataset, we achieve a micro F-score of 0.54. For the smaller radiology report dataset, the classifier chaining approach yields best results. For the smaller subset of the UKY dataset, feature selection, data selection, and label calibration offer best performance.
  
  
  CONCLUSIONS
  We show that datasets at different scale (size of the EMRs, number of distinct codes) and with different characteristics warrant different learning approaches. For shorter narratives pertaining to a particular medical subdomain (e.g., radiology, pathology), classifier chaining is ideal given the codes are highly related with each other. For realistic in-patient full EMRs, feature and data selection methods offer high performance for smaller datasets. However, for large EMR datasets, we observe that the binary relevance approach with learning-to-rank based code reranking offers the best performance. Regardless of the training dataset size, for general EMRs, label calibration to select the optimal number of labels is an indispensable final step.
authors.affiliation: Division of Biomedical Informatics, Department of Biostatistics, University of Kentucky, 230E MDS Building, 725 Rose Street, Lexington, KY 40536, USA; Department of Computer Science, University of Kentucky, Davis Marksbury Building, 329 Rose Street, Lexington, KY 40506, USA. Electronic address: ramakanth.kavuluru@uky.edu.
authors.forename: Ramakanth
authors.initials: R
authors.lastname: Kavuluru
authors.affiliation: Department of Computer Science, University of Kentucky, Davis Marksbury Building, 329 Rose Street, Lexington, KY 40506, USA. Electronic address: anthony.rios1@uky.edu.
authors.forename: Anthony
authors.initials: A
authors.lastname: Rios
authors.affiliation: Department of Computer Science, University of Kentucky, Davis Marksbury Building, 329 Rose Street, Lexington, KY 40506, USA. Electronic address: yuan.lu@uky.edu.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Lu
mesh_terms: D066264:Datasets as Topic; D004194:Disease / Q000145:classification*; D057286:Electronic Health Records; D036262:Empirical Research; D006801:Humans
keywords: Diagnosis code assignment; Label calibration; Learning to rank; Multi-label text classification