Muro Naiara, Larburu Nekane, Bouaud Jacques, Seroussi Brigitte
Sorbonne Universités, UPMC Univ Paris 06, INSERM, Université Paris 13, Sorbonne Paris Cité, UMR S 1142, LIMICS, Paris, France.
eHealth and Biomedical Applications, Vicomtech-IK4, Donostia-San Sebastian, Spain.
Stud Health Technol Inform. 2017;244:33-37.
Technologies such as decision support systems are expected to help clinicians implement clinical practice guidelines (CPGs) with the aim of decreasing practice variations and improving clinical outcomes. However, if CPGs provide recommendations to improve patient care, they may fail to take into account actual clinical outcomes associated to the recommended treatment, such as adverse events or secondary effects. In this paper, we present a novel experience-based decision support approach applied to the management of breast cancer, the most commonly diagnosed cancer among women worldwide. Capitalizing on the clinical know-how of physicians and the modeling of patient's outcomes and toxicities in a computer interpretable way, we are able to discover new knowledge that helps improving patient-centered clinical care. This work is conducted within the EU Horizon 2020 project DESIREE.
诸如决策支持系统之类的技术有望帮助临床医生实施临床实践指南（CPG），以减少实践差异并改善临床结果。然而，如果CPG提供改善患者护理的建议，它们可能无法考虑与推荐治疗相关的实际临床结果，例如不良事件或副作用。在本文中，我们提出了一种新颖的基于经验的决策支持方法，该方法应用于乳腺癌的管理，乳腺癌是全球女性中最常被诊断出的癌症。利用医生的临床专业知识以及以计算机可解释的方式对患者的结果和毒性进行建模，我们能够发现有助于改善以患者为中心的临床护理的新知识。这项工作是在欧盟地平线2020项目DESIREE中进行的。
