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. However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In order to extract FH information form clinical text, there is a need of natural language processing (NLP). 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. For this task, we propose a system based on deep joint learning methods to extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.

中文翻译：

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通过深度联合学习提取家族史信息。

家族史（FH）信息，包括家庭成员，家庭成员的一方（即母亲或父亲），家庭成员的生活状况，家庭成员的观察（疾病）等，在决策中非常重要疾病诊断和治疗的过程。但是，由于FH信息始终嵌入在电子健康记录（EHR）中的非结构化文本中，因此无法直接由计算机使用。为了从临床文本中提取FH信息，需要自然语言处理（NLP）。在BioCreative / OHNLP2018挑战赛中，有一项与FH提取有关的任务（即task1），包括两个子任务：（1）实体识别，识别家庭成员及其在临床案文中提到的观察结果（疾病）；（2）家族史提取，家庭成员家庭一方的提取，家庭成员的生活状况以及家庭成员的观察。为此，我们提出了一种基于深度联合学习方法的系统来提取FH信息。我们的系统在子任务1和子任务2上分别获得最高的F1-分数0.8901和0.6359。