  The translation of medical diagnosis to clinical coding has wide range of
applications in billing, aetiology analysis, and auditing. Currently, coding is
a manual effort while the automation of such task is not straight forward.
Among the challenges are the messy and noisy clinical records, case
complexities, along with the huge ICD10 code space. Previous work mainly relied
on discharge notes for prediction and was applied to a very limited data scale.
We propose an ensemble model incorporating multiple clinical data sources for
accurate code predictions. We further propose an assessment mechanism to
provide confidence rates in predicted outcomes. Extensive experiments were
performed on two new real-world clinical datasets (inpatient & outpatient) with
unaltered case-mix distributions from Maharaj Nakorn Chiang Mai Hospital. We
obtain multi-label classification accuracies of 0.73 and 0.58 for average
precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting
principal diagnosis for inpatient and outpatient datasets respectively.
