Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, Spain.
Servicio de Endocrinología y Nutrición del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain.
Sensors (Basel). 2017 Dec 29;18(1):79. doi: 10.3390/s18010079.
Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.
预期寿命在延长，因此，患者需要与慢性疾病和合并症共存的时间也在延长。2 型糖尿病是最常见的慢性疾病之一，特别是与超重和年龄超过六十岁有关。最近的研究表明，通过积极健康的生活方式组合，对中高危人群队列进行干预，可以延迟甚至预防 2 型糖尿病的发生，这一策略是有效的。前瞻性研究已经在大量人群中进行，以建立风险评分，旨在根据患病的几率对患者进行分类。目前，有两百多种用于此目的的模型和风险评分，但只有少数在外部群体中得到了适当的评估，并整合到临床应用中以支持决策。在本文中，我们提出了一种基于服务编排和混合建模的新型系统架构，该架构能够在临床环境中部署分布式集成的临床数据库、统计和数学引擎以及 Web 接口。该系统在八周的连续时间内由一家医院的八名内分泌学家进行了评估，他们使用两个数学引擎评估了多达 8080 名患有七种不同 2 型糖尿病风险模型的患者。吞吐量被评估为技术关键性能指标的问题，这证实了所提出的架构的可靠性和效率，可将混合人工智能工具集成到日常临床常规中，以识别高风险患者。
