Loda Sophia, Krebs Jonathan, Danhof Sophia, Schreder Martin, Solimando Antonio G, Strifler Susanne, Rasche Leo, Kortüm Martin, Kerscher Alexander, Knop Stefan, Puppe Frank, Einsele Hermann, Bittrich Max
Department of Internal Medicine II, University Hospital Würzburg, 97080 Würzburg, Germany.
Chair for Artificial Intelligence and Applied Informatics, University of Würzburg, 97070 Würzburg, Germany.
J Clin Med. 2019 Jul 9;8(7):999. doi: 10.3390/jcm8070999.
Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text.
Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented "A Rule-based Information Extraction System" (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM.
Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98.
Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.
自然语言处理（NLP）是支持生成真实世界证据（RWE）的强大工具。目前尚无能够对非结构化医学报告中特定于多发性骨髓瘤（MM）的参数进行广泛查询的NLP系统。因此，我们创建了一个特定于MM的本体，以加速从非结构化文本中提取信息（IE）。
我们的MM本体由广泛的特定于MM的分层结构属性和值组成。我们实施了使用该本体的“基于规则的信息提取系统”（ARIES）。我们在200份随机选择的诊断为MM的患者的医学报告上对ARIES进行了评估。
我们的系统在评估数据集上实现了0.92的高F1分数，精确率为0.87，召回率为0.98。
我们基于规则的IE系统能够对医学报告进行全面查询。该IE加速了数据提取，使临床医生能够更快地生成关于血液学问题的RWE。RWE有助于临床医生以循证方式做出决策。我们的工具轻松地加速了研究证据融入日常临床实践的过程。
