Recommender Systems in Telemedicine
Recommender Systems in Telemedicine
复制标题
远程医疗中的推荐系统
DOI：
10.3182/20130925-3-cz-3023.00005
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发表时间：
2013
影响因子：
4
通讯作者：
W. Halang
中科院分区：
文献类型：
--
作者：
Maytiyanin Komkhao;W. Halang
摘要 由于推荐系统已经通过提供个性化推荐证明了其在电子商务中的有用性，因此该方法将被转移到医学领域。特别是，发展中国家、偏远地区或不确定情况下的乡村医院的医生做出的诊断将得到基于大量专业知识的机器建议的补充，以降低患者的风险。为此，集中维护患者病史数据库和集群模型。该模型是通过协作和基于知识的过滤相结合逐步构建的，在此过程中，它永久地扩大了给定医学领域的知识库。为了给出推荐，需要寻找与所考虑的患者的诊断模式最匹配的模型聚类。在推荐后实际应用的治疗及其随后观察到的结果将被反馈以用于模型更新。随时可用的个人数字配件可用于远程数据输入和推荐显示以及与中央站点的通信。该方法使用头盆不称的数据在妇产科领域得到了验证。
Abstract As recommender systems have proven their usefulness in e-commerce by providing personalised recommendations, the approach is to be transferred to medicine. Particularly, the diagnoses made by physicians in rural hospitals of developing countries, in remote areas or in situations of uncertainty are to be complemented by machine recommendations drawing on large bases of expert knowledge to reduce the risk to patients. To this end, a database of patients' medical history and a cluster model is maintained centrally. The model is constructed incrementally by a combination of collaborative and knowledge-based filtering, in the course of which it permanently widens its knowledge base on a medical area given. To give a recommendation, the model's cluster matching the diagnostic pattern of a considered patient best is sought. The therapy actually applied after the recommendation and its subsequently observed consequences are fed back for model updating. Readily available personal digital accessories can be used for remote data entry and recommendation display as well as for communication with the central site. The approach is validated in the area of obstetrics and gynecology using data on cephalopelvic disproportion.