摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsRx编辑在比利时梅里贝克的新闻报道，研究表明：“文献中报道了大量的牛流产和围产期死亡的传染性和非传染性疾病(APM)。然而，由于资金限制或潜在的人畜共患影响，许多实验室只提供标准的分析小组，仅限于预先确定的病原体数量。”我们的新闻记者从根特大学的研究中获得了一句话：“为了提高实验室诊断的成本效益，根据与特定流产病原体流行相关的母体和环境特征，设计一种有针对性的APM病例分析方法可能是有益的。”这项回顾性观察研究的目的是实施机器学习管道(MLP)来预测与感染性APM相关的母体和环境因素。我们基于贪婪集成应用程序Roach的MLP结合了四个模型的标准调谐网格，应用于1590例APM病例的数据集，通过分析一组扩展的流产病原体获得了阳性诊断。贪婪集合成功地预测了奶牛的生产类型(奶牛/牛肉)、妊娠期和季节,模型预测能力在63%~73%之间。除了个体变量的预测准确性外，我们的MLP还分层识别了APM相关环境/母体特征的预测重要性原因。例如，在发生于肉牛的APM病例中，APM季节(春/夏)是最重要的预测因子，相对重要性为2.4%。此外，在妊娠晚期，Py ogenes和犬新孢子虫是APM最重要的预测因子，其相对重要性分别为22%和17%。有趣的是，在多胎母鼠中，羊群大小是APM最相关的预测因子，相对重要性为12%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Merelbeke, Belgium, by NewsRx editors, research stated, "A plethora of infectious and non-infectious c auses of bovine abortions and perinatal mortalities (APM) have been reported in literature. However, due to financial limitations or a potential zoonotic impact , many laboratories only offer a standard analytical panel, limited to a preesta blished number of pathogens." Our news journalists obtained a quote from the research from Ghent University, " To improve the cost-efficiency of laboratory diagnostics, it could be beneficial to design a targeted analytical approach for APM cases, based on maternal and e nvironmental characteristics associated with the prevalence of specific abortifa cient pathogens. The objective of this retrospective observational study was to implement a machine learning pipeline (MLP) to predict maternal and environmenta l factors associated with infectious APM. Our MLP based on a greedy ensemble app roach incorporated a standard tuning grid of four models, applied on a dataset o f 1590 APM cases with a positive diagnosis that was achieved by analyzing an ext ensive set of abortifacient pathogens. Production type (dairy/beef), gestation l ength, and season were successfully predicted by the greedy ensemble, with a mod est prediction capacity which ranged between 63 and 73 %. Besides t he predictive accuracy of individual variables, our MLP hierarchically identifie d predictor importance causes of associated environmental/maternal characteristi cs of APM. For instance, in APM cases that happened in beef cows, season at APM (spring/summer) was the most important predictor with a relative importance of 2 4 %. Furthermore, at the last trimester of gestation Trueperella py ogenes and Neospora caninum were the most important predictors of APM with a rel ative importance of 22 and 17 %, respectively. Interestingly, herd size came out as the most relevant predictor for APM in multiparous dams, with a relative importance of 12 %."
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