Improving ICD-based semantic similarity by accounting for varying
  degrees of comorbidity
Jan Janosch Schneider Marius Adler Christoph Ammer-Herrmenau Alexander Otto König Ulrich Sax Jonas Hügel
Finding similar patients is a common objective in precision medicine,
facilitating treatment outcome assessment and clinical decision support.
Choosing widely-available patient features and appropriate mathematical methods
for similarity calculations is crucial. International Statistical
Classification of Diseases and Related Health Problems (ICD) codes are used
worldwide to encode diseases and are available for nearly all patients.
Aggregated as sets consisting of primary and secondary diagnoses they can
display a degree of comorbidity and reveal comorbidity patterns. It is possible
to compute the similarity of patients based on their ICD codes by using
semantic similarity algorithms. These algorithms have been traditionally
evaluated using a single-term expert rated data set.
  However, real-word patient data often display varying degrees of documented
comorbidities that might impair algorithm performance. To account for this, we
present a scale term that considers documented comorbidity-variance. In this
work, we compared the performance of 80 combinations of established algorithms
in terms of semantic similarity based on ICD-code sets. The sets have been
extracted from patients with a C25.X (pancreatic cancer) primary diagnosis and
provide a variety of different combinations of ICD-codes. Using our scale term
we yielded the best results with a combination of level-based information
content, Leacock & Chodorow concept similarity and bipartite graph matching for
the set similarities reaching a correlation of 0.75 with our expert's ground
truth. Our results highlight the importance of accounting for comorbidity
variance while demonstrating how well current semantic similarity algorithms
perform.