Bui A A, McNitt-Gray M F, Goldin J G, Cardenas A F, Aberle D R
University of California at Los Angeles (UCLA), 90024, USA.
J Am Med Inform Assoc. 2001 May-Jun;8(3):242-53. doi: 10.1136/jamia.2001.0080242.
Prefetching methods have traditionally been used to restore archived images from picture archiving and communication systems to diagnostic imaging workstations prior to anticipated need, facilitating timely comparison of historical studies and patient management. The authors describe a problem-oriented prefetching scheme, detailing 1) a mechanism supporting selection of patients for prefetching via characterizations of clinical problems, using multiple data sources (picture archiving and communication systems, hospital information systems, and radiology information systems), classifying patients into cohorts on the basis of their medical conditions (e.g., lung cancer); and 2) prefetching of multimedia data (imaging, laboratory, and medical reports) from clinical databases to enable the viewing of an integrated patient record. Preliminary evaluation of the prefetching algorithm using classic information retrieval measures showed that the system had high recall (100 percent), correctly identifying and retrieving data for all patients belonging to a target cohort, but low precision (50 percent). A key finding during testing was that the recall of the system was increased through the use of multiple data sources (compared with one data source), because of better patient descriptors. Medical problems and patient cohorts were more specifically defined by combining information from heterogeneous databases.
预取方法传统上用于在预期需要之前将图片存档与通信系统中的存档图像恢复到诊断成像工作站，以便及时比较历史研究结果并进行患者管理。作者描述了一种面向问题的预取方案，详细介绍了：1）一种机制，该机制通过利用多个数据源（图片存档与通信系统、医院信息系统和放射学信息系统）对临床问题进行特征描述，从而支持选择要预取的患者，并根据患者的医疗状况（如肺癌）将患者分类成群组；2）从临床数据库中预取多媒体数据（成像、实验室和医疗报告），以便能够查看综合的患者记录。使用经典信息检索指标对预取算法进行的初步评估表明，该系统具有较高的召回率（100%），能够正确识别并检索属于目标群组的所有患者的数据，但精度较低（50%）。测试期间的一个关键发现是，由于使用了更好的患者描述符，通过使用多个数据源（与使用一个数据源相比），系统的召回率有所提高。通过整合来自异构数据库的信息，可以更具体地定义医疗问题和患者群组。
