Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data
Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data
复制标题
DOI：
10.1197/jamia.m3083
复制
发表时间：
2009-07-01
影响因子：
6.4
通讯作者：
Taylor, Robert J.
中科院分区：
文献类型：
--
作者：
Childs, Lois C.;Enelow, Robert;Taylor, Robert J.
由国家生物医学计算中心（i2b2）赞助的“肥胖症挑战”要求参与者构建软件系统，该系统可以“读取”患者的临床出院摘要，并复制医生在评估是否存在肥胖症和15种合并症时的判断。作者描述了他们的方法，并讨论了应用洛克希德·马丁公司基于规则的自然语言处理（NLP）能力ClinREAD的结果。我们根据医学领域的专业知识对ClinREAD进行了定制，以根据最可能的结果（如ground truth中定义的）创建指定的默认判断。然后，它使用规则来收集与人类法官可能依赖的证据相似的证据，并应用逻辑模块来权衡收集到的所有证据的强度，以得出最终判断。挑战赛的结果表明，由人类医学专业知识指导的基于规则的系统能够解决医学文本机器处理中的复杂问题。
The Obesity Challenge, sponsored by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing, asked participants to build software systems that could "read" a patient's clinical discharge summary and replicate the judgments of physicians in evaluating presence or absence of obesity and 15 comorbidities. The authors describe their methodology and discuss the results of applying Lockheed Martin's rule-based natural language processing (NLP) capability, ClinREAD. We tailored ClinREAD with medical domain expertise to create assigned default judgments based on the most probable results as defined in the ground truth. It then used rules to collect evidence similar to the evidence that the human judges likely relied upon, and applied a logic module to weigh the strength of all evidence collected to arrive at final judgments. The Challenge results suggest that rule-based systems guided by human medical expertise are capable of solving complex problems in machine processing of medical text.