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    <titleInfo>
        <title>Inference of ICD Codes from Japanese Medical Records by Searching Disease Names</title>
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        <namePart type="given">Masahito</namePart>
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            <title>Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Rumshisky</namePart>
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            <namePart type="given">Kirk</namePart>
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            <namePart type="given">Steven</namePart>
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    <abstract>Importance of utilizing medical information is getting increased as electronic health records (EHRs) are widely used nowadays. We aim to assign international standardized disease codes, ICD-10, to Japanese textual information in EHRs for users to reuse the information accurately. In this paper, we propose methods to automatically extract diagnosis and to assign ICD codes to Japanese medical records. Due to the lack of available training data, we dare employed rule-based methods rather than machine learning. We observed characteristics of medical records carefully, writing rules to make effective methods by hand. We applied our system to the NTCIR-12 MedNLPDoc shared task data where participants are required to assign ICD-10 codes of possible diagnosis in given EHRs. In this shared task, our system achieved the highest F-measure score among all participants in the most severe evaluation criteria. Through comparison with other approaches, we show that our approach could be a useful milestone for the future development of Japanese medical record processing.</abstract>
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%0 Conference Proceedings
%T Inference of ICD Codes from Japanese Medical Records by Searching Disease Names
%A Sakishita, Masahito
%A Kano, Yoshinobu
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F sakishita-kano-2016-inference
%X Importance of utilizing medical information is getting increased as electronic health records (EHRs) are widely used nowadays. We aim to assign international standardized disease codes, ICD-10, to Japanese textual information in EHRs for users to reuse the information accurately. In this paper, we propose methods to automatically extract diagnosis and to assign ICD codes to Japanese medical records. Due to the lack of available training data, we dare employed rule-based methods rather than machine learning. We observed characteristics of medical records carefully, writing rules to make effective methods by hand. We applied our system to the NTCIR-12 MedNLPDoc shared task data where participants are required to assign ICD-10 codes of possible diagnosis in given EHRs. In this shared task, our system achieved the highest F-measure score among all participants in the most severe evaluation criteria. Through comparison with other approaches, we show that our approach could be a useful milestone for the future development of Japanese medical record processing.
%U https://aclanthology.org/W16-4209/
%P 64-68

Markdown (Informal)

[Inference of ICD Codes from Japanese Medical Records by Searching Disease Names](https://aclanthology.org/W16-4209/) (Sakishita & Kano, ClinicalNLP 2016)

• Inference of ICD Codes from Japanese Medical Records by Searching Disease Names (Sakishita & Kano, ClinicalNLP 2016)

ACL

• Masahito Sakishita and Yoshinobu Kano. 2016. Inference of ICD Codes from Japanese Medical Records by Searching Disease Names. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 64–68, Osaka, Japan. The COLING 2016 Organizing Committee.