University of Antwerp, Computational Linguistics and Psycholinguistics (CLiPS) Research Center, Lange Winkelstraat 40-42, B-2000 Antwerp, Belgium; University of Antwerp, Advanced Database Research and Modelling Research Group (ADReM), Middelheimlaan 1, B-2020 Antwerp, Belgium; Antwerp University Hospital, ICT Department, Wilrijkstraat 10, B-2650 Edegem, Belgium.
University of Antwerp, Computational Linguistics and Psycholinguistics (CLiPS) Research Center, Lange Winkelstraat 40-42, B-2000 Antwerp, Belgium; Antwerp University Hospital, ICT Department, Wilrijkstraat 10, B-2650 Edegem, Belgium.
J Biomed Inform. 2017 Nov;75S:S112-S119. doi: 10.1016/j.jbi.2017.06.007. Epub 2017 Jun 7.
The CEGS N-GRID 2016 Shared Task (Filannino et al., 2017) in Clinical Natural Language Processing introduces the assignment of a severity score to a psychiatric symptom, based on a psychiatric intake report. We present a method that employs the inherent interview-like structure of the report to extract relevant information from the report and generate a representation. The representation consists of a restricted set of psychiatric concepts (and the context they occur in), identified using medical concepts defined in UMLS that are directly related to the psychiatric diagnoses present in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) ontology. Random Forests provides a generalization of the extracted, case-specific features in our representation. The best variant presented here scored an inverse mean absolute error (MAE) of 80.64%. A concise concept-based representation, paired with identification of concept certainty and scope (family, patient), shows a robust performance on the task.
CEGS N-GRID 2016 共享任务（Filannino 等人，2017）在临床自然语言处理中，根据精神科入院报告，为精神症状分配严重程度评分。我们提出了一种方法，该方法利用报告固有的类似访谈的结构，从报告中提取相关信息并生成表示。表示由一组受限的精神科概念（及其出现的上下文）组成，这些概念是使用与精神障碍诊断与统计手册第 4 版（DSM-IV）本体中存在的精神科诊断直接相关的 UMLS 中定义的医学概念识别的。随机森林为我们表示中提取的特定于病例的特征提供了泛化。这里呈现的最佳变体的逆平均绝对误差（MAE）为 80.64%。简洁的基于概念的表示，结合概念确定性和范围（家族、患者）的识别，在该任务上表现出稳健的性能。
