Department of Infection Sciences, Severn Pathology, Bristol, BS10 5NB, UK.
Division of Infection and Immunity, School of Medicine, Cardiff University, Henry Wellcome Building, Heath Park, Cardiff, CF14 4XN, UK.
BMC Med Inform Decis Mak. 2019 Aug 23;19(1):171. doi: 10.1186/s12911-019-0878-9.
A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible.
Screening process for urine samples prior to culture was modelled in a single clinical microbiology laboratory covering three hospitals and community services across Bristol and Bath, UK. Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods of classification: a heuristic model using a combination of white blood cell count and bacterial count, and a machine learning approach testing three algorithms (Random Forest, Neural Network, Extreme Gradient Boosting) whilst factoring in independent variables including demographics, historical urine culture results, and clinical details provided with the specimen.
A total of 212,554 urine reports were analysed. Initial findings demonstrated the potential for using machine learning algorithms, which outperformed the heuristic model in terms of relative workload reduction achieved at a classification sensitivity > 95%. Upon further analysis of classification sensitivity of subpopulations, we concluded that samples from pregnant patients and children (age 11 or younger) require independent evaluation. First the removal of pregnant patients and children from the classification process was investigated but this diminished the workload reduction achieved. The optimal solution was found to be three Extreme Gradient Boosting algorithms, trained independently for the classification of pregnant patients, children, and then all other patients. When combined, this system granted a relative workload reduction of 41% and a sensitivity of 95% for each of the stratified patient groups.
Based on the considerable time and cost savings achieved, without compromising the diagnostic performance, the heuristic model was successfully implemented in routine clinical practice in the diagnostic laboratory at Severn Pathology, Bristol. Our work shows the potential application of supervised machine learning models in improving service efficiency at a time when demand often surpasses resources of public healthcare providers.
诊断实验室中相当一部分微生物筛查是由于疑似尿路感染（UTI），但大约三分之二的尿液样本通常培养结果为阴性。通过减少需要培养的查询样本数量，并使诊断服务集中在真正存在微生物感染的样本上，可以显著提高服务效率。
在英国布里斯托尔和巴斯的三家医院和社区服务机构的单个临床微生物学实验室中对尿液样本培养前的筛选过程进行建模。使用一年中所有尿液显微镜检查、培养和药敏报告的回顾性分析，比较了两种分类方法：一种是使用白细胞计数和细菌计数组合的启发式模型，另一种是测试三种算法（随机森林、神经网络、极端梯度提升）的机器学习方法，同时考虑了包括人口统计学、历史尿液培养结果和标本提供的临床细节在内的独立变量。
共分析了 212554 份尿液报告。初步结果表明，使用机器学习算法具有潜力，在分类灵敏度>95%的情况下，这些算法在相对工作量减少方面优于启发式模型。在进一步分析亚群分类灵敏度后，我们得出结论，来自孕妇和儿童（11 岁或以下）的样本需要进行独立评估。首先，我们研究了将孕妇和儿童从分类过程中排除，但这降低了实现的工作量减少。发现最佳解决方案是三个独立训练的极端梯度提升算法，用于孕妇、儿童和其他所有患者的分类。当组合在一起时，该系统为每个分层患者组提供了 41%的相对工作量减少和 95%的灵敏度。
基于实现的可观时间和成本节省，同时不影响诊断性能，启发式模型已成功在布里斯托尔 Severn Pathology 诊断实验室的常规临床实践中实施。我们的工作表明，在公共医疗保健提供者的资源经常超过需求的情况下，监督机器学习模型有可能应用于提高服务效率。
