Control Matching via Discharge Code Sequences
This work addresses case-control matching for large-scale cancer datasets where clinical information is codified, representing an incremental improvement.
The paper tackled the patient similarity matching problem in a cancer cohort of over 220,000 patients by embedding ICD codes using Word2Vec and applying a sequential matching algorithm, which improved matching accuracy as measured by multiple clinical outcomes.
In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220,000 patients. Our approach first leverages on Word2Vec framework to embed ICD codes into vector-valued representation. We then propose a sequential algorithm for case-control matching on this representation space of diagnosis codes. The novel practice of applying the sequential matching on the vector representation lifted the matching accuracy measured through multiple clinical outcomes. We reported the results on a large-scale dataset to demonstrate the effectiveness of our method. For such a large dataset where most clinical information has been codified, the new method is particularly relevant.