Automated clinical coding using semantic atoms and topology

Barrett, Neil; Weber-Jahnke, Jens H.; Thai, Vincent

doi:10.1109/cbms.2012.6266386

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The second one is to create an efficient encoding scheme (Ryan et al, 2007). The existing methodologies for mapping clinical text in EHR to SCT concepts range from manual to semi-automatic and automatic methods (Barrett et al, 2012;Lamy et al, 2013;Lee et al, 2010). Most automatic methodologies use Natural Language Processing (NLP) tools, such as OpenNLP.…”

Section: Related Workmentioning

confidence: 99%

An encoding methodology for medical knowledge using SNOMED CT ontology

El–Sappagh

Elmogy

2016

Journal of King Saud University - Computer and Information Scie

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Knowledge-Intensive Case Based Reasoning (KI-CBR) systems mainly depend on ontology. Using ontology as domain knowledge supports the implementation of semanticallyintelligent case retrieval algorithms. The case-based knowledge must be encoded with the same concepts of the domain ontology. Standard medical ontologies, such as SNOMED CT (SCT), can play the role of domain ontology to enhance case representation and retrieval. This study has three stages. First, we propose an encoding methodology using SCT. Second, this methodology is used to encode the case-based knowledge. Third, all the used SCT concepts are collected in a reference set, and an OWL2 ontology of 550 pre-coordinated concepts is proposed. A diabetes diagnosis is chosen as a case study of our proposed framework. SCT is used to provide a pre-coordination concept coverage of $75% for diabetes diagnosis terms. Whereas, the uncovered concepts in SCT are proposed. The resulting OWL2 ontology will be used as domain knowledge representation in diabetes diagnosis CBR systems. The proposed framework is tested by using 60 real cases. Ó 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Related Workmentioning

confidence: 99%

An encoding methodology for medical knowledge using SNOMED CT ontology

El–Sappagh

Elmogy

2016

Journal of King Saud University - Computer and Information Scie

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“…Lee et al (2010) presented a method for manually encoding text with SNOMED CT. There also has been recent work in automatically mapping text to SNOMED CT pre-defined concepts (Jung et al, 2009;Stenzhorn et al, 2009;Barrett et al, 2012) or UMLS pre-defined concepts (Aronson and Lang, 2010). However, these systems at best do an approximate match from clinical phrases to pre-defined concepts, also known as pre-coordinated expressions.…”

Section: Background and Related Workmentioning

confidence: 99%

“…A concept in SNOMED CT is defined in terms of its relations with other concepts and it currently includes around four hundred thousand pre-defined clinical concepts. If a natural language clinical phrase represents a concept which is already present in SNOMED CT then the conversion process reduces to a matching function; some previous work (Stenzhorn et al, 2009;Barrett et al, 2012) as well as existing SNOMED CT browsers, like CliniClue, 1 can automatically perform such a matching. But our focus in this paper is instead on the task of creating new SNOMED CT concepts for clinical phrases for which no SNOMED CT concept already exists.…”

Section: Introductionmentioning

confidence: 99%

Towards Converting Clinical Phrases into SNOMED CT Expressions

Kate

2013

Biomed Inform Insights

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Converting information contained in natural language clinical text into computer-amenable structured representations can automate many clinical applications. As a step towards that goal, we present a method which could help in converting novel clinical phrases into new expressions in SNOMED CT, a standard clinical terminology. Since expressions in SNOMED CT are written in terms of their relations with other SNOMED CT concepts, we formulate the important task of identifying relations between clinical phrases and SNOMED CT concepts. We present a machine learning approach for this task and using the dataset of existing SNOMED CT relations we show that it performs well.

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Normalizing clinical terms using learned edit distance patterns

Kate

2015

Journal of the American Medical Informatics Association

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Background Variations of clinical terms are very commonly encountered in clinical texts. Normalization methods that use similarity measures or hand-coded approximation rules for matching clinical terms to standard terminologies have limited accuracy and coverage. Materials and Methods In this paper, a novel method is presented that automatically learns patterns of variations of clinical terms from known variations from a resource such as the Unified Medical Language System (UMLS). The patterns are first learned by computing edit distances between the known variations, which are then appropriately generalized for normalizing previously unseen terms. The method was applied and evaluated on the disease and disorder mention normalization task using the dataset of SemEval 2014 and compared with the normalization ability of the MetaMap system and a method based on cosine similarity. Results Excluding the mentions that already exactly match in UMLS and the training dataset, the proposed method obtained 64.7% accuracy on the rest of the test dataset. The accuracy was calculated as the number of mentions that correctly matched the gold-standard concept unique identifiers (CUIs) or correctly matched to be without a CUI. In comparison, MetaMap’s accuracy was 41.9% and cosine similarity’s accuracy was 44.6%. When only the output CUIs were evaluated, the proposed method obtained 54.4% best F -measure (at 92.1% precision and 38.6% recall) while MetaMap obtained 19.4% best F -measure (at 38.0% precision and 13.0% recall) and cosine similarity obtained 38.1% best F -measure (at 70.3% precision and 26.1% recall). Conclusions The novel method was found to perform much better than the MetaMap system and the cosine similarity based method in normalizing disease mentions in clinical text that did not exactly match in UMLS. The method is also general and can be used for normalizing clinical terms of other semantic types as well.

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Automated clinical coding using semantic atoms and topology

Cited by 5 publications

References 11 publications

An encoding methodology for medical knowledge using SNOMED CT ontology

An encoding methodology for medical knowledge using SNOMED CT ontology

Towards Converting Clinical Phrases into SNOMED CT Expressions

Normalizing clinical terms using learned edit distance patterns

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