Semi-automatic classification of clinical diagnoses with hybrid approach

Héja, Gergely; Surján, György

doi:10.1109/cbms.2002.1011403

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…They used it to map a diagnosis word into a form/meaning layer, then to convert the corresponding term into a concept layer before matching it to labels of ICD codes in an expression layer. Heja et al [31] compared several models based on N-grams, vector space models, and neural networks for matching diagnosis words with a list of ICD code labels and suggested that a hybrid model yielded better matching results. Pakhomov et al [32] designed an automated coding system that assigned codes to out-patient diagnoses using example-based and machine learning techniques.…”

Section: Semi-automated and Automated Icd Coding Systemsmentioning

confidence: 99%

Development of ICD-10-TM Ontology for a Semi-automated Morbidity Coding System in Thailand

Nitsuwat

Paoin²

2012

Methods Inf Med

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The ICD-10-TM ontology is the first ICD-10 ontology with a comprehensive description of all concepts and relations in an ICD-10-TM tabular list and alphabetical index. A researcher developing an automated ICD coding system should be aware of The ICD index structure and the complexity of coding processes. These coding systems are not a word matching process. ICD-10 ontology should be used as a knowledge base in The ICD coding software. It can be used to facilitate successful implementation of ICD in developing countries, especially in those countries which do not have an adequate number of competent ICD coders.

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Section: Semi-automated and Automated Icd Coding Systemsmentioning

confidence: 99%