title: Assigning clinical codes with data-driven concept representation on Dutch clinical free text.
abstract: Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and performed procedures during that stay. This paper aims to enrich algorithms for automated clinical coding by taking a data-driven approach and by using unsupervised and semi-supervised techniques for the extraction of multi-word expressions that convey a generalisable medical meaning (referred to as concepts). Several methods for extracting concepts from text are compared, two of which are constructed from a large unannotated corpus of clinical free text. A distributional semantic model (i.c. the word2vec skip-gram model) is used to generalize over concepts and retrieve relations between them. These methods are validated on three sets of patient stay data, in the disease areas of urology, cardiology, and gastroenterology. The datasets are in Dutch, which introduces a limitation on available concept definitions from expert-based ontologies (e.g. UMLS). The results show that when expert-based knowledge in ontologies is unavailable, concepts derived from raw clinical texts are a reliable alternative. Both concepts derived from raw clinical texts perform and concepts derived from expert-created dictionaries outperform a bag-of-words approach in clinical code assignment. Adding features based on tokens that appear in a semantically similar context has a positive influence for predicting diagnostic codes. Furthermore, the experiments indicate that a distributional semantics model can find relations between semantically related concepts in texts but also introduces erroneous and redundant relations, which can undermine clinical coding performance.
authors.affiliation: University of Antwerp, Advanced Database Research and Modelling Research Group (ADReM), Middelheimlaan 1, B-2020 Antwerp, Belgium; University of Antwerp, Computational Linguistics and Psycholinguistics (CLiPS) Research Center, Lange Winkelstraat 40-42, B-2000 Antwerp, Belgium. Electronic address: elyne.scheurwegs@uantwerpen.be.
authors.forename: Elyne
authors.initials: E
authors.lastname: Scheurwegs
authors.affiliation: Antwerp University Hospital, ICT Department, Wilrijkstraat 10, B-2650 Edegem, Belgium.
authors.forename: Kim
authors.initials: K
authors.lastname: Luyckx
authors.affiliation: Antwerp University Hospital, Medical Information Department, Wilrijkstraat 10, B-2650 Edegem, Belgium.
authors.forename: Léon
authors.initials: L
authors.lastname: Luyten
authors.affiliation: University of Antwerp, Advanced Database Research and Modelling Research Group (ADReM), Middelheimlaan 1, B-2020 Antwerp, Belgium.
authors.forename: Bart
authors.initials: B
authors.lastname: Goethals
authors.affiliation: University of Antwerp, Computational Linguistics and Psycholinguistics (CLiPS) Research Center, Lange Winkelstraat 40-42, B-2000 Antwerp, Belgium.
authors.forename: Walter
authors.initials: W
authors.lastname: Daelemans
mesh_terms: D000465:Algorithms; D059019:Clinical Coding; D006801:Humans; D051188:Knowledge Bases; D007802:Language; D009323:Natural Language Processing; D009426:Netherlands; D012660:Semantics
keywords: Clinical coding; Data mining; Distributional semantics; Electronic health records; International classification of diseases; Text mining; Unsupervised learning; Word2vec