About: Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study
Creator	Lin, Chin Fang, Wen-Hui Hsu, Chia-Jung Lee, Chia-Cheng Lou, Yu-Sheng Tsai, Dung-Jang Wang, Mei-Chuen Wu, Ding-Chung Eysenbach, Gunther Kakarmath, Sujay Mendes, David Paixao, Klerisson Wang, Yanshan
Source	PMC
abstract	BACKGROUND: Most current state-of-the-art models for searching the International Classification of Diseases, Tenth Revision Clinical Modification (ICD-10-CM) codes use word embedding technology to capture useful semantic properties. However, they are limited by the quality of initial word embeddings. Word embedding trained by electronic health records (EHRs) is considered the best, but the vocabulary diversity is limited by previous medical records. Thus, we require a word embedding model that maintains the vocabulary diversity of open internet databases and the medical terminology understanding of EHRs. Moreover, we need to consider the particularity of the disease classification, wherein discharge notes present only positive disease descriptions. OBJECTIVE: We aimed to propose a projection word2vec model and a hybrid sampling method. In addition, we aimed to conduct a series of experiments to validate the effectiveness of these methods. METHODS: We compared the projection word2vec model and traditional word2vec model using two corpora sources: English Wikipedia and PubMed journal abstracts. We used seven published datasets to measure the medical semantic understanding of the word2vec models and used these embeddings to identify the three–character-level ICD-10-CM diagnostic codes in a set of discharge notes. On the basis of embedding technology improvement, we also tried to apply the hybrid sampling method to improve accuracy. The 94,483 labeled discharge notes from the Tri-Service General Hospital of Taipei, Taiwan, from June 1, 2015, to June 30, 2017, were used. To evaluate the model performance, 24,762 discharge notes from July 1, 2017, to December 31, 2017, from the same hospital were used. Moreover, 74,324 additional discharge notes collected from seven other hospitals were tested. The F-measure, which is the major global measure of effectiveness, was adopted. RESULTS: In medical semantic understanding, the original EHR embeddings and PubMed embeddings exhibited superior performance to the original Wikipedia embeddings. After projection training technology was applied, the projection Wikipedia embeddings exhibited an obvious improvement but did not reach the level of original EHR embeddings or PubMed embeddings. In the subsequent ICD-10-CM coding experiment, the model that used both projection PubMed and Wikipedia embeddings had the highest testing mean F-measure (0.7362 and 0.6693 in Tri-Service General Hospital and the seven other hospitals, respectively). Moreover, the hybrid sampling method was found to improve the model performance (F-measure=0.7371/0.6698). CONCLUSIONS: The word embeddings trained using EHR and PubMed could understand medical semantics better, and the proposed projection word2vec model improved the ability of medical semantics extraction in Wikipedia embeddings. Although the improvement from the projection word2vec model in the real ICD-10-CM coding task was not substantial, the models could effectively handle emerging diseases. The proposed hybrid sampling method enables the model to behave like a human expert.
has issue date	2019-07-23(xsd:dateTime)
bibo:doi	10.2196/14499
bibo:pmid	31339103
has license	cc-by
schema:url	https://doi.org/10.2196/14499
resource representing a document's title	Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study
has PubMed Central identifier	PMC6683650
has PubMed identifier	31339103
schema:publication	JMIR Med Inform
resource representing a document's body	covid:PMC6683650#body_text
is schema:about of	named entity 'ICD-10-CM' named entity 'word2vec' named entity 'word2vec' named entity 'semantic' named entity 'EHR' named entity 'word2vec' named entity 'PubMed' named entity 'Wikipedia' named entity 'PubMed' named entity 'word2vec' named entity 'coevolution' named entity 'cosine similarity' named entity 'ICD-10-CM' named entity 'sampling' named entity 'medical term' named entity 'NLP' named entity 'ICD-10-CM' named entity 'PubMed' named entity 'Multimedia' named entity 'MXNet' named entity 'database' named entity 'word embedding' named entity 'NLP' named entity 'neoplasm' named entity 'EHRs' named entity 'EHRs' named entity 'AlexNet' named entity 'green point' named entity 'data sources' named entity 'hypertension' named entity 'Tri-Service General Hospital' named entity 'Wikipedia' named entity 'medical applications' named entity 'medical coders' named entity 'SARS' named entity 'learning rate' named entity 'EHR' named entity 'semantics' named entity 'pneumonia' named entity 'database' named entity 'Mayo Clinic' named entity 'Research ethics' named entity 'EHRs' named entity 'PubMed' named entity 'linear projection' named entity 'word embeddings' named entity 'word2vec' named entity 'Taichung' named entity 'word embedding' named entity 'pneumonia' named entity 'similarity scores' named entity 'discharge note' named entity 'PubMed' named entity 'NLP' named entity 'medical records' named entity 'cosine similarity' named entity 'NLP' named entity 'NLP' named entity 'oversampling' named entity 'unstructured data' named entity 'word2vec' named entity 'Word embedding' named entity 'Wikipedia' named entity 'PubMed' named entity 'training set' named entity 'transfer learning' named entity 'ICD-10-CM' named entity 'Tri-Service General Hospital' named entity 'word2vec' named entity 'hypertension'

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