Recurrent Neural Network Architectures for Event Extraction from Italian Medical Reports

Medical reports include many occurrences of relevant events in the form of free-text. To make data easily accessible and improve medical decisions, clinical information extraction is crucial. Traditional extraction methods usually rely on the availability of external resources, or require complex annotated corpora and elaborate designed features. Especially for languages other than English, progress has been limited by scarce availability of tools and resources. In this work, we explore recurrent neural network (RNN) architectures for clinical event extraction from Italian medical reports. The proposed model includes an embedding layer and an RNN layer. To find the best configuration for event extraction, we explored different RNN architectures, including Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). We also tried feeding morpho-syntactic information into the network. The best result was obtained by using the GRU network with additional morpho-syntactic inputs.

[1]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[2]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  John F. Hurdle,et al.  Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research , 2008, Yearbook of Medical Informatics.

[5]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[6]  Alex Graves,et al.  Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.

[7]  Geoffrey Zweig,et al.  Linguistic Regularities in Continuous Space Word Representations , 2013, NAACL.

[8]  James Hammerton,et al.  Named Entity Recognition with Long Short-Term Memory , 2003, CoNLL.

[9]  Yoshua Bengio,et al.  Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding , 2013, INTERSPEECH.

[10]  Vito Pirrelli,et al.  The PAISÀ Corpus of Italian Web Texts , 2014, WaC@EACL.

[11]  Peng Li,et al.  Clinical Information Extraction via Convolutional Neural Network , 2016, ArXiv.

[12]  Daniele Bonadiman,et al.  Deep Neural Networks for Named Entity Recognition in Italian , 2015 .

[13]  Yoav Goldberg,et al.  A Primer on Neural Network Models for Natural Language Processing , 2015, J. Artif. Intell. Res..