Automatic assignment of diagnosis codes to free-form medical notes | 29 March 2022 | Stefan Strydom

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Automatic assignment of diagnosis codes to free-form text medical notes =======================================================================
Clinical coding is the process of describing and categorising healthcare episodes according to standardised ontologies. The coded data have important downstream applications, including population morbidity studies, health systems planning and reimbursement. Clinical codes are generally assigned based on information contained in free-form text clinical notes by specialist human coders. This process is expensive, time-consuming, subject to human error and burdens scarce clinical human resources with administrative roles. An accurate automatic coding system can alleviate these problems.
Clinical coding is a challenging task for machine learning systems. The source texts are often long, has a highly specialised vocabulary, contains non-standard clinician shorthand and the code sets can contain tens-of-thousands of codes.
We review previous work on clinical auto-coding systems and perform an empirical analysis of widely used and current state-of-the-art machine learning approaches to the problem. We propose a novel attention mechanism that takes the text description of clinical codes into account. We also construct a small pre-trained transformer model that achieves state-of-the-art performance on the MIMIC II and III ICD-9 auto-coding tasks. To the best of our knowledge, it is the first successful application of a pre-trained transformer model on this task.
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Stefan Strydom
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Stefan is a co-founder of Mast Analytics, a healthcare-focused analytics and AI company based in Cape Town. He is a data scientist, machine learning engineer and healthcare actuary by trade. In addition to extensive experience in the South African healthcare environment, Stefan and Mast Analytics have completed successful projects in North America, Europe, Asia and other African countries outside of South Africa.
Stefan holds B.Sc. and B.Com. (Honours) degrees in mathematical statistics and an M.Sc. (cum laude) in computer science from Stellenbosch University. The title of his master’s thesis was “Automatic assignment of diagnosis codes to free-form text medical notes.”


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