College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqin Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China.
Artif Intell Med. 2012 May;55(1):13-24. doi: 10.1016/j.artmed.2011.10.002. Epub 2011 Dec 5.
Clinicians rely on a large amount of medical knowledge when performing clinical work. In clinical environment, clinical organizations must exploit effective methods of seeking and recommending appropriate medical knowledge in order to help clinicians perform their work.
Aiming at supporting medical knowledge search more accurately and realistically, this paper proposes a collaboration-based medical knowledge recommendation approach. In particular, the proposed approach generates clinician trust profile based on the measure of trust factors implicitly from clinicians' past rating behaviors on knowledge items. And then the generated clinician trust profile is incorporated into collaborative filtering techniques to improve the quality of medical knowledge recommendation, to solve the information-overload problem by suggesting knowledge items of interest to clinicians.
Two case studies are conducted at Zhejiang Huzhou Central Hospital of China. One case study is about the drug recommendation hold in the endocrinology department of the hospital. The experimental dataset records 16 clinicians' drug prescribing tracks in six months. This case study shows a proof-of-concept of the proposed approach. The other case study addresses the problem of radiological computed tomography (CT)-scan report recommendation. In particular, 30 pieces of CT-scan examinational reports about cerebral hemorrhage patients are collected from electronic medical record systems of the hospital, and are evaluated and rated by 19 radiologists of the radiology department and 7 clinicians of the neurology department, respectively. This case study provides some confidence the proposed approach will scale up.
The experimental results show that the proposed approach performs well in recommending medical knowledge items of interest to clinicians, which indicates that the proposed approach is feasible in clinical practice.
临床医生在进行临床工作时依赖大量医学知识。在临床环境中，临床组织必须利用有效的方法来寻找和推荐合适的医学知识，以帮助临床医生完成工作。
针对更准确、更真实地支持医学知识搜索的问题，本文提出了一种基于协作的医学知识推荐方法。具体来说，该方法基于从临床医生对知识项的过去评分行为中隐含的信任因素的度量，生成临床医生的信任档案。然后，将生成的临床医生信任档案纳入协同过滤技术中，以提高医学知识推荐的质量，通过向临床医生推荐感兴趣的知识项来解决信息过载问题。
在中国浙江湖州中心医院进行了两项案例研究。一项案例研究是关于医院内分泌科的药物推荐。实验数据集记录了 16 名临床医生在六个月内的药物处方记录。该案例研究证明了所提出方法的概念验证。另一项案例研究解决了放射学计算机断层扫描 (CT) 扫描报告推荐的问题。具体来说，从医院的电子病历系统中收集了 30 份关于脑出血患者的 CT 扫描检查报告，并由放射科的 19 名放射科医生和神经科的 7 名临床医生分别进行评估和评分。该案例研究提供了一些信心，即所提出的方法将具有可扩展性。
实验结果表明，所提出的方法在向临床医生推荐感兴趣的医学知识项方面表现良好，这表明该方法在临床实践中是可行的。
