Fully-connected LSTM–CRF on medical concept extraction

2020 ◽  
Vol 11 (9) ◽  
pp. 1971-1979
Author(s):  
Jie Ji ◽  
Bairui Chen ◽  
Hongcheng Jiang
Keyword(s):  
Concept Extraction ◽  
Medical Concept ◽  
Fully Connected

scholarly journals Active learning: a step towards automating medical concept extraction

2015 ◽  
Vol 23 (2) ◽  
pp. 289-296 ◽  
Author(s):  
Mahnoosh Kholghi ◽  
Laurianne Sitbon ◽  
Guido Zuccon ◽  
Anthony Nguyen
Keyword(s):  
Active Learning ◽  
Language Processing ◽  
Selection Criteria ◽  
Conditional Random Fields ◽  
Free Text ◽  
Data Sets ◽  
Comparative Performance ◽  
Concept Extraction ◽  
Learning Framework ◽  
Medical Concept

Abstract Objective This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation.

scholarly journals MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7

2014 ◽  
Author(s):  
Alejandro Riveros ◽  
Maria De Arteaga ◽  
Fabio González ◽  
Sergio Jimenez ◽  
Henning Müller
Keyword(s):  
Concept Extraction ◽  
Medical Concept

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

scholarly journals OPTWEB: A Lightweight Fully Connected Inter-FPGA Network for Efficient Collectives

2021 ◽  
pp. 1-1
Author(s):  
Kenji Mizutani ◽  
Hiroshi Yamaguchi ◽  
Yutaka Urino ◽  
Michihiro Koibuchi
Keyword(s):  
Fully Connected
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