The average micro-AUPRCs were 0.3745, 0.6878, and 0.6699, corresponding to directly predictive MIMIC-ICD-10, MIMIC-ICD-10 fine-tuning, and MIMIC-ICD-9 fine-tuning approaches the average macro-AUPRCs for the corresponding models were 0.2819, 0.4219 and 0.5377, respectively. The CNN-PubMedBERT model was externally validated using two subsets of MIMIC-III MIMIC-ICD-10, and MIMIC-ICD-9 datasets, which contained 40,923 and 31,196 discharge summaries.

However, CNN-PubMedBERT performed less well relative to PLM-ICD, with corresponding AUPRCs of 0.7202 and 0.5865. In addition, two state-of-the-art models were also considered convolutional attention for multi-label classification (CAML) and pretrained language models for automatic ICD coding (PLM-ICD). Three algorithms were developed: naïve Bayes with term frequency-inverse document frequency (NB-TF-IDF), convolutional neural network with neural word embedding (CNN-NWE), and CNN with PubMedBERT (CNN-PubMedBERT). The external validation phase included Medical Information Mart for Intensive Care III (MIMIC-III) data. Materials and methods: The development phase included 15,329 discharge summaries from Ramathibodi Hospital from January 2015 to December 2020.

Objectives: To develop an automated international classification of diseases (ICD) coding tool using natural language processing (NLP) and discharge summary texts from Thailand.

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