Post on 20-May-2020
Leveraging Deep Neural Networks And Semantic SimilarityMeasures For Medical Concept Normalization In User Reviews
Miftahutdinov Z.Sh.Tutubalina E.V.
Kazan Federal University
1 june 2018
Problem DescriptionProblem
Medical concept normalization – mapping a disease mention to aconcept in a controlled vocabulary.
Examples:
inflammation in my neck –> C0263854, Cervical arthritisvery painful joints –> C3864084, Arthralgiacan’t sleep –> C0393758, Insomniahigh BP –> C0020538, Increased venous pressure
KFU 1 june 2018 2 / 12
Problem DescriptionPossible Bottlenecks
∙ Social-media language∙ Ambiguity∙ Vocabulary variations∙ Abbreviations∙ Variety of target vocabularies
KFU 1 june 2018 3 / 12
BackgroundMetaMap and DNorm
MetaMap∙ mapping to UMLS∙ rule based
DNorm∙ mapping to MEDIC∙ pairwise learning to rank
KFU 1 june 2018 4 / 12
BackgroundSocial Media Mining for Health
Dataset∙ Twitter∙ mapping to MedDRA concepts∙ Training set 6650 phrases, 472 concepts∙ Test set 2500 phrases, 254 concepts
KFU 1 june 2018 5 / 12
BackgroundSocial Media Mining for Health
Team Results
Team Model Accuracy (%)
gnTeamLogistic Regression 87.7Bi-GRU 85.5Ensemble 88.5
UKNLPHierarchical Char-LSTM 87.2Hierarchical Char-CNN 87.7
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BackgroundLimsopatham and Collier
∙ Data from askapatient∙ mapping to SNOMED∙ 8411 phrases∙ 1029 unique codes∙ 81% Accuracy
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Proposed ModelNeural Network Architecture
w1 UMLS
very poor appetite
Semantic
similarity features
Soft
max
C0232462
Decrease in
appetite
Medical
Concept
RNN
Featuresh1 h2 h3
h'1h'
2h'3
a1 a2 a3
a1 a2 a3
Em
beddin
g
Layer
Bid
irecti
onal R
NN
wit
h a
ttenti
on
KFU 1 june 2018 9 / 12
Results
Model Accuracy (%)DNorm 73.39CNN 81.41RNN 79.98GRU+At., TFIDF 85.71
New FoldsCNN 46.19LSTM 64.51GRU 63.05LSTM + Attention 65.73GRU + Attention 67.08LSTM + Attn, TFIDF 67.63GRU + Attn, TFIDF 69.92
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