Collaborative residual learners for automatic icd10 prediction using prescribed medications

TL;DR

This paper introduces a collaborative residual learning model that predicts ICD10 codes from prescriptions data, addressing challenges like data sparsity and system interoperability in clinical coding.

Contribution

It presents a novel model that uses only prescriptions data for ICD10 prediction, improving applicability and efficiency over existing methods.

Findings

Achieved 0.71 and 0.57 multi-label accuracy on inpatient and outpatient datasets.

Obtained 0.57 and 0.38 F1-scores for ICD10 code prediction.

Demonstrated effectiveness on real-world clinical datasets from a major hospital.

Abstract

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.