Supporting Clinical Decision Making: Semantics Based Classification of Medical Referral Letters
DOI:
https://doi.org/10.32734/jocai.v7.i1-10222Keywords:
Clinical and Health Information, Classification, Decision Systems, Natural Language Processing, Support Vector Machines, Semantic IndexingAbstract
This study aims to develop a Natural Language Processing based decision support system built from a repository of knowledge drawn from referral letters written between primary care doctors and specialist medical consultants. The developed system translates pre-processed referral letters into a semantic matrix of document vectors and a set of vocabulary features, based solely on the words used within each referral letter. The system applies a one-versus-rest heuristic using a Support Vector Machine (SVM) to convert a multinomial classification problem into individual binary classifications. Each document is matched to its probabilistic best fit specialism. The National Health Service Wales sourced 111,700 examples. Accuracy of 91.8% against 29 medical specialities is achieved. Accuracy increases to 97.4% and 99%, respectively, when also including one or two nearest neighbours to the best fit, providing a basis for informing the decision making of a medical professional. The study demonstrates the efficacy of using referral letters to allow or classification into specialisms and subsequent allocation of specialist care. The approach taken in this study does not require added ontologies and is readily extendable. The system offers support to medical professionals, particularly within training scenarios or where access to opinion may be in short supply.
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