Distributed knowledge based clinical auto-coding system

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD- 10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously

Analysing effectiveness of multi-label classification in clinical coding

In Australia, hospital discharge summaries created at the end of an episode of care contain the patient's medical information based on which clinical codes are assigned. A patient can have multiple diseases and interventions carried out during their stay in the hospital. In this paper, we have done multi-label diseases and interventions classification using Binary Relevance, Label Power-set, and Multi-Layer k-Nearest Neighbor classifier. Our experimental work is divided into three tasks: Random Selection, User Selected, and Repetitive Task. Repetitive task gave better performance in comparison to the other two task