  Clinical coding is an administrative process that involves the translation of
diagnostic data from episodes of care into a standard code format such as
ICD10. It has many critical applications such as billing and aetiology
research. The automation of clinical coding is very challenging due to data
sparsity, low interoperability of digital health systems, complexity of
real-life diagnosis coupled with the huge size of ICD10 code space. Related
work suffer from low applicability due to reliance on many data sources,
inefficient modelling and less generalizable solutions. We propose a novel
collaborative residual learning based model to automatically predict ICD10
codes employing only prescriptions data. Extensive experiments were performed
on two real-world clinical datasets (outpatient & inpatient) from Maharaj
Nakorn Chiang Mai Hospital with real case-mix distributions. We obtain
multi-label classification accuracy of 0.71 and 0.57 of average precision, 0.57
and 0.38 of F1-score and 0.73 and 0.44 of accuracy in predicting principal
diagnosis for inpatient and outpatient datasets respectively.
