Background

Multi-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), before or during their stay.

Methods

Applied to a data set of 48,945 hospital stays (including known cases of carriage) with up to 16,325 attributes per stay, MOCA-I generated alert rules for risk of carriage or infection. A risk score was then computed from each stay according to the triggered rules.Recall and precision curves were plotted.

Results

The classification can be focused on specifically detecting high risk of having a positive test, or identifying large numbers of at-risk patients by modulating the risk score cut-off level. For a risk score above 0.85,recall (sensitivity) is 62 % with 69 % precision (confidence) for MDR bacteria, recall is 58 % with 88 % precision for MRSA. In addition, MOCA-I identifies 38 and 21 cases of previously unknown MDR and MRSA respectively.

Conclusions

MOCA-I generates medically pertinent alert rules. This classification algorithm can be used to detect patients with high risk of testing positive to MDR bacteria (including MRSA). Classification can be modulated by appropriately setting the risk score cut-off level to favor specific detection of small numbers of patients at very high risk or identification of large numbers of patients at risk. MOCA-I can thus contribute to more adapted treatments and preventive measures from admission, depending on the clinical setting or management strategy.

中文翻译：

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使用MOCA-I（一种用于医学数据的基于规则的“白盒”分类算法），可以检测出可能对多药耐药细菌呈阳性反应的住院患者。

背景

多重耐药性（MDR）细菌是主要的健康问题。在这项回顾性研究中，使用基于规则的分类算法MOCA-I（数据失衡的多目标分类算法）来识别住院的患者，这些患者的耐多药性（MDR）细菌（包括耐甲氧西林的葡萄球菌）呈阳性金黄色葡萄球菌（MRSA），在他们入住之前或期间。

方法

将MOCA-I应用于数据集48,945例住院（包括已知的运输案例），每次住院多达16,325个属性，生成了有关运输或感染风险的警报规则。然后根据触发的规则从每次入住计算出风险分数。绘制召回率和精度曲线。

结果

该分类的重点可以是通过检测风险评分的临界水平，专门检测出检测阳性的高风险，或识别大量有风险的患者。对于高于0.85的风险评分，MDR细菌的召回（敏感性）为62％，准确度（信心）为69％，MRSA的召回率为58％，准确度为88％。另外，MOCA-1分别识别出38例和21例先前未知的MDR和MRSA。

结论

MOCA-I生成与医学相关的警报规则。该分类算法可用于检测对MDR细菌（包括MRSA）呈阳性的高风险患者。可以通过适当设置风险评分截止水平来调整分类，以支持对极少数高风险患者的特异性检测或对大量高风险患者的识别。因此，根据临床环境或管理策略，MOCA-1可以有助于更适应的治疗和预防入院措施。