审计
乳房成像
叙述的
计算机科学
放射科
医学
自然语言处理
短信
人工智能
情报检索
万维网
乳腺癌
乳腺摄影术
语言学
内科学
哲学
管理
癌症
经济
作者
Wee Ming Tan,Wei Lin Ng,Mogana Darshini Ganggayah,Victor Chee Wai Hoe,Kartini Rahmat,Hana Salwani Zaini,Nur Aishah Mohd Taib,Sarinder Kaur Dhillon
标识
DOI:10.1177/14604582231203763
摘要
Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstructured breast radiology reports into structured formats using natural language processing (NLP) algorithm. This study used 327 de-identified breast radiology reports from the anonymous institute. The radiologist identified the significant data elements to be extracted. Our NLP algorithm achieved 97% and 94.9% accuracy in training and testing data, respectively. Henceforth, the structured information was used to build the predictive model for predicting the value of the BIRADS category. The model based on random forest generated the highest accuracy of 92%. Our study not only fulfilled the demands of clinicians by enhancing communication between medical personnel, but it also demonstrated the usefulness of mineable structured data in yielding significant insights.
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