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. © Springer International Publishing AG 2017.
Recurrent Neural Network Architectures for Event Extraction from Italian Medical Reports
VIANI, NATALIA;NAPOLITANO, CARLO;Priori, Silvia G.;Bellazzi, Riccardo;Sacchi, Lucia;
2017-01-01
Abstract
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. © Springer International Publishing AG 2017.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
