Experiments results show that our proposed system achieve an F1- score of 0.8901 on entity identification and an F1-score of 0.6359 on family history extraction, respectively. The method is derived from Miwa et al.’s method by replacing the tree-structured LSTM by a common LSTM for relation extraction and adding a combination coefficient to adjust two subtasks. In this paper, we propose a deep joint learning method for the FH information extraction task (i.e., task 1) of the BioCreative/OHNLP2018 challenge (called BioCreative/OHNLP2018-FH). Recently, deep learning methods have been introduced to tackle joint learning tasks by sharing parameters in a unified neural network framework, such as.
Early joint learning methods combine the models for the two subtaks through various constraints such as integer linear progamming. To avoid error propagation, a few number of joint learning methods have been proposed. When named entity recognition and relation extraction are tackled separately in pipeline methods, it is impossible to avoid propagating errors in named entity recognition to relation extraction without any feedback, which is called error propagation. Machine learning methods mentioned above have been adopted for clinical entity recognition and relation extraction. In the clinical domain, the related techniques develop rapidly due to several shared tasks, such as the NLP challenges organized by the Center for Informatics for Integrating Biology & the Beside (i2b2) in 2009, 2010, 20, the NLP challenges organized by SemEval in 2014, 20, and the NLP challenges organized by ShARe/CLEF in 20. These methods achieve promising results for each task. For relation recognition, traditional machine learning methods, such as maximum entropy (ME), decision trees (DT) and SVM, and deep learning methods, such as convolution neural network (CNN) and recurrent neural network (RNN), are employed. For named entity recognition, traditional machine learning methods, such as support vector machine (SVM), hidden Markov model (HMM), structured support vector machine (SSVM) and conditional random field (CRF), and deep learning methods, such as Long Short Term Memory networks (LSTM) and LSTM-CRF, are deployed. A large number of machine learning methods have been proposed for each one of the two tasks from traditional machine learning methods depending on manually-crafted features to deep learning methods without needing complex feature engineering. Relation extraction is usually treated as a subsequent task of named entity recognition, and they are tackled by pipeline methods. The goal of FH information extraction, as mentioned in the BioCreative/OHNLP2018 challenge, is to recognize relative entities and their attributes, and determine relations between relative entities and their attributes.įH Information Extraction refers to two fundamental tasks of natural language processing (NLP), namely named entity recognition and relation extraction.
Extracting FH information from clinical text is the first step to use this information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.įH information that records health status of family members such as side of family, living status and observations is very important for disorder diagnosis and treatment decision-making and is always embedded in clinical text.
For this task, we propose a system based on deep joint learning methods to extract FH information. In the BioCreative/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1) entity identification, identifying family members and their observations (diseases) mentioned in clinical text (2) family history extraction, extracting side of family of family members, living status of family members, and observations of family members. In order to extract FH information form clinical text, there is a need of natural language processing (NLP). However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making process of disorder diagnosis and treatment.
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