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A Fuzzy Discrete Event Systems Approach to Selecting Second-Round Combination Antiretroviral Therapy for HIV/AIDS Patients

机译：针对HIV / AIDS患者选择第二轮联合抗逆转录病毒疗法的模糊离散事件系统方法

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We have recently pioneered the development of an innovative general-purpose decision-making and optimization technology, called fuzzy discrete event systems (FDES). In the previous papers, we reported results of applying FDES to selecting optimal first-round regimens for HIV/AIDS patients. In the present paper, we describe our further effort to apply the FDES framework to the second-round treatment, which is more challenging primarily due to drug resistance that occurs during the first-round treatment. We focused on five currently popular second-round regimens and 16 different treatment objectives. Two clinical AIDS experts on our team independently rated the five regimens as first-choice to fifth-choice regimen for each objective and their selections were used as golden standard. We used a genetic algorithm to optimize 20 parameters of our system named AIDS-FDES so that its regimen choices best matched those of the experts individually (i.e., through two different parameters sets). Our preliminary results showed that for the first-choice regimens, the exact agreements between AIDS-FDES and expert A and expert B were 87.5% and 100%, respectively, whereas the mean agreement rate for the five regimens was 77.5% and 80.1%, respectively. For all the five regimens, the agreement within one preference level (i.e., one physician''s second choice is another physician''s first or third choice), which was an overall agreement measure, for experts A and B was 92.5% and 96.3%, respectively. We also optimized and used just one parameter set to match AIDS-FDES to both the experts simultaneously. The agreement within one preference level for expert A was 90% and 86.3% for expert B. In order to adjust for any agreement likely to occur simply by chance, a weighted Cohen''s Kappa was used. The results for the expert''s combined selections relative to AIDS-FDES demonstrated that the specialists agreed with the treatment selection made by the computer system with a weighted Cohen''s Ka-ppa of 0.78 (95% confidence interval is [0.69, 0.87]), which indicates that the expert''s combined agreement with the system''s choices (beyond that expected by chance) was importantly improved over that of either expert''s agreement with each other
机译：我们最近率先开发了一种创新的通用决策和优化技术，称为模糊离散事件系统（FDES）。在以前的论文中，我们报道了将FDES用于选择HIV / AIDS患者最佳首轮治疗方案的结果。在本文中，我们描述了将FDES框架应用于第二轮治疗的进一步努力，这主要是由于在第一轮治疗期间发生的耐药性，因此更具挑战性。我们重点研究了五种当前流行的第二轮治疗方案和16种不同的治疗目标。我们团队的两名临床AIDS专家对每个目标分别将这5种治疗方案作为第一选择至第五选择治疗方案，并将他们的选择作为黄金标准。我们使用了一种遗传算法来优化名为AIDS-FDES的系统的20个参数，从而使其方案选择与个体专家的方案选择最匹配（即，通过两个不同的参数集）。我们的初步结果表明，对于首选方案，AIDS-FDES与专家A和专家B的确切一致率分别为87.5％和100％，而五个方案的平均一致率为77.5％和80.1％，分别。对于所有五种方案，在一个偏好级别内的一致性（即，一位医师的第二选择是另一位医师的第一或第三选择），这是一项总体协议衡量标准，对于专家A和B而言，该协议为92.5％，而分别为96.3％。我们还优化并仅使用了一个参数集来将AIDS-FDES同时与两个专家匹配。专家A在一个偏爱级别内的协议为90％，专家B在86.3％的级别内。为了调整可能偶然发生的任何协议，使用了加权的Cohen Kappa。专家针对AIDS-FDES进行的组合选择结果表明，专家同意计算机系统进行的治疗选择，加权Cohen Ka-ppa值为0.78（95％置信区间为[0.69， 0.87]），这表明与该专家的相互选择相比，该专家与系统选择的合并协议（超出了偶然的期望）得到了重要改进。

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