Using n-gram method in the decomposition of compound medical diagnoses

Héja, Gergely; Surján, György

doi:10.1016/s1386-5056(03)00049-2

Cited by 7 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is reported that the n-gram approach can successfully detect medical compound words [31]. We generated n-gram candidate terms and used rules to determine the best concept.…”

Section: N-gram Approximate Term Mappingmentioning

confidence: 99%

Using UMLS to construct a generalized hierarchical concept-based dictionary of brain functions for information extraction from the fMRI literature

Hsiao

Chen

2009

Journal of Biomedical Informatics

View full text Add to dashboard Cite

With a rapid progress in the field, a great many fMRI studies are published every year, to the extent that it is now becoming difficult for researchers to keep up with the literature, since reading papers is extremely time-consuming and labor-intensive. Thus, automatic information extraction has become an important issue. In this study, we used the Unified Medical Language System (UMLS) to construct a hierarchical concept-based dictionary of brain functions. To the best of our knowledge, this is the first generalized dictionary of this kind. We also developed an information extraction system for recognizing, mapping and classifying terms relevant to human brain study. The precision and recall of our system was on a par with that of human experts in term recognition, term mapping and term classification. Our approach presented in this paper presents an alternative to the more laborious, manual entry approach to information extraction.

show abstract

“…It is reported that the n-gram approach can successfully detect medical compound words [31]. We generated n-gram candidate terms and used rules to determine the best concept.…”

Section: N-gram Approximate Term Mappingmentioning

confidence: 99%

Using UMLS to construct a generalized hierarchical concept-based dictionary of brain functions for information extraction from the fMRI literature

Hsiao

Chen

2009

Journal of Biomedical Informatics

View full text Add to dashboard Cite

show abstract

“…An n-grams is a contiguous sequence of n items from a given sequence of text [27, 28]. It is based on the assumption that in sequence of text the n-th word depends only on the last n-1 word before it, where bigrams (n = 2) and trigrams (n = 3) are commonly used.…”

Section: Resultsmentioning

confidence: 99%

Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity

Chen

2017

PLoS ONE

View full text Add to dashboard Cite

ICD-10(International Classification of Diseases 10th revision) is a classification of a disease, symptom, procedure, or injury. Diseases are often described in patients’ medical records with free texts, such as terms, phrases and paraphrases, which differ significantly from those used in ICD-10 classification. This paper presents an improved approach based on the Longest Common Subsequence (LCS) and semantic similarity for automatic Chinese diagnoses, mapping from the disease names given by clinician to the disease names in ICD-10. LCS refers to the longest string that is a subsequence of every member of a given set of strings. The proposed method of improved LCS in this paper can increase the accuracy of processing in Chinese disease mapping.

show abstract

“…• AM has improved over the last years and a growing trend of AM use can be observed. Certainly, the performance improvement when using AM is evidenced by many research studies on SER [18,20,69,92,[102][103][104]107,108,111]. On the other hand, two works [63,68] did not report improvements when using AM.…”

Section: Impact Of Attention Mechanism On Sermentioning

confidence: 99%

“…Incorporation of the attention mechanism (AM) into DNN-based SER systems was often motivated by research in the NLP field [18,91,92] and computer vision [92]. We give a brief explanation of the attention mechanism from the NLP's point of view due to the similarity of the tasks.…”

Section: Attention Mechanismmentioning

confidence: 99%

“…The best results were reported with a combination of MFB features, attentive CNN with MTL learning. Li et al [92] used two types of convolution filters for extraction of time-specific and frequency-specific features from the spectrograms. Feature extraction was followed by CNN architecture for modelling high-level representation.…”

Section: Attentive Deep Convolutional Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Human Computer Interaction for Intelligent Systems

2023

View full text Add to dashboard Cite

show abstract

Using n-gram method in the decomposition of compound medical diagnoses

Cited by 7 publications

References 4 publications

Using UMLS to construct a generalized hierarchical concept-based dictionary of brain functions for information extraction from the fMRI literature

Using UMLS to construct a generalized hierarchical concept-based dictionary of brain functions for information extraction from the fMRI literature

Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity

Human Computer Interaction for Intelligent Systems

Contact Info

Product

Resources

About