In the hospital world there are several complex combinatory problems, and solving these problems is important to increase the degree of patients' satisfaction and the quality of care offered. The problems in the healthcare are complex since to solve them several constraints and different type of resources should be taken into account. Moreover, the solutions must be evaluated in a small amount of time to ensure the usability in real scenarios. We plan to propose solutions to these kind of problems both expanding already tested solutions and by modelling solutions for new problems, taking into account the literature and by using real data when available. Solving these kind of problems is important but, since the European Commission established with the General Data Protection Regulation that each person has the right to ask for explanation of the decision taken by an AI, without developing Explainability methodologies the usage of AI based solvers e.g. those based on Answer Set programming will be limited. Thus, another part of the research will be devoted to study and propose new methodologies for explaining the solutions obtained.
翻译:在医院世界,存在若干复杂的综合问题,解决这些问题对于提高病人满意度和提供护理的质量十分重要。保健问题很复杂,因为要解决这些疾病,需要考虑一些限制因素和不同种类的资源。此外,必须用少量的时间评估解决办法,以确保在真实情况下使用。我们计划提出解决这些问题的办法,扩大已经测试过的解决办法,并在考虑到文献的情况下,利用实际数据来模拟解决新问题的办法。解决这些问题很重要。解决这类问题,因为欧洲联盟委员会根据《一般数据保护条例》规定,每个人都有权要求解释AI做出的决定,而没有制定可解释的方法,例如,基于“回答方法”的解决方案的使用将受到限制。因此,研究的另外一部分将专门用于研究和提出解释获得的解决办法的新方法。