Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2075-2078. doi: 10.1109/EMBC46164.2021.9630137.
Tinnitus is the perception of a phantom sound and the individual's reaction to it. Although much progress has been made, tinnitus remains an unresolved scientific and clinical issue, affecting more than 10% of the general population and having a high prevalence and socioeconomic burden. Clinical decision support systems (CDSS) are used to assist clinicians in their complex decision-making processes, having been proved that they improve healthcare delivery. In this paper, we present a CDSS for tinnitus, attempting to address the question which treatment approach is optimal for a particular patient based on specific parameters. The CDSS will be developed in the context of the EU-funded "UNITI" project and, after the project completion, it will be able to determine the suitability and expected attachment of a particular patient to a list of available clinical interventions, utilizing predictive and classification machine learning models.Clinical Relevance - The proposed clinically utilizable CDSS will be able to suggest the optimal treatment strategy for the tinnitus patient based on a set of heterogeneous data.
耳鸣是一种幻听的感知和个体对此的反应。尽管已经取得了很大的进展,但耳鸣仍然是一个未解决的科学和临床问题,影响着超过 10%的普通人群,具有较高的患病率和社会经济负担。临床决策支持系统(CDSS)用于协助临床医生进行复杂的决策过程,已经证明它们可以改善医疗保健的提供。在本文中,我们提出了一个耳鸣的 CDSS,试图根据特定参数回答对于特定患者哪种治疗方法是最佳的问题。CDSS 将在欧盟资助的“UNITI”项目的背景下进行开发,并且在项目完成后,它将能够确定特定患者适合和预期的一系列可用临床干预措施,利用预测和分类机器学习模型。临床相关性 - 拟议的临床实用 CDSS 将能够根据一组异构数据为耳鸣患者建议最佳的治疗策略。