About: Multi-information Source HIN for Medical Concept Embedding

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Multi-information Source HIN for Medical Concept Embedding
Creator	Peng, Hao Cao, Yuwei Yu, Philip
Source	PMC
abstract	Learning low-dimensional representations for medical concepts is of great importance in improving public healthcare applications such as computer-aided diagnosis systems. Existing methods rely on Electronic Health Records (EHR) as their only information source and do not make use of abundant available external medical knowledge, and therefore they ignore the correlations between medical concepts. To address this issue, we propose a novel multi-information source Heterogeneous Information Network (HIN) to model EHR while incorporating external medical knowledge including ICD-9-CM and MeSH for an enriched network schema. Our model is well aware of the structure of EHR as well as the correlations between medical concepts it refers to, and learns semantically reflective medical concept embeddings. In experiments, our model outperforms unsupervised baselines in a variety of medical data mining tasks.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_30
has license	no-cc
sha1sum (hex)	2dc33680880653973ec260220b2c9d025a921e37
schema:url	https://doi.org/10.1007/978-3-030-47436-2_30
resource representing a document's title	Multi-information Source HIN for Medical Concept Embedding
has PubMed Central identifier	PMC7206250
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:2dc33680880653973ec260220b2c9d025a921e37#body_text
is schema:about of	named entity 'computer-aided diagnosis' named entity 'EHR' named entity 'data mining' named entity 'Acute myocardial infarction' named entity 'laboratory test' named entity 'EHR' named entity 'ICD-9-CM' named entity 'EHR' named entity 'medical knowledge' named entity 'MeSH' named entity 'SGD' named entity 'MeSH' named entity 'Hematocrit' named entity 'EHR' named entity 'machine learning' named entity 'random walks' named entity 'Electronic Health Records' named entity 'EHR' named entity 'MeSH' named entity 'softmax function' named entity 'EHR' named entity 'ICD-9-CM' named entity 'ICD-9-CM' named entity 'decimal point' named entity 'k-means algorithm' named entity 'feature extraction' named entity 'Congestive heart failure' named entity 'feature engineering' named entity 'probability' named entity 'Pioneer works' named entity 'Hematocrit' named entity 'word2vec' named entity 'EHR' named entity 'skip-gram' named entity 'information source' named entity 'word2vec' named entity 'EHR' named entity 'Acute myocardial infarction' named entity 'EHR' named entity 'ICD-9-CM' named entity 'ARI' named entity 'EHR' named entity 'ICD-9-CM' named entity 'representation learning' named entity 'skip-gram' named entity 'skip-gram' named entity 'semantics' named entity 'categorical data' named entity 'decimal point' named entity 'Medical Subject Headings' named entity 'Heart Disease' named entity 'training set' named entity 'ICD-9-CM' named entity 'ICD-9' named entity 'MeSH' named entity 'classifier' named entity 'United States' named entity 'EHR' named entity 'Stochastic Gradient Descent' named entity 'self-loops' named entity 'natural language processing' named entity 'heterogeneous network' named entity 'Representation Learning' named entity 'EHR' named entity 'EHR' named entity 'word2vec' named entity 'representation learning' named entity 'ICD-9-CM' named entity 'EHR' named entity 'EHR' named entity 'ARI' named entity 'EHR' named entity 'data mining' named entity 'MeSH' named entity 'ICD-9-CM' named entity 'ICD-9-CM' named entity 'MeSH' named entity 'MeSH codes' named entity 'ICD-9-CM' named entity 'heart failure' named entity 'mutual information'

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