Ensemble model for pre-discharge icd10 coding prediction
Yassien Shaalan, Alexander Dokumentov, Piyapong Khumrin, Krit Khwanngern, Anawat Wisetborisu, Thanakom Hatsadeang, Nattapat Karaket, Witthawin Achariyaviriya, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Terence Siganakis
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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.