Collaborative residual learners for automatic icd10 prediction using prescribed medications
This work aims to improve the efficiency and applicability of clinical coding for healthcare providers by using a single data source (prescriptions), which is an incremental improvement over existing methods.
This paper addresses the challenge of automatic ICD10 code prediction using only prescription data. The proposed model achieved multi-label classification accuracies of 0.71 (inpatient) and 0.57 (outpatient) for average precision, and F1-scores of 0.57 (inpatient) and 0.38 (outpatient).
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.